Estimating wood growth across the Hubbard Brook valley: an analysis linking imaging spectroscopy and...
1
_ _ W S7 W S9 W S8 W S3 W S4 W S5 W S2 W S6 W S1 W S101 Plots Stream s Experim entalw atersheds Elevation H igh :1003 m Elevation Low :222 m 0 1000 2000 3000 4000 5000 m Ü M t.C ushm an (977 m ) M t.K ineo (1015 m ) MirrorLake (213 m ) Estimating wood growth across the Hubbard Brook valley: an analysis linking imaging spectroscopy and ecosystem model PnET Zaixing Zhou, Scott Ollinger, Mary Martin, Lucie Lepine Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space University of New Hampshire, Durham, NH, 03824 USA Introducti on Methods Results and Discussion Summary and Future Work References Ecosystem model PnET-II was used to estimate the spatial wood growth across the Hubbard Brook valley (Fig 2-a). The valley was delineated into grid cells in a 30-m resolution. For each cell, PnET-II was run for either of deciduous stands or evergreen stands, or both for the mixed. The mountain microclimate model MTCLIM was applied to estimate the climate variables (maximum and minimum temperature, precipitation, and PAR) across the valley in conjunction with a DEM (Fig 2-a) and the climate from the HBEF headquarter (HQ) weather station. Temperature estimates were based on the base (HQ) temperatures and a temperature lapse rate. Separate lapse rates were derived for daily maximum and minimum temperature(Fig 2-b). Precipitation estimates at the site were based on the daily record of precipitation from the base, and a specified ratio of annual total precipitation between the site and the base(Fig 2-c). Radiation estimates were based on the observation that the diurnal temperature range (from minimum temperature to maximum temperature) is closely related to the daily average atmospheric transmittance. Predicted radiation was in good agreement with observations at HQ (Fig 2-d). The complementary nature of remote sensing and ecosystem modeling has been widely demonstrated to bring benefits in studies of terrestrial carbon cycling. Ecosystem models focus on the mechanical biogeochemical processes that regulate carbon and nutrient cycling. Remote sensing can now provide regular, high – resolution spatially continuous information of land surface characteristics relevant to modeling, such as canopy nitrogen (Martin et al., 2008). Integration of remotely sensed data sets with ecosystem process models and distributed climate data provides a means for regional assessment of carbon fluxes and analysis of the underlying processes affecting them (Ollinger & Smith, 2005). In this study, we examined spatial patterns of forest productivity across a topographically and floristically diverse 3160-ha valley at the Hubbard Brook Experimental Forest (HBEF), in central New Hampshire, USA. Our approach involved integration of a forest process model (PnET-II, Aber and others 1995) with spatially-explicit canopy nitrogen estimates, derived from high-resolution imaging spectroscopy. A mountain microclimate model MTCLIM (Thornton & Running, 1999) was also used to estimate the spatial climate variables across the valley. Data for wood growth collected from a network of measurement plots were used to validate model predictions and to help interpret the spatial pattern of predicted growth. Aber, J.D., Ollinger, S.V., Federer, C.A., Reich, P.B., Goulden, M.L., Kicklighter, D.W., Melillo, J.M., Lathrop, R.G., 1995. Predicting the effects of climate change on water yield and forest production in the northeastern United States. Climate Research 5, 207–222. Martin, M.E., Plourde, L.C., Ollinger, S.V., Smith, M.-L., McNeil, B.E., 2008. A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment 112, 3511–3519. Ollinger, S.V., Smith, M.-L., 2005. Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data. Ecosystems 8, 760–778. Thornton, P.E., Running, S.W., 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology 93, 211–228. Figure 3. Foliar N and relative proportions of deciduous and evergreen forests retrieved by remote sensing. (a) Predicted versus observed canopy nitrogen concentration based on PLS regression model(n = 18).(b) Forest composition as a fraction of deciduous and evergreen species in relation to the plot-level canopy N. (c) Measured foliar N concentration for deciduous components of field plots in relation to SpecTir %N. (d) Measured foliar N concentration for evergreen components of field plots in relation to SpecTir %N. Figure 7. Effects of topographic factors on predicted wood growth. (a) Effect of aspect on predicted wood growth; (b) Elevational pattern in predicted wood growth; (c) Effect of slope on predicted wood growth. In this study, we estimated the wood growth across the Hubbard Brook valley by integration of remotely sensed data sets with ecosystem process models and distributed climate data. Predicted wood growth ranged from about 150 g m-2 year-1 to 460 g m-2 year-1 with a mean of 330 g m-2 year-1. Aspect trends in predicted wood growth indicate that wood growth in south facing area is greater than in north facing area, reflecting the remotely sensed canopy N trend and the fact that south facing areas are less shaded with higher solar radiations. There are slight trends in wood growth with slope. The elevation trend is nonlinear, increasing from low to mid elevations and then decreasing at upper-elevation sites where evergreen stands become increasingly dominant. Spatial patterns also reveals effects of forest succession after harvest. Overall, canopy N was the primary driver to regulate wood productivity. It proved the importance of remote sensing of canopy N in this study. Linking remote sensing with process-based ecosystem models can be an important approach to provide critical information about forest dynamics at landscape scales. In the future, we will compile more field data across the whole valley, such as soil properties, vegetation factors, hydrologic factors, disturbance history, to assess the spatial regulators of canopy nitrogen and wood growth. We will also extend our carbon analysis to include foliage production, and our nitrogen analysis to N cycling fluxes. Figure 4. Spatial pattern in SpecTIR remotely sensed canopy N across HB valley. The remotely sensed canopy nitrogen concentration showed a great variability across HB valley. It ranged from 0.9% to 2.3% with a mean of 1.61%. The south facing slopes showed greater values than the north facing slopes. Highest canopy N areas were located to experimental watersheds subject to clear cuts, such as Watershed 2,4,5, and 101. It reveals that canopy N also reflected the forest ages in the experimental watersheds. Younger forests have greater canopy N. Forest managements had great impact on canopy N which could be retrieved by using high spectral resolution remote sensing. f Figure 6. Spatial pattern in predicted wood growth across the HB valley. Predicted wood growth ranged from about 150 g m-2 year-1 to 460 g m-2 year-1 with a mean of 330 g m-2 year-1. Aspect trends in predicted wood growth indicate that wood growth in south facing area is greater than in north facing area, reflecting the remote sensed canopy trend (Fig 4) and the fact that south facing areas are less shaded with higher solar radiations. Figure 7-a shows a strong positive correlation between southness (cosine of aspect) and wood growth with a R-squared of 0.56. Elevation trends show a decline in growth with elevation (Fig 7-b). Higher wood productions occur at mid-elevation and young stands. The predicted wood growth reflect a combined effect of forest management, soil, and species composition that upper elevation areas are dominated by evergreen species(red spruce, balsam fir and eastern hemlock). Through the effect on canopy nitrogen, forest community succession exerted its effect on the wood production, e.g. at the experimental watersheds 2,4,5 and 101. The results also showed a slight decline in growth with grid slopes (Fig 7-c). It is due to the similar relationship of canopy nitrogen with slope, the effect of slope on solar radiations and the fact that greater slope areas are dominated by evergreen species. Figure 8. Relationship between remotely sensed canopy N and predicted wood growth. Although topographic factors showed great impact on predicted wood growth (Fig 7), The similarity of spatial pattern of canopy nitrogen and predicted wood growth reflects canopy N was the primary driver of predicted wood growth across the HB valley. A significantly positive correlation between them (Fig 8) shows remotely sensed canopy N can explain 98% of variation in predicted wood growth. This suggests that the leaf level foliar N–photosynthesis relationship used in the model translates to similar patterns of carbon gain for whole forest canopies. Previous studies in the same region reported the similar results that canopy nitrogen was an indicator of ecosystem spatial patterns of growth, both in the measured field data and as predicted by models. The combined effect of forest management, soil, and species composition is reflected on the observed patterns of canopy nitrogen, which controlled the productivity. a b Figure 1. Schematic diagram of the approach used in this analysis. A canopy N concentration map over HBEF was developed from airborne imaging spectrometer (ProSpecTIR VS system) data collected by the SpecTIR, LLC. The regression model was established using measured canopy %N data and then applied to the entire image data set to derive spatially explicit estimates of canopy %N across the HBEF valley. The predicted canopy nitrogen was in line with the observed (Fig 3-a). The relative proportions of deciduous and evergreen forests (Fig 3-b) and the foliar N concentration (Figs 3-c,d) of each component for each pixel were estimated by the linear relationships between remotely sensed canopy N and the corresponding observed values. Figure 2. Parameterization of MTCLM to estimate distributed climate data at HBEF. (a) Elevation map of the Hubbard Brook valley with measurement plots. (b) Temperature lapse rates derived from HBEF stations (n=7). (c) Average annual precipitation by elevation for rain gauges across HBEF (n=24). (d) Relationship between predicted and observed global solar radiation at HQ. c d a b c d Figure 5. Predicted versus observed wood growth. For wood production, model predictions corresponded reasonably well with measured values. Although the scatter around predicted versus observed regression line, comparison with the 1:1 line shows that model predictions overestimated measured values. This bias could arise from errors in the model, error in the input parameters used in the simulation, or differences between the specific plant tissues included in the model versus those captured by the field measurements. In PnET, wood production includes woody root tissue as well as aboveground stem and branch production, whereas observed wood production was derived from allometric equations that included aboveground woody tissues, but not woody roots. Another source of error in predicted values corresponded to errors in the remotely-derived canopy nitrogen. The importance of canopy N as a driver for patterns of growth has been showed in the same region and in Fig 8. Watersheds C anopyN (% ) 0.00 -1.00 1.01 -1.25 1.26 -1.50 1.51 -1.75 1.76 -2.00 2.01 -3.11 . 0 1,000 2,000 3,000 4,000 5,000 m Watersheds W ood grow th 153 -230 231 -278 279 -322 323 -363 364 -407 408 -460 . 0 1,000 2,000 3,000 4,000 5,000 m Acknowledgements This study was supported by the National Science Foundation and the US Forest Service. We thank Hubbard Brook Ecosystem Study (HBES) to provide climate and wood growth survey data. We thank Andrew Ouimette for his thoughtful discussion and comments. a b c
-
Upload
brianna-chambers -
Category
Documents
-
view
217 -
download
0
Transcript of Estimating wood growth across the Hubbard Brook valley: an analysis linking imaging spectroscopy and...
_
_
WS7 WS9WS8
WS3
WS4WS5
WS2WS6
WS1
WS101
Plots
Streams
Experimental watersheds
Elevation High : 1003 m
Elevation Low : 222 m
0 1000 2000 3000 4000 5000m
ÜMt. Cushman (977 m)
Mt. K
ineo
(10
15
m)
Mirror Lake (213 m)
Estimating wood growth across the Hubbard Brook valley: an analysis linking imaging spectroscopy and ecosystem model PnET Zaixing Zhou, Scott Ollinger, Mary Martin, Lucie LepineEarth Systems Research Center, Institute for the Study of Earth, Oceans, and Space University of New Hampshire, Durham, NH, 03824 USA
Introduction
Methods
Results and Discussion
Summary and Future Work
References
Ecosystem model PnET-II was used to estimate the spatial wood growth across the Hubbard Brook valley (Fig 2-a). The valley was delineated into grid cells in a 30-m resolution. For each cell, PnET-II was run for either of deciduous stands or evergreen stands, or both for the mixed.
The mountain microclimate model MTCLIM was applied to estimate the climate variables (maximum and minimum temperature, precipitation, and PAR) across the valley in conjunction with a DEM (Fig 2-a) and the climate from the HBEF headquarter (HQ) weather station. Temperature estimates were based on the base (HQ) temperatures and a temperature lapse rate. Separate lapse rates were derived for daily maximum and minimum temperature(Fig 2-b). Precipitation estimates at the site were based on the daily record of precipitation from the base, and a specified ratio of annual total precipitation between the site and the base(Fig 2-c). Radiation estimates were based on the observation that the diurnal temperature range (from minimum temperature to maximum temperature) is closely related to the daily average atmospheric transmittance. Predicted radiation was in good agreement with observations at HQ (Fig 2-d).
The complementary nature of remote sensing and ecosystem modeling has been widely demonstrated to bring benefits in studies of terrestrial carbon cycling. Ecosystem models focus on the mechanical biogeochemical processes that regulate carbon and nutrient cycling. Remote sensing can now provide regular, high –resolution spatially continuous information of land surface characteristics relevant to modeling, such as canopy nitrogen (Martin et al., 2008). Integration of remotely sensed data sets with ecosystem process models and distributed climate data provides a means for regional assessment of carbon fluxes and analysis of the underlying processes affecting them (Ollinger & Smith, 2005).
In this study, we examined spatial patterns of forest productivity across a topographically and floristically diverse 3160-ha valley at the Hubbard Brook Experimental Forest (HBEF), in central New Hampshire, USA. Our approach involved integration of a forest process model (PnET-II, Aber and others 1995) with spatially-explicit canopy nitrogen estimates, derived from high-resolution imaging spectroscopy. A mountain microclimate model MTCLIM (Thornton & Running, 1999) was also used to estimate the spatial climate variables across the valley. Data for wood growth collected from a network of measurement plots were used to validate model predictions and to help interpret the spatial pattern of predicted growth.
Aber, J.D., Ollinger, S.V., Federer, C.A., Reich, P.B., Goulden, M.L., Kicklighter, D.W., Melillo, J.M., Lathrop, R.G., 1995. Predicting the effects of climate change on water yield and forest production in the northeastern United States. Climate Research 5, 207–222. Martin, M.E., Plourde, L.C., Ollinger, S.V., Smith, M.-L., McNeil, B.E., 2008. A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment 112, 3511–3519. Ollinger, S.V., Smith, M.-L., 2005. Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data. Ecosystems 8, 760–778. Thornton, P.E., Running, S.W., 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology 93, 211–228.
Figure 3. Foliar N and relative proportions of deciduous and evergreen forests retrieved by remote sensing. (a) Predicted versus observed canopy nitrogen concentration based on PLS regression model(n = 18).(b) Forest composition as a fraction of deciduous and evergreen species in relation to the plot-level canopy N. (c) Measured foliar N concentration for deciduous components of field plots in relation to SpecTir %N. (d) Measured foliar N concentration for evergreen components of field plots in relation to SpecTir %N.
Figure 7. Effects of topographic factors on predicted wood growth. (a) Effect of aspect on predicted wood growth; (b) Elevational pattern in predicted wood growth; (c) Effect of slope on predicted wood growth.
In this study, we estimated the wood growth across the Hubbard Brook valley by integration of remotely sensed data sets with ecosystem process models and distributed climate data. Predicted wood growth ranged from about 150 g m-2 year-1 to 460 g m-2 year-1 with a mean of 330 g m-2 year-1. Aspect trends in predicted wood growth indicate that wood growth in south facing area is greater than in north facing area, reflecting the remotely sensed canopy N trend and the fact that south facing areas are less shaded with higher solar radiations. There are slight trends in wood growth with slope. The elevation trend is nonlinear, increasing from low to mid elevations and then decreasing at upper-elevation sites where evergreen stands become increasingly dominant. Spatial patterns also reveals effects of forest succession after harvest. Overall, canopy N was the primary driver to regulate wood productivity. It proved the importance of remote sensing of canopy N in this study. Linking remote sensing with process-based ecosystem models can be an important approach to provide critical information about forest dynamics at landscape scales. In the future, we will compile more field data across the whole valley, such as soil properties, vegetation factors, hydrologic factors, disturbance history, to assess the spatial regulators of canopy nitrogen and wood growth. We will also extend our carbon analysis to include foliage production, and our nitrogen analysis to N cycling fluxes.
Figure 4. Spatial pattern in SpecTIR remotely sensed canopy N across HB valley.The remotely sensed canopy nitrogen concentration showed a great variability across HB valley. It ranged from 0.9% to 2.3% with a mean of 1.61%. The south facing slopes showed greater values than the north facing slopes. Highest canopy N areas were located to experimental watersheds subject to clear cuts, such as Watershed 2,4,5, and 101. It reveals that canopy N also reflected the forest ages in the experimental watersheds. Younger forests have greater canopy N. Forest managements had great impact on canopy N which could be retrieved by using high spectral resolution remote sensing.
f
Figure 6. Spatial pattern in predicted wood growth across the HB valley.Predicted wood growth ranged from about 150 g m-2 year-1 to 460 g m-2 year-1 with a mean of 330 g m-2 year-1. Aspect trends in predicted wood growth indicate that wood growth in south facing area is greater than in north facing area, reflecting the remote sensed canopy trend (Fig 4) and the fact that south facing areas are less shaded with higher solar radiations. Figure 7-a shows a strong positive correlation between southness (cosine of aspect) and wood growth with a R-squared of 0.56. Elevation trends show a decline in growth with elevation (Fig 7-b). Higher wood productions occur at mid-elevation and young stands. The predicted wood growth reflect a combined effect of forest management, soil, and species composition that upper elevation areas are dominated by evergreen species(red spruce, balsam fir and eastern hemlock). Through the effect on canopy nitrogen, forest community succession exerted its effect on the wood production, e.g. at the experimental watersheds 2,4,5 and 101. The results also showed a slight decline in growth with grid slopes (Fig 7-c). It is due to the similar relationship of canopy nitrogen with slope, the effect of slope on solar radiations and the fact that greater slope areas are dominated by evergreen species.
Figure 8. Relationship between remotely sensed canopy N and predicted wood growth.Although topographic factors showed great impact on predicted wood growth (Fig 7), The similarity of spatial pattern of canopy nitrogen and predicted wood growth reflects canopy N was the primary driver of predicted wood growth across the HB valley. A significantly positive correlation between them (Fig 8) shows remotely sensed canopy N can explain 98% of variation in predicted wood growth. This suggests that the leaf level foliar N–photosynthesis relationship used in the model translates to similar patterns of carbon gain for whole forest canopies. Previous studies in the same region reported the similar results that canopy nitrogen was an indicator of ecosystem spatial patterns of growth, both in the measured field data and as predicted by models. The combined effect of forest management, soil, and species composition is reflected on the observed patterns of canopy nitrogen, which controlled the productivity.
a b
Figure 1. Schematic diagram of the approach used in this analysis.
A canopy N concentration map over HBEF was developed from airborne imaging spectrometer (ProSpecTIR VS system) data collected by the SpecTIR, LLC. The regression model was established using measured canopy %N data and then applied to the entire image data set to derive spatially explicit estimates of canopy %N across the HBEF valley. The predicted canopy nitrogen was in line with the observed (Fig 3-a). The relative proportions of deciduous and evergreen forests (Fig 3-b) and the foliar N concentration (Figs 3-c,d) of each component for each pixel were estimated by the linear relationships between remotely sensed canopy N and the corresponding observed values.
Figure 2. Parameterization of MTCLM to estimate distributed climate data at HBEF. (a) Elevation map of the Hubbard Brook valley with measurement plots. (b) Temperature lapse rates derived from HBEF stations (n=7). (c) Average annual precipitation by elevation for rain gauges across HBEF (n=24). (d) Relationship between predicted and observed global solar radiation at HQ.
c d
a b c d
Figure 5. Predicted versus observed wood growth.For wood production, model predictions corresponded reasonably well with measured values. Although the scatter around predicted versus observed regression line, comparison with the 1:1 line shows that model predictions overestimated measured values. This bias could arise from errors in the model, error in the input parameters used in the simulation, or differences between the specific plant tissues included in the model versus those captured by the field measurements. In PnET, wood production includes woody root tissue as well as aboveground stem and branch production, whereas observed wood production was derived from allometric equations that included aboveground woody tissues, but not woody roots. Another source of error in predicted values corresponded to errors in the remotely-derived canopy nitrogen. The importance of canopy N as a driver for patterns of growth has been showed in the same region and in Fig 8.
Watersheds
CanopyN(%)
0.00 - 1.00
1.01 - 1.25
1.26 - 1.50
1.51 - 1.75
1.76 - 2.00
2.01 - 3.11
.
0 1,000 2,000 3,000 4,000 5,000m
Watersheds
Wood growth
153 - 230
231 - 278
279 - 322
323 - 363
364 - 407
408 - 460
.
0 1,000 2,000 3,000 4,000 5,000m
AcknowledgementsThis study was supported by the National Science Foundation and the US Forest Service. We thank Hubbard Brook Ecosystem Study (HBES) to provide climate and wood growth survey data. We thank Andrew Ouimette for his thoughtful discussion and comments.
a b c
