Global Forest Watch: 5 Stories to Watch

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At the 14th World Forestry Congress in Durban, South Africa, Global Forest Watch introduced new data and research, uniting tree cover loss data derived from satellite imagery and the latest statistics from FAO’s Forest Resources Assessment 2015, to reveal five critical trends shaping the future of forest landscapes.

Education

CRYSTAL DAVIS, DIRECTOR, GLOBAL FOREST WATCH
FRED STOLLE, SENIOR ASSOCIATE, GLOBAL FOREST WATCH
BIG DATA, DEEP INSIGHTS
GLOBAL FOREST WATCH PRESENTS FIVE
STORIES TO WATCH
#WRIatWFC

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
WE NOW KNOW MORE ABOUT FORESTS

BIGGER DATA, BETTER DECISIONS?
Data DecisionsInsights

EXAMPLE: GLOBAL FOREST CHANGE
Forest Area
Loss & Gain
Tree Cover
Loss & Gain

SOURCE: GLOBAL FOREST RESOURCES ASSESSMENT 2015
Country statistics
National statistics
5-year intervals
Multiple variables

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
Global map, 30x30 meter pixels
Annual updates
Spatial datasets can be overlaid

FRA “forest” UMD “tree cover”
 
 
X 
 X

FRA UMD
Loss Loss
No
Change
Loss
Gain
No
Change
No
Change
Gain

FIVE STORIES TO WATCH
@globalforests
#WRIatWFC

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
TROPICS
TROPICS

TROPICAL TREE COVER
LOSS ACCELERATES
@globalforests
#WRIatWFC

TROPICAL FOREST LOSS
SOURCE: GLOBAL FOREST RESOURCES ASSESSMENT 2015;
HANSEN/UMD/GOOGLE/USGS/NASA
FRA reports 8.8 million ha of gross natural forest
loss per year (2010-2015).
80% of this loss occurred in the tropics
UMD/Google reports 10 million ha of tropical
tree cover loss in 2014

TROPICAL FOREST LOSS
SOURCE: Hansen/UMD/Google/USGS/NASA
3-year moving average
Annual tree cover loss

TREE COVER LOSS INCREASING IN TROPICAL
COUNTRIES OUTSIDE BRAZIL AND INDONESIA
Countries Outside Brazil/Indonesia
Brazil
Indonesia
SOURCE: Hansen/UMD/Google/USGS/NASA
*Solid line represents 3-year moving average
*Dotted line represents annual tree cover loss

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
MEKONG

THE MEKONG EMERGES
AS A HOTSPOT FOR LOSS

TREE COVER LOSS IN MEKONG AREA (MINUS CHINA)
CAMBODIA. LAOS, MYANMAR, VIETNAM
SOURCE: Hansen/UMD/Google/USGS/NASA
3-year moving average
Annual tree cover loss

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
CAMBODIA

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
CAMBODIA TREE COVER LOSS

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA; WORLD BANK. RUBBER PRICES CALCULATED
BY AVG. HISTORICAL MONTHLY DATA OF WORLD COMMODITY MARKETS.
CAMBODIA TREE COVER LOSS AND GLOBAL
RUBBER PRICES

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
DEM REP OF CONGO

DEMOCRATIC REPUBLIC
OF CONGO’S FORESTS
UNDER RISING
PRESSURES

TREE COVER LOSS IN THE DRC
SOURCE: Hansen/UMD/Google/USGS/NASA
3-year moving average
Annual tree cover loss
~50%

TREE COVER LOSS ANNUAL RATES (%)
SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
(10km)

YANGAMBI BIOSPHERE RESERVE (30KM FROM
KISANGANI, PROVINCE ORIENTALE)

Kisangani
Yangambi
SOURCE: SPOT 5, 2010-03-02, (AIRBUS DEFENCE AND SPACE, IGNFI)

1km
SOURCE: SPOT 5, 2010-03-02, (AIRBUS DEFENCE AND SPACE, IGNFI)

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
PERU

SOURCE: Hansen/UMD/Google/USGS/NASA
FRA DEFORESTATION IN PERU

TREE COVER LOSS IN PERU
SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
3-year moving average
Annual tree cover loss

CONVERSION TIMBER IN PERU
SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA; SKYBOX
Skybox image of the Ucayali Plantations

Currently working to
crowdsource a complete
temporal map of all logging
roads in the Congo Basin
•Merged datasets
•Adapted OpenStreetMap tools
•Organized Mapping parties
LoggingRoads.org

SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
DRY LANDS

DRY TROPICAL FORESTS
OVERLOOKED BUT UNDER
THREAT

TREE COVER LOSS IN PARAGUAY
SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
3-year moving average
Annual tree cover loss

TROPICS
MEKONG
DEM REP OF CONGOPERU
DRY LANDS
SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
TROPICS
DRY LANDS

FOREST AREA GAIN
SOURCE: GLOBAL FOREST RESOURCES ASSESSMENT 2015
Countries reporting
greatest annual
forest area gain
(2010-2015)
Annual forest area gain
Area
(000 ha)
% of 2010
forest area
1 China 1542 0.8
2 Australia 308 0.2
3 Chile 301 1.9
4 USA 275 0.1
5 Philippines 240 3.5
6 Gabon 200 0.9
7 Lao PDR 189 1.1
8 India 178 0.3
9 Viet Nam 129 0.9
10 France 113 0.7

PRIVATE SECTOR ACTION
SOURCE: HANSEN/UMD/GOOGLE/USGS/NASA
RSPO oil palm
concessions
Non-RSPO oil palm
concessions
RSPO certified
concessions show less:
•Encroachment into
protected areas
•Tree cover loss
•Primary forest loss
•Conversion of peatlands
•Fires

SECURING COMMUNITY FOREST RIGHTS
SOURCE: WRI PUBLICATION “SECURING RIGHTS, COMBATTING CLIMATE CHANGE”

Thank you!
@globalforests
#WRIatWFC
CRYSTAL DAVIS, DIRECTOR, GLOBAL FOREST WATCH
FRED STOLLE, SENIOR ASSOCIATE, GLOBAL FOREST WATCH

PANEL DISCUSSION
Leo Bottrill, Moabi
Crystal Davis, World Resources Institute
Patrick Houdry, Airbus
Fred Stolle, World Resources Institute
Ken MacDicken, Food and Agriculture Organization (Moderator)
@globalforests
#WRIatWFC

During last year’s partnership meeting, partners asked whether GFW should monitor land and forest values beyond trees. Since then, several GFW partners have been developing new approaches for monitoring land cover, land use, and values such as biodiversity, carbon, and water. Discussion topics include: what are needs for a monitoring system beyond forest area (e.g. for climate and biodiversity)? What role should GFW play in advancing new monitoring approaches? Which monitoring needs should we prioritize first?

Alertas Tempranas de Pérdida de Bosques tropicales en Perú usando Landsat

Alejandro Leon

Desde el 16 de marzo del 2017 el Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático (PNCBMCC) del Ministerio del Ambiente de Perú (MINAM) viene implementando una metodología para la detección de alertas tempranas de la pérdida de cobertura de bosques húmedos tropicales de Perú, la detección se hace cada semana usando imágenes de Landsat 7 y 8 calibradas a reflectancia al tope de la atmosfera (TOA). La cobertura de nubes, neblina y sombra son enmascaradas de las imágenes y para la detección de la pérdida de cobertura de bosque se desarrolló una técnica de análisis de mixtura espectral (SMA) que denominamos desmezcla espectral directa (DSU), el modelo de desmezcla espectral fue construido a partir de los endmembers de bosque primario y la pérdida de cobertura de bosques. Se detectó hasta un 25% de pérdida de cobertura de bosque dentro de un pixel de Landsat. Hasta el 25 de diciembre del 2017 se usaron 500 imágenes Landsat y se detectaron 137 142.99 ha de pérdida de cobertura de bosques húmedos tropicales, esta pérdida incluye la deforestación por expansión agropecuaria, actividades extractivas ilegales o informales, que puede incluir la apertura de caminos para la tala selectiva, se detecta de igual forma las pérdida natural de bosques producida por vientos huracanados, deslizamientos de tierra en áreas montañosas con fuerte pendiente, entre otros. Los resultados fueron verificados con imágenes satelitales de alta resolución espacial, sobrevuelos y trabajo de campo. La evaluación de la exactitud se realizó utilizando un método de muestreo estratificado aleatorio, obteniéndose una alta precisión de usuario y productor. Las alertas tempranas de pérdida de bosques se distribuyen y están disponibles en la plataforma geobosques (http://geobosques.minam.gob.pe/geobosque/visor/index.php)

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