Mapping the structure of Borneo’s tropical forests across a … · 2017. 1. 28. · 1 1 Mapping...

1

Mapping the structure of Borneo’s tropical forests across a degradation gradient 1

2

Pfeifer M1*, Kor L1, Nilus R2, Turner E3, Cusack J4, Lysenko I1, Khoo M1, Chey VK2, Chung 3

AC2, Ewers RM1 4

5

1 Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst 6

Road, Ascot, Berkshire SL5 7PY, UK. [email protected]; 7

[email protected]; [email protected]; [email protected]; 8

[email protected] 9

2 Forest Research Centre, Sabah Forestry Department, PO Box 1407, 90715 Sandakan, 10

Sabah, Malaysia. [email protected] 11

3 Insect Ecology Group, Department of Zoology, University of Cambridge, Downing Street, 12

Cambridge, CB2 3EJ, UK. [email protected] 13

4 Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, 14

Oxford, OX1 3PS, UK. [email protected] 15

16

* Corresponding author 17

18

Keywords: Borneo, humid tropical forests, degradation, RapidEyeTM, co-occurrence 19

measures, spectral reflectance, biophysical structure, maps, field data20

2

Abstract 21

South East Asia has the highest rate of lowland forest loss of any tropical region, with 22

logging and deforestation for conversion to plantation agriculture being flagged as the most 23

urgent threats. Detecting and mapping logging impacts on forest structure is a primary 24

conservation concern, as these impacts feed through to changes in biodiversity and ecosystem 25

functions. Here, we test whether high-spatial resolution satellite remote sensing can be used 26

to map the responses of aboveground live tree biomass (AGB), canopy leaf area index (LAI) 27

and fractional vegetation cover (FCover) to selective logging and deforestation in Malaysian 28

Borneo. We measured these attributes in permanent vegetation plots in rainforest and oil 29

palm plantations across the degradation landscape of the Stability of Altered Forest 30

Ecosystems project. We found significant mathematical relationships between field-measured 31

structure and satellite-derived spectral and texture information, explaining up to 62 % of 32

variation in biophysical structure across forest and oil palm plots. These relationships held at 33

different aggregation levels from plots to forest disturbance types and oil palms allowing us 34

to map aboveground biomass and canopy structure across the degradation landscape. The 35

maps reveal considerable spatial variation in the impacts of previous logging, a pattern that 36

was less clear when considering field data alone. Up-scaled maps revealed a pronounced 37

decline in aboveground live tree biomass with increasing disturbance, impacts which are also 38

clearly visible in the field data even a decade after logging. Field data demonstrate a rapid 39

recovery in forest canopy structure with the canopy recovering to pre-disturbance levels a 40

decade after logging. Yet, up-scaled maps show that both LAI and FCover are still reduced in 41

logged compared to primary forest stands and markedly lower in oil palm stands. While 42

uncertainties remain, these maps can now be utilised to identify conservation win-wins, 43

especially when combining them with ongoing biodiversity surveys and measurements of 44

carbon sequestration, hydrological cycles and microclimate. 45

Deleted: ¶46

3

1. Introduction 47

Through the processes of selective logging and agricultural conversion, large areas of tropical 48

forests are being degraded and fragmented worldwide (Blaser, Sarre, Poore, & Johnson, 49

2011). South East Asia, in particular, has the highest rate of lowland forest loss of any 50

tropical region, with deforestation and logging for conversion to oil palm being flagged as the 51

most urgent threats (Sodhi, Koh, Brook, & Ng, 2004). Conventional logging, widely used in 52

South East Asia, and to a lesser extent ‘Reduced Impact Logging’ (Pinard & Putz, 1996) 53

reduces aboveground biomass (AGB) through the removal of large trees (Slik et al., 2013), 54

and causes residual damage that includes opening previously dense forest canopies (Pfeifer et 55

al., 2015). Resultant decreases in canopy leaf area index (LAI) and fractional vegetation 56

cover (FCover) affect microclimate (Didham & Lawton, 1999; Hardwick et al., 2015), carbon 57

sequestration through photosynthesis and regulation of surface water flows (Douglas, 1999). 58

These changes can affect biodiversity (Edwards et al., 2011; Wilcove, Giam, Edwards, 59

Fisher, & Koh, 2013) and ecosystem functions operating at different trophic levels (Ewers et 60

al., 2015). 61

Thus, unsurprisingly, detecting and mapping forest degradation is a primary conservation 62

concern, and remote sensing could provide an effective means to achieve this task (Rose et 63

al., 2014). Satellite or airborne sensor data have previously been employed to map 64

deforestation at coarse to fine spatial resolutions from landscape to global scales (Hansen et 65

al., 2013). Sensor-data based forest degradation and change maps combined with ground-66

surveys aid in quantifying degradation impacts on carbon stocks and emissions (Baccini et 67

al., 2012; Harris et al., 2012), as well as on biodiversity (Gibson et al., 2011; Strassburg et al., 68

2010; Wearn, Reuman, & Ewers, 2012). 69

Yet, mapping forest degradation using space-borne sensors is challenging in many 70

tropical humid landscapes, not least because of persistent cloud cover. Tree cover maps 71

4

(Hansen et al. 2013) or simpler maps of vegetation greenness (Pettorelli et al., 2005) do not 72

directly map forest biophysical structure or biomass and struggle to distinguish primary from 73

disturbed forests or agricultural land, including oil palm stands (Tropek et al., 2014). The 74

latter now cover large areas of South East Asian landscapes (Koh & Wilcove, 2008), and are 75

easily confused with forests during image classifications, especially when reaching maturity. 76

A number of methods can distinguish logged from primary forests (Asner et al., 2005). 77

But, these methods map categories and thus treat the habitat within each land-use as 78

homogenous. Yet, forest canopy structure and biomass can vary considerably within logged 79

and unlogged forests affecting ecosystem processes on a continuous scale. For instance, the 80

intensity of forest disturbance is a key determinant of the carbon (Berenguer et al., 2014; 81

Pfeifer et al., 2015) and biodiversity (Burivalova, Şekercioǧlu, & Koh, 2014; Gibson et al., 82

2011) that persists in logged forest. Carbon and biodiversity, in turn, are used to dictate 83

conservation and management priorities at the landscape scale under certification schemes 84

such as the High Carbon Stock and High Conservation Value standards applied to the palm 85

oil industry (Greenpeace International, 2013; RSPO, 2013). 86

Successfully linking sensor data to actual forest structure data is challenging. Multi-87

sensor approaches combining airborne LIDAR with satellite data are delivering promising 88

results in estimating canopy gap structure at landscape scales (Asner, 2009; Asner et al., 89

2010). But despite the decreasing costs of airborne LIDAR data (Asner, 2009), their 90

widespread use is still limited by the need for expensive expert knowledge for data 91

processing, large financial resources to fund planes and flights, and special flying permits that 92

are often hard to obtain in many tropical countries. Spaceborne Synthetic Aperture Radar 93

(SAR) such as ALOS PALSAR overcomes the challenges of persistent cloud cover and 94

atmospheric effects when mapping forest degradation (Woodhouse, Mitchard, Brolly, 95

Maniatis, & Ryan, 2012), with long-wavelength L-Band signals penetrating vegetation 96

5

canopies and relating to forest biophysical properties (Boyd & Danson, 2005; Lucas et al, 97

2014). However, costs of Radar imagery are high and availability limited (Joshi et al., 2015). 98

Backscatter starts to saturate at around 100 - 150 Mg ha-1 with errors being introduced largely 99

in ≥ 150 Mg ha-1 forest stands (Robinson, Saatchi, Neumann, & Gillespie, 2013; Mermoz et 100

al., 2015) typical for humid tropical forests with dense canopies. Also, backscattered energy 101

is not only affected by the size and orientation of the structural elements in the vegetation 102

canopy, but also by the moisture content of the vegetation and the underlying soil conditions 103

(Naidoo et al., 2015). 104

Unlike LIDAR and Radar, moderate to high-resolution satellite images can be obtained 105

for most regions in the world free of charge (e.g. Landsat 8 data via USGS Earth Explorer; 106

SPOT and RapidEyeTM data via European Space Agency). Spectral and texture information 107

derived from such images have been exploited by ecologists to map tree cover (Céline et al., 108

2013), green foliage density (Glenn, Huete, Nagler, & Nelson, 2008), and forest biomass and 109

canopy structure (Castillo-Santiago, Ricker, & de Jong, 2010; Pfeifer, Gonsamo, Disney, 110

Pellikka, & Marchant, 2012; Wang, Qi, & Cochrane, 2005). In addition, tools to process 111

these images are now easily accessible via statistical and spatial analyses software, including 112

freeware such as R Open Source Statistical software (R Development Core Team, 2013) and 113

QGIS Open Source Geographic Information System (QGIS Development Team, 2012), more 114

accessible to management staff in the tropics and elsewhere. 115

Here, we test whether significant relationships can be found between high spatial 116

resolution optical satellite sensor data and three attributes of forest structure (AGB, LAI, 117

FCover) measured in rainforest and oil palm plantation areas across a degradation landscape 118

in Malaysian Borneo. Satellite imagery was obtained at no-cost from the European Space 119

Agency under Category 1 User Agreement. Forest attributes were collected in permanent 120

vegetation plots (a combined survey area of > 12 ha) established across the Stability of 121

6

Altered Forest Ecosystems (SAFE) Project landscape (Ewers et al., 2011). In particular, we 122

ask: (1) what are the impacts of logging on forest structure more than one decade after 123

logging, and are these impacts spatially homogenous or heterogeneous; and (2) can we 124

reliably detect spatial variability in logging impacts, observed on the ground, using high 125

spatial resolution passive sensor data? Our aim was to identify reliable mathematical 126

relationships that would enable mapping of continuous surfaces of forest and oil palm 127

structure across entire landscapes. This would provide important baseline data for use in the 128

planning and management of future logging and agricultural activities in Borneo’s highly 129

threatened forests to minimise their environmental footprints. 130

131

2. Methods 132

133

2.1 The SAFE degradation landscape 134

The Stability of Altered Forest Ecosystem (SAFE) Project (4° 38’ N to 4° 46’ N, 116° 57’ to 135

117° 42’ E; Fig. 1), located within lowland dipterocarp forest regions of East Sabah in 136

Malaysian Borneo, features a gradient of forest disturbance from unlogged primary forest 137

through to severely degraded forest and oil palm plantations (Ewers et al., 2011). SAFE thus 138

reflects Sabah’s predominant land use change over the past decades, characterised by 139

industrial harvesting and premature re-logging of extensive tracts of once-logged forests and 140

large-scale forest-to-palm conversions (Reynolds, Payne, Sinun, Mosigil, & Walsh, 2011). 141

The region has a tropical climate with high rainfall (> 2000 mm / year) and varying terrain 142

topography, although all plots are below 800 m altitude. The geology comprises a mixture of 143

sedimentary rocks including siltstones, sandstones and others that are easily eroded (Douglas 144

et al. 1999). 145

7

Meteorological records from nearby Danum Valley Field Centre, show that the climate in 146

this part of Sabah is aseasonal, with occasional dry spells that are usually associated with El 147

Niño events (Walsh & Newbery, 1999). Recent data from Danum Valley (SEARRP: 148

http://www.searrp.org/danum-valley/the-conservation-area/climate) show that no dry months 149

occurred between September 2011 and January 2013, the period during which our data was 150

collected. This is important, as this could have potentially changed biophysical attributes 151

between time of field measurements and time of satellite data acquisition, as reported for 152

drought-affected areas in evergreen Amazonian forests (Myneni et al., 2007). 153

193 vegetation plots (25 m x 25 m) with North–South orientation were set out at SAFE in 154

2010 using a Garmin GPSMap60 device. Vegetation plots were established across the forest 155

degradation landscape according to a hierarchical sampling design as an objective procedure 156

to assess regional forest attributes (Ewers et al 2011). The design was chosen to ensure 157

unbiased decisions as to where to establish vegetation monitoring plots in the field (Ewers et 158

al 2011). Plots were located at roughly equal altitude and oriented to minimize potentially 159

confounding factors such as slope, latitude, longitude and distance to forest edges (prior to 160

controlled forest-to-oil palm conversion currently being carried out at SAFE) (Ewers et al 161

2011). Plots were distributed among 17 sampling blocks: three oil palm plantation blocks of 162

two different ages (OP1 and OP2 planted in 2006 and OP3 planted in 2000), two primary 163

forest stands (OG1 and OG2), lightly or illegally logged forests inside multi-use or protected 164

areas (OG3 and VJR), twice logged forests (LFE, LF1 - LF3) and salvage logged forest (A - 165

F). The latter is forest that has been logged with lifted restrictions on type of tree, size limits 166

and volume in advance of outright conversion to a new land use type such as agriculture (for 167

details on the hierarchical sampling design see Ewers et al., 2011). Twice and salvage logged 168

forests were selectively logged, once during the 1970s followed by a second logging round 169

from the late 1990s to the early 2000s removing medium hardwoods (Drybalanops and 170

Deleted: ¶171

Formatted: Font: 12 pt, Font color: Auto

http://www.searrp.org/danum-valley/the-conservation-area/climate

8

Dipterocarpus), and lighter hardwoods (Shorea and Parashorea). Historical forestry data 172

suggest that logging implemented under a modified uniform system removed an estimated 173

113 m3 ha-1 during the first rotation followed by an estimated 37 m3 ha-1 extraction during the 174

second rotation in lightly logged forests; the salvage logged forest were re-logged three times 175

with a cumulative extraction rate of 66 m3 ha-1 (Struebig et al., 2013), although our up-scaled 176

maps indicate even higher harvesting intensities. Overall, logging in the area resulted in 177

heavily degraded stands with a high density of roads and skid trails, a paucity of commercial 178

timber species, few emergent trees and the dominance of pioneer and invasive vegetation. At 179

SAFE, blocks A - F are currently being converted into a fragmented agricultural landscape. 180

181

2.2 Quantifying forest structure: AGB, LAI, FCover 182

Permanent vegetation structure plots were measured for above-ground biomass in 2010 and 183

2011 (N = 193). We measured canopy structure in 172 of these plots in 2012 and 2013. 184

Above-ground biomass was derived from measurements of tree size (height and diameter at 185

breast height, DBH) following RAINFOR protocols and including all trees with DBH ≥ 10 186

cm and with > 50 % of their roots located within the plot boundaries (for details see Pfeifer et 187

al., 2015). 188

We computed above-ground biomass using an improved pan-tropical algorithm (Chave et 189

al., 2014), as a recent study showed that it provided better biomass estimates than regional 190

algorithms for East Kalimantan (Rutishauser et al., 2013). We computed AGB using wood 191

specific gravity values reflecting different disturbance level of forest stands in Borneo (Slik et 192

al., 2008). For each tree, we computed AGB as above but using wood specific gravity 193

estimates drawn randomly (N = 100) from a normal distribution of wood specific gravity 194

values (for details see Pfeifer et al., 2015). This distribution was defined by maximum and 195

standard deviation and depended on the tree’s location: primary forests (0.64 ± 0.18) (King et 196

9

al 2006), and lightly logged forests (0.57 ± 0.02), twice logged forests (0.54 ± 0.03), and 197

salvage logged forests (0.41 ± 0.05) (Slik et al 2008). Oil palms have a fundamentally 198

different physical structure to forest trees, so we estimated AGB in oil palm plantations 199

separately (Pfeifer et al., 2015). We computed dry mass in Mg for each palm tree from its 200

height (in m) using sing (Thenkabail et al 2004). 201

Leaf area index (corrected for foliage clumping) and fractional vegetation cover were 202

estimated from 12 to 13 hemispherical photographs acquired in each plot following standard 203

sampling procedures (Pfeifer et al., 2014), using in house-algorithms (Pfeifer et al., 2014) and 204

the freeware CAN-EYE v 6.3.8 (Weiss & Baret, 2010). 205

206

2.3 Satellite data and derived spectral and texture data 207

We used five RapidEyeTM satellite images acquired over the SAFE landscape in 2012 and 208

January 2013 (Table A1 in Appendix). These cover the entire globe with a temporal 209

resolution of 5.5 days (at nadir) and are high-resolution, ortho-rectified products resampled to 210

5 m pixel resolution. The Multi-Spectral Imager on-board the satellites acquires data in five 211

spectral bands. The blue (0.44 - 0.51 µm), green (0.52 - 0.59 µm), red (0.63 - 0.68 µm) and 212

near-infrared (0.76 - 0.85 µm) bands are very similar to the corresponding Landsat spectral 213

bands, but the sensor has an additional red-edge band (0.69 - 0.73 µm), giving additional 214

sensitivity to changes in the reflectance of vegetation (Chander et al., 2013). 215

We pre-processed all images (calibration, cloud and cloud shadowing-masking) using 216

ENVI v 5.1 (Exelis Visual Information Solutions, Boulder, Colorado). We applied 217

atmospheric correction to these images using the 6s algorithm (Second Simulation of Satellite 218

Signal in the Solar Spectrum) (Vermote, Tanre, Deuze, Herman, & Morcette, 1997) 219

implemented with the Open Source Geographic Resources Analysis Support System 220

(GRASS) i.atcorr function (Neteler, Bowman, Landa, & Metz, 2012) implemented in 221

10

Quantum GIS v 2.6.1 (Hugentobler, 2008). We computed standard spectral vegetation 222

indices, including Normal Difference Vegetation Index (NDVI) (Tucker, 1979), a two-band 223

Enhanced Vegetation Index (EVI2) (Jiang, Huete, Didan, & Miura, 2008), and the Modified 224

Soil-Adjusted Vegetation Index (MSAVI2) (Qi, Chehbouni, Huete, Kerr, & Sorooshian, 225

1994), as well as texture indices using the ‘raster’ (Hijmans & van Etten, 2010) and ‘glcm’ 226

(Zvoleff, 2014) libraries in R statistical software (R Development Core Team, 2013). To 227

obtain texture indices, we computed a grey-level co-occurrence matrix (GLCM) with a 90 228

degree shift and 64 grey-levels and varied the window size for analyses from 3 x 3 to 9 x 9 229

pixels. This is important as the size of the analysis window used affects textural features, with 230

the most appropriate option depending on image resolution and landscape characteristics (Lu 231

2005). We computed the mean ( ), variance 232

( ), contrast ( ) and 233

dissimilarity ( ) as measures of image texture for each of the 234

five bands, where P is the symmetrical GLCM with dimension N x N and i and j are the ith 235

and jth element of P (Gallardo-Cruz et al. 2012). Pij therefore represents the probability of 236

finding the value of the reference pixel i in combination with the neighbouring pixel j. 237

We extracted spectral intensity and texture data for each band as well as spectral 238

vegetation indices to the point coordinates of each plot as mean values within a buffer radius 239

of 20 m. During this step, we excluded 58 plots that were covered by cloud and cloud 240

shadows. We used these values as predictors in subsequent modelling and removed highly 241

inter-correlated indices from global models. In line with other studies (Wang, Qi, & 242

Cochrane, 2005), MSAVI2 outperformed NDVI and EVI2 (Pearson's product moment 243

correlation computed for measures of vegetation greenness: r ≥ 0.9; see also Figure A1 in 244

Appendix) in its ability to separate primary from lightly-logged forest stands and was 245

subsequently used in all analyses. Dissimilarity estimates computed from band intensities 246

11

within a window of 9 x 9 pixels outperformed other texture indices in single predictor 247

regression models (see Tables A2 and A3 in Appendix), and we subsequently used this 248

resolution in all analyses. We excluded intensity and dissimilarity values for the blue band in 249

modelling and upscaling forest structure. Visual inspections revealed obvious striping and 250

banding in the blue band of some images that almost certainly derive from image calibration, 251

which is known to be problematic in large amount of cloud-filled transition scenes (Godard, 252

Stoll, Anderson, Schulze, & D’Souza, 2012). 253

254

2.4 Data analysis 255

Plot data (forest attributes and spectral as well as texture indices) were aggregated to the level 256

of the 17 sampling blocks and five disturbance levels defined as oil palm (OP1-OP3, three 257

blocks), salvage logged (A-F, six blocks), twice-logged (LFE & LF1-LF3, four blocks), 258

lightly logged (OG3 & VJR, two blocks) and primary forest (OG1& OG2, two blocks). We 259

used one-way ANOVA with post-hoc Tukey HSD tests for pairwise comparisons aimed at 260

detecting general differences among the three metrics of forest structure and among earth 261

observation signals with respect to disturbance level. We checked for homogeneity of 262

variance within groups using Bartlett's K-squared test statistic, and computed the Welsh 263

correction to compare pairwise group means in case of variance non-homogeneity using the 264

R ‘stats’ package (R Development Core Team, 2013). 265

We explored single predictor relationships between earth observation data and forest 266

structure data across the three levels of aggregation (plots, blocks, disturbance levels), using 267

nonlinear least squares and general linear models as implemented in the R ‘stats’ package (R 268

Development Core Team, 2013). 269

We used beta-logistic regression with the betareg package in R (Grün, Kosmidis, & 270

Zeileis, 2012; Simas, Barreto-Souza, & Rocha, 2010) to develop multiple predictor models. 271

12

We scaled model predictions between lower and upper bounds, which were set to reflect 272

assumptions about expected forest structure per plot (each 0.0625 ha); FCover: 0 - 100, LAI: 273

0 - 10, AGB: 0 - 65 (maximum value observed at site was 60.8 Mg / Plot). We fitted beta-274

logistic regression models for plot level data using Maximum Likelihood and automated 275

model selection via information theoretic approaches and multi-model averaging. First, we 276

constructed a global model each for LAI, FCover and AGB, including predictors from the set 277

of predictors described above, after first excluding highly correlated predictors (see Table 1 278

for details on global models). For each global model, we used the dredge function in the R 279

MuMIn package v1.10.5 (Barton 2014), which constructs models using all possible 280

combinations of the explanatory variables supplied in each global model. These models were 281

ranked, relative to the best model, based on the change in the Akaike Information Criterion 282

(delta AIC). A multi-model average (final model) was calculated across all models with delta 283

AIC < 4. 284

285

2.5 Upscaling structure across the landscape 286

We added the grey-level dissimilarity layers as well as the MSAVI2 layer to each 287

RapidEyeTM scene and subsequently mosaicked those rasters. We aggregated all rasters to 25 288

m pixel resolution maps, to match the scale of vegetation structure plots using the R raster 289

package (Hijmans & van Etten, 2010). We then used the model-averaged final model to 290

predict scaled mean and fitted quantiles of the response distribution with lower (at 0.25) and 291

upper quantiles (at 0.75) estimates of AGB, FCover and LAI for each pixel in the SAFE 292

landscape. We unscaled model estimates of forest structure traits and saved the output as new 293

rasters. 294

We created minimum convex polygons with a 200 m buffer enclosing all plots within 295

each block. We extracted mean AGB, LAI and FCover for each pixel encompassed by each 296

13

polygon and summarised overall patterns for variation in the forest structure attributes across 297

the five disturbance levels using density curves and histogram statistics. 298

299

3. Results 300

301

3.1 Variability of forest structure across the forest disturbance gradient 302

Oil palms differed structurally from primary and degraded forest, featuring significantly 303

lower biomass and canopy cover (Fig. 2). Structural differences were less consistent between 304

primary and disturbed forests. Above-ground biomass decreased significantly from primary 305

(Mean: 413.4 t / ha) to lightly logged (267.3 t / ha) to twice-logged (110.6 t / ha) and salvage 306

logged forest (42.4 t / ha). LAI and FCover were significantly higher in twice-logged versus 307

salvage logged forests (P < 0.05, Welsh corrected). We found no further significant 308

differences between forest types with regard to forest canopy structure (Fig. 2). 309

310

3.2 Spectral and texture attributes across the forest disturbance gradient 311

Oil palm blocks differed significantly from forests and displayed lower vegetation greenness 312

(except oil palm versus twice-logged forest), higher red intensity and dissimilarity and lower 313

near-infrared dissimilarity (except oil palm versus salvage logged forest) (Fig. 3). The red 314

intensity was lower in primary and lightly logged forest compared to salvage logged and 315

twice-logged forest, indicating higher photosynthetic capacity in less-disturbed stands (P < 316

0.05, Welsh corrected). Patterns were similar for the red-edge intensity but less pronounced 317

and not significant for oil palm versus salvage logged, and primary and lightly logged versus 318

twice logged forest. The near-infrared intensity was significantly higher in salvage logged 319

forest and oil palm compared to lightly and twice-logged forest (all P < 0.01, except oil palm 320

versus lightly logged: P = 0.07). Vegetation greenness was higher in primary compared to 321

14

twice-logged and salvage logged forest (P < 0.001, Welsh corrected) and lower in twice-322

logged versus salvage logged forest (P < 0.005). The red dissimilarity was higher in primary 323

and twice-logged forest compared to salvage logged forest (P < 0.001, Welsh corrected). In 324

addition, primary forest had significantly lower near-infrared band dissimilarity compared to 325

twice-logged forest (P < 0.05) but significantly higher near-infrared band dissimilarity 326

compared to salvage logged forest (Welsh corrected, P < 0.001). The near-infrared 327

dissimilarity was lower in salvage logged forest compared to twice-logged (P < 0.0001) and 328

lightly logged forest (Welsh corrected, P < 0.001). 329

330

3.3 Modelling relationships between forest attributes and remotely sensed measures 331

Clear relationships emerged between earth observation data and forest structure attributes 332

across aggregation levels (Fig. 4). At stand level, leaf area index and fractional vegetation 333

cover showed strong relationships with red intensity in single-predictor linear models (LAI = 334

8.556 - 1.059 * RED, RSS = 0.59, Deviance = 0.65, coefficient and intercept significant at P 335

< 0.0001; FCover = 152.09 - 17.59 * RED, RSS = 9.475, Deviance = 0.66, coefficient and 336

intercept significant at P < 0.0001). Aboveground biomass increased exponentially with 337

increasing vegetation greenness (AGB = 6.729e-17 * exp(51.18*MSAVI2), RSS = 4.845, 338

Deviance = 0.79, coefficient significant at P < 0.001). At plot level, relationships are weaker 339

but still significant (FCover = 124.918 -11.685 * RED, RSS = 16.13, Deviance = 0.24, 340

coefficient and intercept significant at P < 0.0001; LAI = 7.18 - 0.76 * RED, RSS = 0.8466, 341

Deviance = 0.33, coefficient and intercept significant at P < 0.0001; AGB = 4.701e-8 * 342

exp(24.52 *MSAVI2), RSS = 9.009, Deviance = 0.13, coefficient significant at P < 0.0001). 343

Including additional predictors improved model performance at the plot level (Table 2). 344

The green and red-edge intensities as well as vegetation greenness were most important 345

predictors of AGB (Table 2). Grey-level dissimilarities of red and red-edge (in the case of 346 Deleted: and near-infrared 347

15

LAI) and of red edge (for FCover) improved performance of models predicting forest canopy 348

attributes. Observed values showed reasonable agreement with predicted values for all three 349

forest attributes at the level of forest types, although variability at plot level is high (Figure 350

A2 in Appendix). Our models underestimate AGB in primary and lightly logged forests, and 351

overestimate canopy structure attributes in oil palms (Figure A2). It is important to note, 352

however, that the models describing the relationships between forest canopy structure and 353

sensor data were strongly influenced by the distinction between oil palm and forest habitats, 354

but that variation among forest plots was less strongly differentiated. 355

356

3.4 Up-scaled estimates of logging damage 357

Maps of forest structure attributes show that all forest types differ significantly from each 358

other in their structure (Fig. 5). On average, AGB (t / ha), LAI and FCover decreased 359

sequentially from primary forest to lightly logged, twice-logged and salvage logged forests 360

(Table 3). Field data as well as map-derived AGB estimates clearly showed the decline in 361

forest biomass with increasing disturbance (see also Pfeifer et al., 2015). Our maps indicate 362

average biomass extraction levels of 187 t / ha for lightly logged forest, 228 t / ha for twice-363

logged forest, and 255 t / ha for salvage logged forest. Yet, whilst field data suggest rapid 364

canopy recovery following disturbance, data extracted for buffered minimum convex 365

polygons from up-scaled maps reveal a more nuanced process. LAI was still reduced by 15 % 366

for salvage and by 7 % for lightly logged forest compared to primary forest. And forest types 367

differed significantly from each other (P < 0.001); FCover was still reduced by 12.4 % for 368

salvage logged forest and by 7.8 % for lightly logged forest and differed significantly 369

between forest types except between twice and lightly logged forests. 370

Biomass varied less in oil palm plantations compared to forests (Variance test: P < 0.001) 371

and varied more in primary forests compared to disturbed forest stands (P < 0.001); AGB 372

Deleted: FCover373

Deleted: LAI374

16

heterogeneity was lower in twice logged forests compared to little and salvage logged stands 375

(P < 0.001). Conversely, canopy LAI varied significantly more in oil palm plantations 376

compared to forests and in salvage logged forests compared to twice and lightly logged 377

forests (P < 0.001). However, canopy LAI varied significantly less in salvage logged stands 378

compared to primary forest stands. FCover varied significantly more in oil palms compared 379

to forests, in salvage logged compared to twice logged forests, in twice logged compared to 380

lightly logged forests; FCover varied more in primary compared to lightly logged forests but 381

less compared to other forest types (P < 0.001). 382

383

4. Discussion 384

Forest degradation via processes like selective logging, is a process that leads to a 385

deterioration in the density or structure of forest cover (Lambin, 1999) causing significant 386

loss of aboveground biomass (Slik et al., 2013) and reduced photosynthesis through the 387

removal of large trees and canopies (Figueira et al., 2008). Degradation impacts are 388

accompanied by structural changes at stand level, producing a heterogeneous forest landscape 389

comprised of intact forest, tree-fall gaps, remains of logging infrastructure with compacted 390

soils, and collateral damage on non-target trees (Laporte et al., 2007). Structural changes are 391

temporary, as productivity of smaller trees and saplings increases with light availability 392

allowing them to refill canopy gaps. Recovery time to pre-disturbance conditions of 393

biophysical structure, however, will vary with location and disturbance intensity (Frolking et 394

al., 2009; Martin et al., 2015). 395

Using our extensive network of field measurements, we show that canopy cover appears 396

to recover more rapidly than aboveground biomass in Borneo’s degraded forests. Canopy 397

structure and biomass are considerably lower in oil palm stands, though, indicating their 398

reduced functions in terms of carbon sequestration (Pfeifer et al., 2015) and microclimate 399

17

regulation (Hardwick et al., 2015). We found significant mathematical relationships between 400

ground measures of forest structure and high-spatial resolution satellite sensor data. These 401

relationships held at different aggregation levels, highlighting the potential to use these 402

relationships to map aboveground biomass, vegetation cover and canopy leaf area across the 403

SAFE degradation landscape (Fig. 6 and Figure A3 in Appendix). Such maps revealed 404

considerable spatial variability in the impacts of previous logging and forest-to-palm 405

conversion across the landscape. This pattern was less clear when considering field data 406

alone, despite the large survey area covered. 407

408

4.1 Spectral signatures across the degradation gradient 409

Higher absorption in the red wavelengths by canopies of undisturbed and lightly logged 410

forests indicates higher photosynthetic capacity compared to photosynthesis in forests that 411

were heavily logged more than ten years ago (see Fig. 3). Red absorption appears more 412

heterogeneous in primary forest versus lightly logged ones at stand level, possibly because 413

natural mortality of large trees that still exist in the undisturbed creates large canopy gaps, 414

visible in the high variability of canopy LAI. These gaps with intense regeneration are 415

surrounded by intact forests. This interpretation is supported by heterogeneous but high near-416

infrared reflectance measured above undisturbed forests. High near-infrared reflectance may 417

be caused by grass growth or strong regrowth of pioneer tree species, which are both patterns 418

we would expect in heavily disturbed stands or in recent larger canopy gaps (Riou & Seyler, 419

1997). Salvage logged forests are strongly degraded comprising regrowth patches covered in 420

less productive vines intersected by an extensive road network; they feature slow tree 421

regeneration and significantly reduced aboveground biomass. Thus, unsurprisingly, salvage 422

logged forests exhibit high but homogeneous near-infrared reflectance and low but 423

homogeneous red absorption. 424

Deleted: 2425

18

Twice logged stands differed clearly from undisturbed forest and dense forest regrowth 426

but also from salvage logged stands. They exhibit high near-infrared reflectance and low red 427

absorption, but both with variable texture patterns, which is indicative of rainforests with 428

signs of man-made disturbance (Riou & Seyler, 1997). These stands are irregular mosaics of 429

intact forest, logging infrastructure and clear-cuts that are currently regenerating. Oil palm 430

shows less red wavelength absorption and considerably higher dissimilarity in doing so, 431

probably because they vary in age and canopy LAI and hence photosynthetic capacity. 432

Furthermore, oil palm canopies are intersected by a dense network of roads used for 433

maintenance and harvest. Unsurprisingly, their near-infrared reflectance is high but very 434

homogeneous. Overall, spectral data density curves show considerable overlap across forest 435

disturbance levels, especially between forests that experienced moderate to high logging 436

pressures, and between heavily logged forests and mature oil palm plantations. 437

438

4.2 Modelling structure - sensor data relationships 439

Statistically fitting observed values of biophysical attributes, such as leaf area index, to 440

corresponding spectral vegetation indices is a common upscaling procedure. Spectral 441

vegetation indices exploit the differential between near infrared and red reflectance and the 442

ratio of these two spectral bands. This should render them more sensitive to the vegetation 443

component, while minimizing external effects caused by solar irradiance, atmospheric 444

conditions, and canopy background (Qi et al., 1995). Vegetation indices have consequently 445

been linked to productivity and canopy physical traits (Pfeifer et al., 2012; Sims et al., 2006; 446

Sjöström et al., 2011). Here, we show that vegetation greenness explains only 13 % of 447

biomass variability at plot level and heavily underestimates biomass in denser forests. Hence, 448

assuming vegetation greenness to be a direct surrogate for on-the-ground forest structure, 449

19

productivity or biodiversity (Pettorelli et al., 2005) is too simplistic, and is unlikely to 450

produce reliable outcomes for degradation landscapes in the humid tropics. 451

We demonstrate that spectral intensity data themselves can be used to estimate 452

biophysical structure, in particular for canopy attributes. By including texture information 453

into our mathematical models, we could significantly improve the models’ predictive 454

capacity, especially for canopy structure at stand level. Texture and spectral data combined 455

can be used to separate oil palm from moderately disturbed to undisturbed forests. The plots 456

measured at SAFE encompass the gradient of biomass found across the wider landscape, 457

setting this study apart from other tree plot studies (Marvin et al., 2014). Using tree census 458

data from these plots allowed us to map biomass, and thus carbon, across the landscape. 459

However, despite using data from more than 12 ha of surveyed area, we found that more 460

than 40% of the variation in plot attributes remain unexplained. These uncertainties in the 461

upscaling algorithm need to be accounted for when using the resulting maps for research and 462

conservation planning at landscape scales. Sources of observed uncertainties may include 463

inaccuracies in field measurements (Jonckheere, Muys, & Coppin, 2005; Phillips, Brown, 464

Schroeder, & Birdsey, 2000) or noise introduced through satellite image acquisition and 465

correction procedures (Dungan, 2003). Canopy reflectance at stand level is affected by many 466

factors operating at different spatial scales, i.e. transmittance of leaves, amount and 467

arrangement of leaves in the canopy, and characteristics of other canopy components. Large 468

time-spans between date of measurement in the field and date of image acquisition can 469

introduce errors, especially in seasonal environments. However, climate at SAFE landscape is 470

aseasonal and no dry months occurred between September 2011 and January 2013, the period 471

during which our data was collected. Canopy structure was measured in the field in the same 472

year (and for some plots within a month of image acquisition). Biomass was measured 473

approximately one year prior to image acquisition, which may contribute to errors in 474

20

upscaling algorithms as Borneo’s forests typically show high wood growth rates (9.73 Mg 475

dry mass per ha, Banin et al., 2014). However, these high growth rates are caused primarily 476

through tall trees of the Dipterocarpaceae family (Banin et al., 2014), which had been 477

targeted by selective logging rounds in disturbed forests. Low spectral resolution of the 478

images may limit our ability to distinguish between forest degradation stages at higher 479

disturbance levels, especially when analysing texture. 480

Spectral reflectance data are good indicators of rates (i.e. photosynthesis), but less 481

reliable indicators of vegetation state (e.g. leaf area index), due to non-linearity in 482

relationships (Sellers, 1987). In our study, we use empirical approaches to estimate 483

biophysical attributes from remote sensing data and to subsequently use the derived statistical 484

relationships to generate continuous surfaces of these attributes across the SAFE landscape. 485

A major drawback of this computationally efficient approach is that it is a site and sensor-486

specific one (Pfeifer et al., 2012). Physically based approaches using radiative transfer theory 487

model photon transport in vegetation canopies and can be adapted for various conditions 488

(Myneni et al., 2002). They are more powerful for understanding the mechanisms behind 489

observed variations in the spectral reflectance at the scale of leaves and individual trees, but 490

they are difficult to implement, scale- and vegetation type dependent and there is no universal 491

canopy radiative transfer model suitable for all canopies (Yin et al., 2015). 492

Complex patterns of topography and disturbance underlie spatial variation in above-493

ground carbon at landscape scale (Gale 2000, Pfeifer et al. 2015). Environmental factors 494

other than disturbance measured at SAFE probably affect above-ground live tree biomass, as 495

terrain steepness may affect both forest accessibility for logging but also forest growth. 496

Including micro-topographic drivers into models to map AGB variation across a forest 497

degradation landscape will be the subject of future studies. 498

499

Formatted: Font: 12 pt, Font color: Auto

21

4.3 Degradation impacts on forest structure at landscape scale 500

Up-scaled maps revealed a pronounced decline in aboveground live tree biomass with 501

increasing disturbance across the SAFE degradation landscape. These impacts are clearly 502

visible in both field data and maps, even a decade after logging. Harvesting rates computed 503

from these maps are higher than previously suggested in the literature (Struebig et al., 2013). 504

However, these were computed assuming that pre-logging biomass was similar to biomass in 505

nearby undisturbed forests, which may not necessarily be the case. In future analyses, 506

harvesting rates could be computed from time-series of biomass maps to avoid errors 507

introduced during such a space-for-time substitution approach. 508

Field data demonstrate a rapid recovery in forest canopy structure with the canopy 509

recovering to pre-disturbance levels a decade after logging. Yet, up-scaled maps, which cover 510

a larger survey area, suggest that both LAI and FCover are still reduced in logged compared 511

to primary forest stands (for details see Table 3 and Figure 5). Reduced LAI is linked to 512

higher daily maximum temperatures and lower daily humidity within tropical forests and at 513

this study site (Hardwick et al., 2015), and this in turn is likely to affect invertebrates and 514

invertebrate-mediated ecosystem functions (Ewers et al., 2015). We therefore suggest that 515

logging impacts on ecosystem processes and biodiversity are likely to be present more than a 516

decade after logging. Overall, our analyses suggest that both canopy structure and biomass 517

respond negatively to increased forest degradation. 518

Degradation impacts on forest structure appeared spatially heterogeneous, but not 519

necessarily in the way we would expect. For example, biomass was low in oil palms, and 520

quite consistently so. In contrast, AGB in primary forests varied strongly in space, despite 521

being higher on average when compared to disturbed forest stands. Canopy structure was 522

most variable in oil palms as we would expect given age differences of our oil palm stands, 523

but showed no trends with disturbance in forests, even though forest stands were increasingly 524

22

heterogeneous in their FCover response to disturbance. Natural tree falls causing localised 525

canopy damage may be one of a number of reasons explaining the heterogeneity observed in 526

the biophysical structure of primary forests. 527

528

4.4 Mapping High Carbon Stocks (HCS) in degraded landscapes 529

Current estimates suggest that at least one third of the world’s tropical humid forests are 530

affected by logging. Oil palm (Elais guineensis) is now grown across more than 13.5 million 531

ha of tropical humid lowland, often replacing high-biodiversity and high-biomass forest 532

(Fitzherbert et al., 2008). Malaysia and Indonesia, in particular, are major palm oil producers, 533

where demand for suitable agricultural land conflict with conservation initiatives (Koh & 534

Wilcove, 2007; Sodhi et al., 2004). In recent years, major conservation NGOs have been 535

working with corporate actors in palm oil and pulp & paper industries, developing a High 536

Carbon Stock approach to Forest Conservation Policies (Greenpeace International, 2013). 537

This approach aims to identify degraded lands suitable for development, cultivation and 538

expansion of oil palm plantations and to spatially delineate areas that should be conserved. 539

Our maps are sufficiently detailed to enable such decision-making and to identify (relatively) 540

high carbon stock areas within the wider study landscape. However, the practical value of 541

these maps will be dependent on the user’s ability to assess biomass variability in relative 542

rather than absolute terms. High biomass, little disturbed forests are clearly separated from 543

low biomass, heavily degraded forests and pockets of high-biomass forest are clearly visible 544

within the study landscape. Setting appropriate cut-off points for separating high from 545

moderate biomass forests is likely to be more challenging, given that the uncertainty in our 546

models and maps increases in high biomass zones. 547

548

5. Conclusions 549

23

Tropical forest landscapes are under increasing pressure worldwide as a result of 550

anthropogenic disturbance. Mapping forest degradation at near-real time and at the landscape 551

level can provide a valuable means of informing conservation actions or pre-emptive 552

management to limit losses. Our maps (Figure A3 in Appendix) depict the impacts of past 553

logging events on the canopy structure and biomass of tropical humid forests. These maps 554

can now be utilised to identify conservation win-wins (Strassburg et al., 2010), for example 555

by combining them with ongoing biodiversity surveys to delineate zones of high biodiversity 556

across forest types, and with measurements of carbon sequestration, hydrological cycles and 557

microclimate. These maps can also be manipulated to incorporate trade-offs between land use 558

demands and to take into account recent developments in fragmentation research. This 559

information ultimately feeds into High Carbon Stocks and High Conservation Value 560

approaches to forest and biodiversity conservation (Senior, Brown, Villalpando, & Hill, 561

2014). 562

563

Acknowledgement 564

We thank the Sime Darby Foundation, the Sabah Foundation, Benta Wawasan and the Sabah 565

Forestry Department for their generous support of the SAFE Project. We thank the Royal 566

Society’s South East Asia Rainforest Research Programme for logistical support in the field, 567

and Yayasan Sabah, Maliau Basin Management Committee, the State Secretary, Sabah Chief 568

Minister’s Departments, the Malaysian Economic Planning Unit and the Sabah Biodiversity 569

Council for permission to conduct research in Sabah. Data in the field were largely collected 570

with the help and support of the SAFE Project’s team of Research Assistants. MP and RME 571

were supported by European Research Council Project number 281986. We thank the 572

European Space Agency for providing RapidEyeTM under Category 1 - User Agreement to 573

MP (C1P 13735). 574

Deleted: 2575

24

576

References 577

Asner, G. P. (2009). Tropical forest carbon assessment: integrating satellite and airborne 578

mapping approaches. Environmental Research Letters, 4, doi:10.1088/1748-579

9326/4/3/034009. 580

Asner, G. P., Knapp, D. E., Broadbent, E. N., Oliveira, P. J. C., Keller, M., & Silva, J. N. 581

(2005). Selective Logging in the Brazilian Amazon. Science, 310, 480-482. 582

Asner, G. P., Powell, G. V. N., Mascaro, J., Knapp, D. E., Clark, J. K., et al. (2010). High-583

resolution forest carbon stocks and emissions in the Amazon. Proceedings of the 584

National Academy of Sciences of the United States of America, 107, 16738-16742. 585

Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., et al. (2012). Estimated 586

carbon dioxide emissions from tropical deforestation improved by carbon-density maps. 587

Nature Climate Change, 2, 182-185. 588

Banin, L., Lewis, S. L., Lopez-Gonzalez, G., et al. (2014). Tropical forest wood production: 589

A cross-continental comparison. Journal of Ecology, 102, 1025-1037. 590

Barton, K. (2014). MuMIn: Multi-model inference. R package version 1.10.0. From 591

http://cran.r-project.org/package=MuMIn 592

Berenguer, E., Ferreira, J., Gardner, T. A., Aragão, L. E. O. C., De Camargo, P. B., et al. 593

(2014). A large-scale field assessment of carbon stocks in human-modified tropical 594

forests. Global Change Biology, 20, 3713-3726. 595

Blaser, J., Sarre, A., Poore, D., & Johnson, S. (2011). Status of Tropical Forest Management 596

2011. ITTO Technical Series. doi:10.1017/S0032247400051135 597

Boyd, D. S. S., & Danson, F. M. M. (2005). Satellite remote sensing of forest resources: three 598

decades of research development. Progress in Physical Geography, 29, 1-26. 599

25

Burivalova, Z., Şekercioǧlu, Ç. H., & Koh, L. P. (2014). Thresholds of Logging Intensity to 600

Maintain Tropical Forest Biodiversity. Current Biology, 24, 1893-1898. 601

Castillo-Santiago, M. A., Ricker, M., & de Jong, B. H. J. (2010). Estimation of tropical forest 602

structure from SPOT-5 satellite images. International Journal of Remote Sensing, 31, 603

2767-2782. 604

Céline, E., Philippe, M., Astrid, V., Catherine, B., Musampa, C., & Pierre, D. (2013). 605

National forest cover change in Congo Basin: deforestation, reforestation, degradation 606

and regeneration for the years 1990, 2000 and 2005. Global Change Biology, 19, 1173-607

87. 608

Chander, G., Haque, M. O., Sampath, A., Brunn, A., Trosset, G., et al. (2013). Radiometric 609

and geometric assessment of data from the RapidEye constellation of satellites. 610

International Journal of Remote Sensing, 34, 5905-5925. 611

Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., et al. (2014). 612

Improved allometric models to estimate the aboveground biomass of tropical trees. 613

Global Change Biology, 20, 3177-3190. 614

Didham, R. K., & Lawton, J. H. (1999). Edge Structure Determines the Magnitude of 615

Changes in Microclimate and Vegetation Structure in Tropical Forest Fragments1. 616

Biotropica, 31, 17-30. 617

Douglas, I. (1999). Hydrological investigations of forest disturbance and land cover impacts 618

in South-East Asia: a review. Philosophical Transactions of the Royal Society of London 619

B: Biological Sciences, 354, 1725-38. 620

Dungan, J. (2003). Spatial uncertainty in ecology: Implications for remote sensing and GIS 621

applications. International Journal of Geographical Information Science, 17, 93–94. 622

26

Edwards, D. P., Larsen, T. H., Docherty, T. D. S., Ansell, F. A., Hsu, Wet al. (2011). 623

Degraded lands worth protecting: the biological importance of Southeast Asia’s 624

repeatedly logged forests. Proceedings of the Royal Society B, 278, 82-90. 625

Ewers, R. M., Didham, R. K., Fahrig, L., Ferraz, G., Hector, A., et al. (2011). A large-scale 626

forest fragmentation experiment: the Stability of Altered Forest Ecosystems Project. 627

Philosophical Transactions of the Royal Society B: Biological Sciences, 366, 3292-628

3302. 629

Ewers, R. M., Boyle, M. J. W., Gleave, R., Plowman, N. S., Benedick, S., et al. (2015). 630

Logging cuts the functional dominance of social insects in tropical rainforest. Nature 631

Communications, 6, doi:10.1038/ncomms7836 632

Figueira, A. M. E. S., Miller, S. D., de Sousa, C. A. D., Menton, M. C., Maia, A. R., da 633

Rocha, H. R., & Goulden, M. L. (2008). Effects of selective logging on tropical forest 634

tree growth. Journal of Geophysical Research, 113, 1-11. 635

Fitzherbert, E. B., Struebig, M. J., Morel, A., Danielsen, F., Brühl, C. A., Donald, P. F., & 636

Phalan, B. (2008). How will oil palm expansion affect biodiversity? Trends in Ecology 637

& Evolution, 23, 538-545. 638

Frolking, S., Palace, M. W., Clark, D. B., Chambers, J. Q., Shugart, H. H., & Hurtt, G. C. 639

(2009). Forest disturbance and recovery: A general review in the context of spaceborne 640

remote sensing of impacts on aboveground biomass and canopy structure. Journal of 641

Geophysical Research: Biogeosciences, 114, doi:10.1029/2008JG000911 642

Gibson, L., Lee, T. M., Koh, L. P., Brook, B. W., Gardner, T. A., et al. (2011). Primary 643

forests are irreplaceable for sustaining tropical biodiversity. Nature, 478, 378-381. 644

Glenn, E. P., Huete, A. R., Nagler, P. L., & Nelson, S. G. (2008). Relationship Between 645

Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological 646

27

Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. 647

Sensors, 8, 2136-2160. 648

Godard, E., Stoll, E., Anderson, C., Schulze, R., & D’Souza, B. (2012). Integrating Advanced 649

Calibration Techniques into Routine Spacecraft Operations. From 650

http://www.spaceops2012.org/proceedings/documents/id1275279-paper-001.pdf. The 651

12th International Conference on Space Operations. Last accessed 19/03/2015. 652

Greenpeace International. (2013). Identifying High Carbon Stock (HCS) Forest for 653

Protection. 654

http://www.greenpeace.org/international/Global/international/briefings/forests/2013/HC655

H-Briefing-2013.pdf. Last accessed 19/03/2015. 656

Grün, B., Kosmidis, I., & Zeileis, A. (2012). Extended Beta Regression in R: Shaken, Stirred, 657

Mixed, and Partitioned. Journal of Statistical Software, 48, 1-25. 658

Hansen, M. C., Potapov, P. V, Moore, R., Hancher, M., Turubanova, S., et al. (2013). High-659

resolution global maps of 21st-century forest cover change. Science, 342, 850-3. 660

Hardwick, S. R., Toumi, R., Pfeifer, M., Turner, E. C., Nilus, R., & Ewers, R. M. (2015). The 661

relationship between leaf area index and microclimate in tropical forest and oil palm 662

plantation: Forest disturbance drives changes in microclimate. Agricultural and Forest 663

Meteorology, 201, 187-195. 664

Harris, N. L., Brown, S., Hagen, S. C., Saatchi, S. S., Petrova, S., et al. (2012). Baseline Map 665

of Carbon Emissions from Deforestation in Tropical Regions. Science, 336, 1573-1576. 666

Hijmans, R. J., & van Etten, J. (2010). raster: Geographic analysis and modeling with raster 667

data. R Package Version. 668

Hugentobler, M. (2008). Quantum GIS. In Encyclopedia of GIS. 669

Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced 670

vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845. 671

28

Jonckheere, I. G., Muys, B., & Coppin, P. R. (2005). Derivative analysis for in situ high 672

dynamic range hemispherical photography and its application in forest stands. 673

Geoscience and Remote Sensing Letters, IEEE, 2, 296-300. 674

Joshi, N., Mitchard, E. T., Woo, N., Torres, J., Moll-Rocek, J., et al. (2015). Mapping 675

dynamics of deforestation and forest degradation in tropical forests using radar satellite 676

data. Environmental Research Letters, 10, doi:10.1088/1748-9326/10/3/034014 677

Koh, L. P., & Wilcove, D. S. (2007). Cashing in palm oil for conservation. Nature, 448, 993-678

994. 679

Koh, L. P., & Wilcove, D. S. (2008). Is oil palm agriculture really destroying tropical 680

biodiversity? Conservation Letters, 1, 60-64. 681

Lambin, E. F. (1999). Monitoring forest degradation in tropical regions by remote sensing: 682

some methodological issues. Global Ecology & Biogeography, 8, 191-198. 683

Laporte, N. T., Stabach, J. A., Grosch, R., Lin, T. S., & Goetz, S. J. (2007). Expansion of 684

industrial logging in Central Africa. Science, 316, 1451 685

Lucas, R. M., Clewley, D., Accad, A., Butler, D., Armston, J., et al. (2014). Mapping forest 686

growth and degradation stage in the Brigalow Belt Bioregion of Australia through 687

integration of ALOS PALSAR and Landsat-derived foliage projective cover data. 688

Remote Sensing of Environment, 155, 42-57 689

Marvin, D. C., Asner, G. P., Knapp, D. E., Anderson, C. B., Martin, R. E., et al. (2014). 690

Amazonian landscapes and the bias in field studies of forest structure and biomass. 691

Proceedings of the National Academy of Sciences of the United States of America, 111, 692

5224-5232. 693

Mermoz, S., Réjou-Méchain, M., Villard, L., Le Toan, T., Rossi, V., & Gourlet-Fleury, S. 694

(2015). Decrease of L-band SAR backscatter with biomass of dense forests. Remote 695

Sensing of Environment, 159, 307-317. 696

29

Myneni, R. B., Yang, W., Nemani, R. R., Huete, A. R., Dickinson, R. E., et al. (2007). Large 697

seasonal swings in leaf area of Amazon rainforests. Proceedings of the National 698

Academy of Sciences of the United States of America, 104, 4820-4823. 699

Naidoo, L., Mathieu, R., Main, R., Kleynhans, W., Wessels, K., Asner, G., & Leblon, B. 700

(2015). Savannah woody structure modelling and mapping using multi-frequency (X-, 701

C- and L-band) Synthetic Aperture Radar data. ISPRS Journal of Photogrammetry and 702

Remote Sensing, 105, 234-250. 703

Neteler, M., Bowman, M. H., Landa, M., & Metz, M. (2012). GRASS GIS: A multi-purpose 704

open source GIS. Environmental Modelling and Software, 31, 124-130. 705

Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. 706

(2005). Using the satellite-derived NDVI to assess ecological responses to 707

environmental change. Trends in Ecology & Evolution, 20, 503-10. 708

Pfeifer, M., Gonsamo, A., Disney, M., Pellikka, P., & Marchant, R. (2012). Leaf area index 709

for biomes of the Eastern Arc Mountains: Landsat and SPOT observations along 710

precipitation and altitude gradients. Remote Sensing of Environment, 118, 103-115. 711

Pfeifer, M., Lefebvre, V., Gonsamo, A., Pellikka, P., Marchant, R., Denu, D., & Platts, P. 712

(2014). Validating and Linking the GIMMS Leaf Area Index (LAI3g) with 713

Environmental Controls in Tropical Africa. Remote Sensing, 6, 1973-1990. 714

Pfeifer, M., Lefebvre, V., Turner, E., Cusack, J., Khoo, M.S. (2015). Deadwood biomass: an 715

underestimated carbon stock in degraded tropical forests? Environmental Research 716

Letters, 10, 10.1088. 717

Phillips, D. L., Brown, S. L., Schroeder, P. E., & Birdsey, R. A. (2000). Toward error 718

analysis of large-scale forest carbon budgets. Global Ecology and Biogeography, 9, 305-719

313. 720

30

Pinard, M. A., & Putz, F. E. (1996). Retaining Forest Biomass by Reducing Logging 721

Damage. Biotropica, 28, 278-295. 722

QGIS Development Team. (2012). QGIS Geographic Information System. Open Source 723

Geospatial Foundation Project. http://qgis.osgeo.org. Qgisorg. Retrieved from 724

http://www.qgis.org/ 725

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil 726

adjusted vegetation index. Remote Sensing of Environment, 48, 119–126. 727

R Development Core Team. (2013). R Development Core Team. R: A Language and 728

Environment for Statistical Computing. 729

Reynolds, G., Payne, J., Sinun, W., Mosigil, G., & Walsh, R. P. D. (2011). Changes in forest 730

land use and management in Sabah, Malaysian Borneo, 1990-2010, with a focus on the 731

Danum Valley region. Philosophical Transactions of the Royal Society of London B: 732

Biological Sciences, 366, 3168-76. 733

Riou, R., & Seyler, F. (1997). Texture analysis of tropical rain forest infrared satellite images. 734

Photogrammetric Engeneering and Remote Sensing, 63, 515-521. 735

Robinson, C., Saatchi, S., Neumann, M., & Gillespie, T. (2013). Impacts of spatial variability 736

on aboveground biomass estimation from l-band radar in a temperate forest. Remote 737

Sensing, 5, 1001-1023 738

Rose, R. A., Byler, D., Eastman, J. R., Fleishman, E., Geller, G., et al. (2014). Ten Ways 739

Remote Sensing Can Contribute to Conservation. Conservation Biology, 740

doi:10.1111/cobi.12397 741

RSPO. (2013). Adoption of Principles and Criteria for the Production of Sustainable Palm 742

Oil (p. 70). From http://www.rspo.org/file/revisedPandC2013.pdf. Last accessed 743

19/03/2015. 744

31

DeFries, R., Asner G., Achard, F., Justtice, C., Laporte, N., et al. (2005). Monitoring 745

Tropical Deforestation for Emerging Carbon Markets. In: Tropical Deforestation and 746

Climate Change ( Moutinho, S. & Schwartzman S.). Amazon Institute for 747

Environmental Research. 748

http://publications.jrc.ec.europa.eu/repository/handle/JRC31742. Last accessed 749

19/03/2015. 750

Rutishauser, E., Noor’an, F., Laumonier, Y., Halperin, J., Hergoualc’h, K., & Verchot, L. 751

(2013). Generic allometric models including height best estimate forest biomass and 752

carbon stocks in Indonesia. Forest Ecology and Management, 307, 219-225. 753

Senior, M. J. M., Brown, E., Villalpando, P., & Hill, J. K. (2014). Increasing the Scientific 754

Evidence Base in the “High Conservation Value” (HCV) Approach for Biodiversity 755

Conservation in Managed Tropical Landscapes. Conservation Letters, Early View 756

online. doi:10.1111/conl.12148 757

Simas, A. B., Barreto-Souza, W., & Rocha, A. V. (2010). Improved estimators for a general 758

class of beta regression models. Computational Statistics and Data Analysis, 54, 348-759

366. doi:10.1016/j.csda.2009.08.017 760

Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., et al. (2006). 761

On the use of MODIS EVI to assess gross primary productivity of North American 762

ecosystems. Journal of Geophysical Research: Biogeosciences, 111. 763

doi:10.1029/2006JG000162 764

Sjöström, M., Ardö, J., Arneth, A., Boulain, N., Cappelaere, B., et al. (2011). Exploring the 765

potential of MODIS EVI for modeling gross primary production across African 766

ecosystems. Remote Sensing of Environment, 115, 1081–1089. 767

32

Slik, J. W. F., Bernard, C. S., Breman, F. C., VAN Beek, M., Salim, A., & Sheil, D. (2008). 768

Wood density as a conservation tool: quantification of disturbance and identification of 769

conservation-priority areas in tropical forests. Conservation Biology, 22, 1299-308. 770

Slik, J. W. F., Paoli, G., Mcguire, K., Amaral, I., Barroso, J., et al. (2013). Large trees drive 771

forest aboveground biomass variation in moist lowland forests across the tropics. Global 772

Ecology and Biogeography, 22, 1261-1271. 773

Sodhi, N. S., Koh, L. P., Brook, B. W., & Ng, P. K. L. (2004). Southeast Asian biodiversity: 774

An impending disaster. Trends in Ecology and Evolution, 19, 654-660. 775

Strassburg, B. B., Kelly, A., Balmford, A., Davies, R. G., Gibbs, H. K., et al. (2010). Global 776

congruence of carbon storage and biodiversity in terrestrial ecosystems. Conservation 777

Letters, 3, 98-105. 778

Struebig, M. J., Turner, A., Giles, E., Lasmana, F., Tollington, S., et al. (2013). Quantifying 779

the biodiversity value of repeatedly logged rainforests: gradient and comparative 780

approaches from Borneo. Advances in Ecological Research, 48, 183-224. 781

Tropek, R., Sedláček, O., Beck, J., Keil, P., Musilová, Z., et al. (2014). Comment on “High-782

resolution global maps of 21st-century forest cover change”. Science, 344, 981. 783

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring 784

vegetation. Remote Sensing of Environment, 8, 127-150. 785

Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., & Morcette, J. J. (1997). Second 786

Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview. IEEE 787

Transactions on Geoscience and Remote Sensing, 35, 675-686. 788

Walsh, R. P., & Newbery, D. M. (1999). The ecoclimatology of Danum, Sabah, in the 789

context of the world’s rainforest regions, with particular reference to dry periods and 790

their impact. Philosophical Transactions of the Royal Society of London. Series B, 791

Biological Sciences, 354, 1869-83. 792

33

Wang, C., Qi, J., & Cochrane, M. (2005). Assessment of tropical forest degradation with 793

canopy fractional cover from Landsat ETM+ and IKONOS imagery. Earth Interactions, 794

9, 1-18. 795

Wearn, O. R., Reuman, D. C., & Ewers, R. M. (2012). Extinction Debt and Windows of 796

Conservation Opportunity in the Brazilian Amazon. Science, 337, 228-232. 797

Weiss, M., & Baret, F. (2010). CAN-EYE V6. 1 USER MANUAL. From 798

http://www6.paca.inra.fr/can-eye/Download. Last accessed 19/03/2015. 799

Wilcove, D. S., Giam, X., Edwards, D. P., Fisher, B., & Koh, L. P. (2013). Navjot’s 800

nightmare revisited: logging, agriculture, and biodiversity in Southeast Asia. Trends in 801

Ecology & Evolution, 28, 531-40. 802

Woodhouse, I. H., Mitchard, E. T. a, Brolly, M., Maniatis, D., & Ryan, C. M. (2012). Radar 803

backscatter is not a “direct measure” of forest biomass. Nature Climate Change, 2, 556-804

557. 805

Yin, G., Li, J., Liu, Q., Fan, W., Xu, B., Zeng, Y., & Zhao, J. (2015). Regional Leaf Area 806

Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model 807

Selection. Remote Sensing, 7, 4604-4625 808

Zvoleff, A. (2014). Calculate textures from grey-level co-occurrence matrices (GLCMs) in R. 809

Retrieved from http://cran.r-project.org/web/packages/glcm/index.html 810

811

812 Formatted: Space After: 8 pt

34

Table 1 Global beta-logistic models relating forest structure attributes to RapidEyeTM 813

satellite sensor data at 135 plots. Models include intensity data for sensor bands 2 (GREEN), 814

3 (RED), 4 (RED_EDGE) and 5 (NEAR_IR), as well as their respective grey-level 815

dissimilarities (DissB2, DissB3, DissB4, DissB5). Structure attributes include (aboveground 816

biomass, AGB, leaf area index, LAI, and fractional vegetation cover, FCover) MSAVI2, 817

which is computed from red and near-infrared intensities, replaced both predictors in the 818

global model for AGB. The predicted estimates were scaled between 1e-6 and 65 (AGB), 1e-819

6 and 100 (FCover) and 1e-6 and 10 (LAI) to construct the models. See Table 2 for model-820

averaging results. DEV - Model fit computed as model-explained deviance. AIC - Akaike 821

Information Criterion. 822

Predicted Global Model DEV AIC

AGB

scaled

exp(MSAVI2) + GREEN + RED_EDGE + DissB2 + DissB3

+ DissB4 + DissB5

0.62 -457.9

LAI

scaled

DissB3 + DissB4 + DissB5 + RED + RED_EDGE +

NEAR_IR

0.52 -293.8

FCover

scaled

DissB3 + DissB4 + DissB5 + RED + RED_EDGE +

NEAR_IR

0.38 -143.9

823

Deleted: ¶824

Deleted: green825

Deleted: red826

Deleted: red-edge827

Deleted: near-infrared828

35

Table 2 Results of model-averaging carried out on three global beta-logistic regression 829

models relating forest structure attributes to satellite derived spectral and texture information 830

(see Table 1 for global models). The mathematical relationships between forest structure and 831

satellite sensor data were used to upscale forest structure across the SAFE landscape. Relative 832

variable importance (RVI) was computed during multi-model averaging. RVI = 1 if the 833

predictor was present in all models. Coefficient estimates in bold: RVI = 1. DEV - Model fit 834

computed as model-explained deviance. N - Number of models computed during model-835

averaging. For further explanation of variables used see Table 1. 836

Predicted Model coefficients (with shrinkage) Phi DEV N

AGB

scaled

19.45a - 5.01*exp(MSAVI2a) - 2.39*GREENa +

1.08*RED_EDGEa + 2.65*DissB2a - 0.28*DissB3 -

0.13*DissB5 + 0.09*DissB4

12.15a 0.61 8

LAI

scaled

0.90c - 0.59*REDa + 0.41*RED_EDGEa -

0.11*NEAR_IRa - 0.53*DissB3d+ 1.08*DissB4a -

0.36*DissB5a

37.43a 0.52 2

FCover

scaled

2.66b - 0.66*REDb + 0.30*RED_EDGEd -

0.08*NEAR_IRc + 1.48*DissB4a - 0.42*DissB5c -

0.17*DissB3

8.93a 0.38 7

a P < 0.001, b P < 0.01, c P < 0.05, d P < 0.01 837

838

839

840

841

Deleted: ¶842

Deleted: green843


Deleted: red845



Deleted: red848



Formatted: Space After: 8 pt

36

Table 3 Summary statistics for forest structure variation across disturbance levels using maps 851

derived from upscaling forest structure. Aboveground biomass (AGB): the distribution of 852

estimates was least variable in oil palms (F test, P < 0.001); AGB was more variable in 853

primary and lightly logged forest compared to twice-logged and salvage logged forest, but 854

less variable in twice compared to salvage logged forest (all P < 0.001). Leaf area index 855

(LAI): the distribution of estimates was most variable in oil palm; LAI was more variable in 856

primary forest compared to other forests, but less variable in lightly versus twice-logged 857

forest, and less variable in twice versus salvage logged forest (all P < 0.001). Fractional 858

vegetation cover (FCover): the distribution of estimates was most variable in oil palm; 859

FCover was less variable in lightly logged versus other forest types, less variable in primary 860

compared to twice-logged forest and less variable in twice-logged versus salvage logged 861

forest (all P < 0.001). 862

863

AGB (t/ha) LAI (m2/m2) FCover (%)

Mean Median SD Mean Median SD Mean Median SD

Oil Palm 38.1 33.2 25.2 2.47 2.48 0.7 56.23 57.85 10.5

Salvage L 95.4 75.4 72.8 3.78 3.84 0.5 70.96 71.13 6.9

Twice L 122.4 112.6 52.6 3.82 3.81 0.4 74.32 75.04 4.8

Lightly L 163.2 123.2 102.7 4.12 4.10 0.3 74.71 74.10 3.5

Primary 350.4 337.5 111.9 4.43 4.46 0.5 81.03 81.43 4.1

864

865

866

867

Deleted: ¶868

Formatted: Superscript


37

Fig. 1 Location of vegetation plots within the SAFE landscape. The average altitude of plots 869

is 439 m above sea level [range: 238 - 618 m] based on ASTER global digital elevation 870

model 2 (ASTER GDEM is a product of METI and NASA). Selected close-ups of the 871

sampling design in forest stands and oil palm plantations are overlaid on False Colour 872

Composites of two RapidEyeTM images. 873

874

Fig. 2 Forest attributes aggregated at the level of forest types. (A) Above-ground biomass in 875

live tree (AGB) decreased significantly from unlogged, protected forest stands (OG) to 876

severely degraded stands (Salvage logged) and oil palm plantations (OP). (B) and (C) 877

Canopy structure (Leaf Area Index, LAI; Fraction of vegetation cover, FCover) does not 878

differ significantly between forest types, although it does differ markedly between forest 879

stands and OP. For details on pairwise significant differences see text. 880

881

Fig. 3 Spectral and textural image data derived from RapidEyeTM imagery for plots and 882

aggregated to the level of forest types. Oil palm blocks differed significantly from forests in 883

all satellite derived information. (A) Red intensity was lower in primary and lightly logged 884

forest compared to salvage logged and twice-logged forest (P < 0.05). (B) Vegetation 885

greenness expressed as MSAVI2 was higher in primary compared to twice-logged and 886

salvage logged forest (P < 0.001) and lower in twice-logged versus salvage logged forest (P 887

< 0.005). (C) Red dissimilarity was higher in primary and twice-logged forest compared to 888

salvage logged forest (P < 0.001). Primary forest had significantly lower near-infrared band 889

dissimilarity compared to twice-logged forest (P < 0.05) but significantly higher near-infrared 890

band dissimilarity compared to salvage logged forest (P < 0.001). (D) Near-infrared 891

dissimilarity was lower in salvage logged forest compared to twice-logged (P < 0.0001) and 892

lightly logged forest (P < 0.001). 893

Deleted: ¶894

38

895

Fig. 4 Single-predictor relationships between forest attributes and spectral data derived from 896

RapidEyeTM imagery at plot level and aggregated to stand level. At stand level, aboveground 897

biomass increased exponentially with increasing vegetation greenness. Leaf area index and 898

fractional vegetation cover decreased linearly with increasing red intensity. At plot level, 899

relationships are weaker but still significant. For details on models and models fits see text. 900

901

Fig. 5 Density curves computed from histograms of structure attributes and extracted for each 902

disturbance level from the newly generated maps. For details on the extraction see text. All 903

density curves differed significantly from each other (two-sample Kolmogorov-Smirnov test; 904

P < 0.001). All forest types differ significantly from each other in their structure. On average, 905

AGB (t / ha), LAI and FCover decreased sequentially from primary forest to lightly logged, 906

twice-logged and salvage logged forests (see also Table 3). 907

908

Fig. 6 Derived maps of forest structure attributes (above-ground biomass in live tree, AGB, 909

leaf area index, LAI, and fractional vegetation cover, FCover) at the SAFE degradation 910

landscape. Details on models used to generate these maps are provided in Table 2. 911

39

912

913

Figure 1 Location of vegetation plots within the SAFE landscape. 914

40

915

916

Figure 2 Forest attributes aggregated at the level of forest types.917

41

918

919

Figure 3 Spectral and textural image data derived from RapidEyeTM imagery for plots and 920

aggregated to the level of forest types. Red intensity is given in µW/(cm2 * sr * nm). 921

922

923


42

924

925

Figure 4 Single-predictor relationships between forest attributes and spectral data derived 926

from RapidEyeTM imagery at plot level and aggregated to stand level.927

43

928

929

930

Figure 5 Density curves computed from histograms of structure attributes and extracted for 931

each disturbance level from the newly generated maps.932

44

933

934

Figure 6 Derived maps of forest structure attributes (above-ground biomass in live tree, 935

AGB, leaf area index, LAI, and fractional vegetation cover, FCover) at the SAFE degradation 936

landscape. 937

938 Formatted: Left, Space After: 8 pt, Line spacing: Multiple 1.08 li

45

Appendix 939

940

Table A1 Details on RapidEyeTM imagery used in this study. There were 193 vegetation plots 941

(25 m x 25 m) established at SAFE in 2010 and distributed among 17 sampling blocks (table 942

1): three oil palm plantation blocks (OP1 and OP2 planted in 2006 and OP3 planted in 2000), 943

two primary forest blocks (OG1 and OG2), two lightly or illegally logged forest blocks (OG3 944

and VJR), four twice-logged forest blocks (LFE, LF1 - LF3) and six salvage-logged forest 945

blocks that are currently being converted into a fragmented agricultural landscape (A - F) 946

(Ewers et al 2011). We thank the European Space Agency for providing RapidEyeTM under 947

Category 1 - User Agreement to Marion Pfeifer (C1P 13735). Res - Resolution, IA - 948

Incidence angle, SE - Illumination elevation angle, SVA - Spacecraft viewing angle. 949

950

Tile Date Blocks (forests and

oil palms) covered

Res IA SE SVA

5041214 21/09/2012 OG1 to OG 3 5 m 18.56 80.84 16.79

5041215 21/09/2012 - 5 m 16.37 81.04 16.79

5041216 13/05/2012 - 5 m 16.78 75.08 -13.16

5041217 18/08/2012 F, VJR, LFE, B, C,

D, E, OP1, OP2, OP3

5 m 7.20 78.77 -6.29

5041218 24/01/2013 A, LF1 to LF3 5 m 6.53 63.36 6.78

951

952

Deleted: ¶953

46

Table A2 Pearson’s product moment correlation coefficients (Spearman’s rho) between 954

predictor variables (texture indices: mean, variance contrast, dissimilarity; spectral data) 955

computed for bands 2 to 5 (B2 to B5) and extracted for the field derived point data. ns - 956

correlation not significant at P = 0.05. Showing results for window size 9 pixels x 9 pixels 957

only. B2 - GREEN, B3 - RED, B4 - RED_EDGE, B5 - NEAR_IR. 958

959

Mean Variance Contrast Dissimilarity

B2 B3 B4 B5 B2 B3 B4 B5 B2 B3 B4 B5 B2 B3 B4 B5

Mean

B2 - .88 .89 .75 .34 .26 .39 .41 .39 .25 .23 .24 .46 .28 .25 .28

B3 - - .54 .31 - .56 .44 .20 - .53 .24 .10 - .57 .22 .13

B4 - - - .92 - - .36 .56 - - .29 .42 - - .33 .45

B5 - - - - - - - .55 - - - .38 - - - .40

Vari

an

ce

B2 - - - - - .96 .79 .16 .62 .57 .34 .09 .56 .55 .26 .08

B3 - - - - - - .68 .02 - .64 .25 -.02 - .64 .16 -

.03

B4 - - - - - - - .62 - - .65 .51 - - .62 .49

B5 - - - - - - - - - - - .86 - - - .85

Con

trast

B2 - - - - - - - - - .85 .58 .14 .94 .71 .48 .14

B3 - - - - - - - - - - .41 -.1 - .91 .30 -

.06

B4 - - - - - - - - - - - .74 - - .96 .69

B5 - - - - - - - - - - - - - - - .98

Dis

sim

ilari

ty

B2 - - - - - - - - - - - - - .61 .48 .20

B3 - - - - - - - - - - - - - - .22 -

.09

B4 - - - - - - - - - - - - - - - .74

B5 - - - - - - - - - - - - - - - -

Sp

ectr

al

B2 .45 - - - .37 - - - .50 - - - .41 - - -

B3 .32 .70 - - .45 .56 - - .56 .73 - - .44 .74 - -

B4 .47 .49 .31 - .19 .23 -.03 - .30 .37 -.11 - .25 .41 -.16 -

B5 .25 .04 .28 .41 -.00 .00 -.14 -.19 -.01 .04 -.29 -.27 -.00 .10 -.32 -

.27

960

961

Deleted: 1962

Deleted: 1963

Formatted: Left, Space After: 8 pt

47

Table A3 Models fits (R2adj ) of linear single - predictor models relating texture indices to 964

biophysical attributes at plot level. ns - correlation not significant at P = 0.05. AGB - Live 965

tree aboveground biomass, Fcov - Fractional vegetation cover. LAI - Leaf area index. B2 - 966

GREEN, B3 - RED, B4 - RED_EDGE, B5 - NEAR_IR. 967

968

Window 3 x 3 Window 5 x 5 Window 7 x 7 Window 9 x 9

AGB LAI FCov AGB LAI FCov AGB LAI FCov AGB LAI FCov

Mean

B2 .16 ns ns .17 ns ns .18 ns ns .19 ns ns

B3 ns .15 .10 .02 .16 .11 .02 .17 .12 .03 .17 .13


B5 .42 .04 .03 .43 .04 .03 .44 .04 .03 .43 .03 .03

Vari

an

ce

B2 ns .06 .-3 ns .09 .05 ns .10 .06 ns .11 .07

B3 ns .17 .07 ns .19 .09 ns .19 .11 ns .19 .11


B5 .16 .07 .04 .21 .06 .03 .22 .05 ns .19 .03 ns

Con

trast

B2 ns .02 ns ns .03 ns ns .05 ns ns .07 ns

B3 ns .08 ns ns .10 .02 ns .13 .04 .14 .18 .06

B4 ns ns ns ns ns ns ns ns ns ns ns ns


Dis

sim

ilari

ty

B2 ns ns ns ns ns ns ns .03 ns ns .04 ns

B3 .03 .19 .09 .04 .21 .10 .05 .25 .13 .06 .28 .15

B4 .04 ns .02 .05 ns .03 .05 ns .02 .04 ns ns


969

970

971

972

Deleted: ¶973

48

974

975 Figure A1 Spectral vegetation indices derived from RapidEyeTM imagery for plots and 976

aggregated to the level of forest types. We calculated the Normalized Difference Vegetation 977

Index as NDVI = (Near_IR - RED)/(Near_IR + RED), the Enhanced Vegetation Index as 978

EVI = 2.5 * (NEAR_IR - RED)/( NEAR_IR + 2.4 * RED + 1), the Modified Soil-Adjusted 979

Vegetation Index as MSAVI2 = ((2 * NEAR_IR + 1) - (( 2 * NEAR_IR + 1 )^{2} - 8 * 980

(NEAR_IR - RED))^{1/2}) / 2, and the Red Edge Index as REI = ((NEAR_IR - RED)/( 981

NEAR_IR + RED)) +1 Spectral vegetation indices were highly correlated with each other 982

across plots as indicated by Pearson’s product-moment correlation, r (NDVI ~ MSAVI2: r = 983

0.996, NDVI ~ EVI2: r = 0.994, EVI2 ~ MSAVI2: r = 0.985, MSAVI2 ~ REI: r = 0.882, 984

NDVI ~ REI: r = 0.849, EVI2 ~ REI: r = 0.872). 985

986

987

Deleted: ¶988

Deleted: ¶989

Deleted: were 990

Deleted: ¶991 ¶992

Formatted: Indent: Left: 0 cm, First line: 0 cm

49

993

Figure A2 Comparison of predicted forest attributes (using our final models) against forest 994

attributes observed in the field at the level of plots (grey dots) and at the level of forest types 995

(mean ±standard deviation). 996

997

Formatted: Space After: 0 pt, Line spacing: Double

Formatted: Font: Not Bold

50

998

Mapping the structure of Borneo’s tropical forests across a … · 2017. 1. 28. · 1 1 Mapping...

Documents

Transcript of Mapping the structure of Borneo’s tropical forests across a … · 2017. 1. 28. · 1 1 Mapping...