Remote Sensing for Biodiversity and Ecosystem Services InventorySão José dos Campos, São Paulo, Brazil, 12-15 June 2018

Jean P. OmettoEarth System Science Center (CCST)

Brazilian Institute for Space Research (INPE)

‘Contemporary human interaction with the environment’

Improvement of biomass estimation methods and emission estimation models for change of land use

MCTI, TCN, 2016.

Forest Biomass MapsSubproject 7

a) Saatchi et al. (2007)b) Saatchi et al. (2011)c) Nogueira et al. (2008)d) Baccini et al. (2012)e) MCTI (2010)

Biomassa Alta

Biomassa Baixa

Differences...Subproject 7

Biomass Map

LiDAR data - Transects Calibration

A

Biomass Estimation –Transects

B

Biomass andUncertainty map

Spatial data

Subproject 7

Goal: to propose improvements for biomass estimates and models that estimate emissions from land use change.

Climate change - efforts to develop plans to reduce greenhouse gas emissions - contain deforestation and so on.

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National CommunicationsNational Strategy for REDD +

CO2 emissions estimates

MSA - Project Environmental Monitoring Via Satellite In The Amazon Biome

Subproject 7

1000 LiDAR transectsWidth: 300mLength: 12,5KmArea covered: 3,750km2 (~0,11%)

192 flown twice (Arc/Degradation)91 directed to field plots

Randomly distributed:- PRODES forest- TERRACLASS Secondary vegetation

and - wetlands

50 Hyperspectral transects

LiDAR + HiperespectralSubproject 7

Collaboration with several universities and institutes in the region. GEDI mission, GLOBIOMASS-ESA

Sensor LiDAR HARRIER 68iScan frequency: 5 Hz to 200 HzFull WaveformIntensityField of view: Up to 30o

Inertial Measurement Units (IMU)Dual frequency GNSS receiver L1 / L2 CESSNA aircraft model 206

Full Scan Angle 45ºPulse density requested: 4 pulses / m2

Footprint: 30 cmFlying height 600 mTrack width on the ground: 494 m

Details of LiDAR SensorSubproject 7

Maximumdry season

Maximum after therainy season

Hiperespectral TransectsSubproject 7

- 50 images collected in two seasons

Criteria flight:- LiDAR data- Field data- DGPS - Representative vegetation

Details of Hiperespectral SensorSubproject 7

Sensor AISAFENIX

Camera Specifications VNIR SWIR

Spectral range 380 - 970nm 970 - 2 500nm

Spectral resolution 3.5nm 12nm

Spatial resolution 384 pixels

Altitude for 1m pixel size 660 m

Flying height 800 m

Detector CMOS Stirling cooled MCT

Spectral binning options 2x 4x 8x -

Number of spectral bands 348 174 87 274

Spectral sampling/band 1.7nm 3.4nm 6.8nm 5.7nm

- 361 bands- 1 m spatial resolution

Hiperespectral ImageSubproject 7

1st Level: field plot → the data are used to validate

the biomass estimated by LiDAR (2nd). Eq. used by

Chave et al 2014 and Longo et al 2016 (407 field plots

were used for this validation)

3rd - Map produced by extrapolating the biomass

biome using MODIS vegetation index, SRTM data, ppt

from TRMM, PALSAR and soil and vegetation maps.

Random Forest (nonparametric regression method)

→ correlates the above ground biomass within the

LIDAR transects to a list of variables, and then used

for the extrapolation of the biomass to the region.

The coefficient of determination and the root mean

squared error between the third level extrapolated

biomass data and the LiDAR data were R2=0.8059 and

20.58 MgC.ha-1

Forest Biomass Map

Tested other methodologies: Maxent, Kriging, IDW

Steps of the Methodology – Biomass MapSubproject 7

Forest Biomass MapSubproject 7

Electric power transmission

CuriositiesSubproject 7

Geoglyph - ACRE

CuriositiesSubproject 7

Geoglyph - Amapá

CuriositiesSubproject 7

Terrestrial LiDARAmazonFace project

Source of Cartographic Data: IBGE / M.M.A.

Forest 2020 - UK Space Agency, as part of the International Partnership Programme (IPP)

The main objectives include:

1) The first goal is the Advance on of Land

Cover and Land Use Maps looking at agriculture

expansion and natural vegetation.

2) Mapping of degraded areas (low

degradation, high degradation, very high

degradation), through methodology already used

for another area of study. It is based on the

frequency of occurrence of exposed soil in a

stipulated period.

3) Spatialization of the areas affected by

fires, will allow the identification of degraded

forests through these occurrences, contributing

with a correct classification of the land use of these

areas. We will use data provided by the Burnt

Areas Monitoring Program/INPE.

4) Carbon fluxes

Source: NATIONAL MUSEUM – UFRJ

Photo: Victor B. Quaresma (INPE)

DRY SEASON

WET SEASON

Mapping of

degraded areas

MOD17A2H.006: Gross Primary Productivity 8-Day L4 Global 500 m

The MOD17A2H version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meterpixel size based on the radiation-use

efficiency concept that can be potentiallyused as inputs to data models to calculate

terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about Gross PrimaryProductivity (GPP) and Net Photosynthesis(PSN). The PSN band values are the GPP

minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control layer. The quality layer contains quality information for both the GPP and the PSN.

First Results

• 14% of the area is on a high and a very high risk of forest fire;

• The risk of fire is higher in flat areas;

• Herbaceous vegetation and pasture are most vulnerable to fire;

• The Model Accuracy was of 74%.

• The model assists in long-term planning since it

provides information on where should be apply direct

financial resources for preservation and recovery;

• 18% of the area over very high risk of fire effectively

burned according to the CPTEC-INPE data. So, these

areas can be prioritized for regeneration;

• 82% of the area over very high risk of fire remaining

should be carefully monitored.

LUCCME

A generic open source framework to construct

spatially explicit LUCC allocation models for

different applications and scales.

You can use existing allocation, potential and demand

components (based on literature), or create new ones.

http://luccme.ccst.inpe.br

Potential AllocationDemandPotential AllocationDemandPotential AllocationDemandAllocation DemandPotential

Slide: Aguiar, Randow

Aguiar et al. (2012) Global Change BiologyINPE-EM 2.0 Components

Land Use demand

Land Allocation

model (LuccME

Brasil)

Intra-regional drivers/information:

Productivity (kg/ha, nr/ha)

Land availability

GL

OB

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EG

ION

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EN

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IOS

Global drivers

(GDP, population,

technology)

Intra-regional drivers

(institutional biophysical

technological drivers)

National demand for agricultural

products (ha or kg?)

Land Surface Model

(INLAND or other) Climate drivers

(precipitation, temperature)

Soil moisture

Seasonality indexes

Productivity (crops)

Land change maps

Regional Emission

Model

(INPE-EM Brasil)

Secondary vegetation dynamics

Biomassas growth rate

Land change maps

Mais informações disponíveis em:

http://luccme.ccst.inpe.br/

www.ccst.inpe.br/projetos/inland/

inpe-em.ccst.inpe.br/

http://www.terrame.org//

Social dimension: drivers and policiesConceptual framework of nitrogen emissions drivers in Latin America