gfw partner meeting 2017 - parallel discussions 3: monitoring beyond forests
TRANSCRIPT
GFW PARTNERSHIP MEETINGWASHINGTON, DC | FEBRUARY 8TH & 9TH
PHOTO: CIFOR #GFWPartners17
#GFWPartners17Presenters: Matt Hansen, Tasso Azovedo, Steven Brumby, Andy Arnell and Pete Bunting
PARALLEL DISCUSSIONS 3:MONITORING BEYOND FORESTS
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Tocantins Piauí Maranhão Bahia MatoGrosso do
Sul
MinasGerais
Paraná Rio Grandedo Sul
MatoGrosso
São Paulo
CR
OP
LAN
D A
REA
(M
HA
)
CROPLAND EXTENT (2000) AND EXPANSION (2000-2014)
ag2000 (Mha) expansion (Mha)
MATOPIBA
South Americasouthern hemispheregrowing season soybeancultivated area
South Americasouthern hemispheregrowing season soybeancultivated area
South Americasouthern hemispheregrowing season soybeancultivated area
Bare ground
Grass/shrubs/moss
Trees0% 100%Landsat 2010 percent cover
Vegetation continuous fields
Global bare ground
Permanent bare ground, 2000-2012
0% 100%
Permanent bare ground
Percent bare ground times-series
0%
100%
Urbanization
Dallas-Fort Worth, TX2006-2008
2008-2010
Bare ground increase
21
10 km
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Anual Land Cover & Land Use Mapping ofBrazil
Global Forest Watch Partners Meeting
Washington D.C.– 09 Feb, 2017
LUCF is the largest source of GHG emissionsin Brazil
Emissions GHG in Brazil (Mt CO2e) Estimations of emissions for LUCF has being a challenge by lack of frequent and update information on land use change.
1994 2002 2010
Problem
• Maps Expensive and Slow to produce (visual interpretation)
• With large intervals (5-7 years) most of the degradation and loggind is mising
• In the absence of annual land use maps deforestation is used as proxy
• Lack of deforestation data in most biomes
Who is involved
A initiative from
co-creators
Technology Partners
Support
Funders
Annual land cover/use maps of Brazil covering all
national territory
(since 1985)
Matrix of transitions/changes for any pair of years
Annual mosaic of 9,5 billion pixels (30x30mt) representing Brazil in
each year.
28 bands/parameters for each pixel
• 7 landsat bands (blue, green, red, thermal…)
• 21 Fractions and index (gv, npv, ndvi, ndfi …)
Project in 3 phases of implementation
1985 2000 2008 2017
Collection 2
17 years
2000-2016
Collection 3
33 years
1985-2017
Collection 1
8 years
2008-2015
MapBiomas Aplications in Development by partners
Deforetation Rate for All Biomas Gross Net Primary Forest Secondary Forest
Urbanization Map of Brasil
Deforestation heritage of Main Brazilian Crops
Observatory of Public Lands and Protected Areas
Mapping priority areas for restoration
Monitoring desertification process
GHG emissons estimations
General Approach
USGS
Coleção
LandSat
Google Earth
Engine
Code Editor / PhytonDesenvolvimento & Produção
(Pecuária)
WorkspaceProdução
Servidor de
mapas e dados
Google Cloud
Computing Google
Cloud Storage
Dashboard / WebsiteDivulgação dos Dados
First extension of the iniciative in CHACO (2017)
A key element
•Multiply local capacity
•Easy to replicate in other regioes and contexts through the Priduction Interface (Workspace)
Who is involved
A initiative from
co-creators
Technology Partners
Support
Funders
From Pixels to Answers
Global forecasting using a living atlas of the world
Steven Brumby, PhD, Co-Founder & Chief Strategy Officer,
[email protected] | 505-423-3216
descarteslabs.com | @DescartesLabs
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Building a Living Atlas of the World
continuously updated map from multiple sensors39
WEB SERVER
RAW DATA
DERIVED DATA
AUTOMATED
ANALYSIS
CODES
METADATA
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+ …
MODIS
daily 250m/pixel
Planet RapidEye
monthly 5m/pixel
LANDSAT
weekly 30m/pixel
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Sentinel-2
weekly 10m/pixel
Sentinel-1
weekly 20m/pixel
Landsat NIR/SWIR
weekly 30m/pixel
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Descartes Labs processes time sequences of satellite observations
into cloud-free, scientifically calibrated images for analysis.NASA MODIS Iowa 2002-2014 49
50DESCARTES “NLCD” 2015
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Descartes System: Zoom in on Descartes-CID2015
>90% match, trained using historical imagery +USDA CDL
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DESCARTES LABS CORN YIELD FORECAST
EXPECTED ERROR BY DAY-OF-YEAR
DESCARTES LABS CORN YIELD FORECAST
BY DAY-OF-YEAR FOR 2004-2015
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USGS NLCD 2001, 2006, 2011
National land-cover maps are produced only every
5 years, and each political entity uses a different set
of land-cover classes. We use these maps to train
our machine learning system and apply to both our
real-time and historical imagery datasets.
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60ESA GlobCover 2009 300m/pixel (derived from ESA ENVISAT)
Landsat 8 cloud-free composite61
Global vegetative health 2015 62
63Descartes Global Land Cover 2015 250m/pixel (derived from NASA MODIS)
Andy Arnell,(on behalf of UNEP-WCMC, BirdLife International, IUCN, NHM)
9th February 2017
Monitoring beyond forests: a biodiversity perspective
Background
Ability to monitor biodiversity change is critical.
Previously…
DateTitle
Land use
Low resolution
Limited time periods
Land cover
Full coverage – many habitats
Limited time periods ~5 yr intervals
And now…
GFW data!
GFW Biodiversity layers
GFW platform with near real-time alerts
Biodiversity intactness Biodiversity importance
DateTitle
However: forest habitats only, no land use data (plantations are missing etc.)
Expand to other habitats
Advantages:
• Biodiversity importance – more refined range data, non-forest species
• Intactness index – not just forest!
But which habitats first?
Look at key habitats for species
• shrubland,
• grassland,
• wetlands/rivers/lakes
DateTitle
Mammals
Amphibians
Birds
Recent change products
DateTitle
From Landsat archive:
• Global surface water
• Global human settlement
Improve land use data
DateTitle
Primary
UrbanSecondary
Cropping
Pasture
Needs RS and on the ground data.
Advantages:• Intactness index more precise• Can refine estimates for the Half Earth concept
CSIRO - temporal LU layers becoming available
But agriculture still challenging: • where? • how intensive?
Improve land use data
What to focus on monitoring?
Look at main threats to species:
• agriculture,
• residential/commercial development
• (invasive species)
Look at intensity and location:
improve statistics, in-situ data collection, RS
and machine learning
DateTitle
Date
Joppa et al., 2016
Summary
Change data is critical for biodiversity conservation.
GFW provides this.
But we need to:
Expand to other habitats
Look at key habitats for species:
shrubland, grassland, wetlands/rivers/lakes
Improve land use data
Look at main threats to species:
agriculture (including plantations), residential/commercial development
Look at intensity and location
Title
@AU_EarthObs
Global Mangrove Watch
Richard Lucas, Ake Rosenqvist,
Pete Bunting, Lammert Hilarides
et al.,
Why Mangroves?
• 2000-2005 - 12% decrease in area (15.2 – 13.7 millionha). (FAO, 2007)
• Loss of 0.2 million ha/yr-1 in areal extent (1980-2005).(FAO, 2007)
• Primary causes of loss:– Conversion to aquaculture
– Forest Use
– Diversion of freshwater
• Reduction of ecosystem services– Carbon
– Fisheries
– Coastal protection and resilience
Colour composite
ALOS
PALSAR/JERS-1
radar imagery.
JERS-1 (1996),
PALSAR (2007),
PALSAR (2010)a
Time-series Change using RADAR
Aquaculture
Kalimantan, Indonesia
RADAR (SAR)
‘sees’ through
cloud.
How often is
there a cloud just
over the bit of the
image you want
information on?!?
https://earthengine.google.com/timelapse/#v=-
0.74029,103.35356,9.483,latLng&t=1.38
2016
1985
GMW map
showing
mangrove
expansion (1996-
2010) in blue
Growing Coastline: Sumatra Indonesia
https://earthengine.google.com/timelapse/#v=5.2832
7,-52.74341,8.455,latLng&t=0.09
1985 2016
Time series of JERS-1 SAR (Red),
ALOS PALSAR (Green) and
ALOS-2 PALSAR-2 (Blue)
(1996, 2007 and 2015).
Red is loss and
blue is expansion
Dynamic Coastline: French Guiana
Global Mangrove Watch (GMW)
Globally there have been significant changes in mangrove extent…
150°0'0"E
150°0'0"E
120°0'0"E
120°0'0"E
90°0'0"E
90°0'0"E
60°0'0"E
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30°0'0"W
60°0'0"W
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90°0'0"W
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30°0
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30°0
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Mangrove Gain Mangrove Loss Mangrove Gain and Loss
GMW: Methods
• Current focus has been on SAR data from JAXA
– JERS-1 (1993-1998)
– ALOS PALSAR (2007-2010)
– ALOS-2 PALSAR-2 (2015-Present)
Coastal
Mask
Mangrove
‘Habitat’
Mangrove
Baseline
(2010)
Change
1996
Change
2015
Change
2007
Change
2008
Change
2009
Change
2016JRC Water Occurrence
2010 PALSAR
Random Forests
Classification
Giri 2000Wetlands
Atlas 2010Coastal Line
Random Forests
Classification
Giri 2000Wetlands
Atlas 2010Coastal Line
2010 PALSAR
Random Forests
Classification
2010 Mangrove baseline
(1996/2015) SAR data
Map-to-Image Change Method
Analysis undertaken in the RSGISLib software (www.rsgislib.org) and associated python modules.
GMW: 2010 Baseline of Mangroves
2010 Baseline
Where are we up to and going?
• We have produced global products for 1996, 2010 and 2015 (Version 1.0).
– Validation of extent and change based on airborne and VHR spacebornemapping undertaken across the globe
– Subsequent refinement of algorithms.
• Further refinements planned throughout 2017 to increase the accuracy of mapping.
• Integration planned with the WRI platforms
• Potential for integrating ESA Sentinel-1 SAR data
– Better discrimination of mangroves and proximal forest.
– Additional and more frequent detection of change.
• More urgent need to provide information on the state and dynamics of mangroves because of increasing evidence of an adverse response to climate change.
Questions
Substantive dieback of
Mangroves, northern
Australia, from Darwin
to Cape York – 1000’s
km!.