Post on 05-May-2022
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
AL
/R
EG
ION
AL
SC
EN
AR
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