docDegradation monitoring - GOFC-GOLD Land Cover Project Office
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Degradation monitoring - GOFC-GOLD Land Cover Project Office
Degradation Monitoring Methods Carlos Souza Jr. [email protected] Imazon www.imazon.org.br Forest Degradation Selectively logged forest, Sinop-MT Deforested area for plantation, Sinop-MT Forest degradation has been defined as a type of land modification, which means that the original land cover structure and composition is temporarily or permanently changed, but it is not replaced by other type of land cover type (Lambin, 1999). Sources of Human Pressure that Cause Forest Degradation Remote Sensing Detection Highly Detectable Marginally Detectable Almost Undetectable Deforestation ► Forest fragmentation ► Recent slash-and-burn agriculture ► Major canopy fires ► Major roads ► Conversion to three monocultures ► Hydroelectric dams and other forms of flood disturbances ► Large-scale mining Selective logging ► Forest surface fires ► A range of edge-effects ► ‘Old-slash-and-burn agriculture ► Small scale gold-mining Unpaved secondary roads (6► Selective logging 20-m wide) Burned forests ► Selective thinning of canopy Forest fragmentation trees Hunting and exploitation of animal products ► Harvesting of most non-timber plants products ► Old-mechanized selective logging ► Narrow sub-canopy roads (<6m wide) ► Understorey thinning and and clear cutting ► Invasion of exotic species ► Spread of pathogens ► Changes in net primary productivity ► Community wide shifts in plant species composition ► Other cryptic effects of climate changes ► Most higher-order effects ► ► Roads Gold mining Peres et al., (2006), TREE ► Selective Logging Selective Logging in Sinop – MT, Brazil Photo: Carlos Souza Jr. Deforestation, Selective Logging and Fires Photo: P. Barreto, Paragominas, Paragominas, PA. 1993 ► Predominantly unplanned ► Harvesting intensity varies from 5 to 40 m3 of logs / ha ► Builds extensive road network ► Creates favor conditions for forest fires ► Catalyzes deforestation Souza Jr. and Roberts (2005) Available Methods to Detect and Map Selective Logging Mapping Approach Visual Interpretation Detection of Logging Landings + Buffer Studies Sensor Spatial Extent Disadvantages Does not require sophisticated image processing techniques Labor intensive for large areas and may be user biased to define the boundaries. Local Map total logging area (canopy damage, clearings and undamaged forest) Relatively simple to implement and satisfactorily estimate the total logging area Logging buffers varies across the landscape and does not reproduce the actual shape of the logged area. SPOT 4 Local Map forest canopy damage associated with logging and burning Simple and intuitive classification rules. It has not been tested in very large areas and classification rules may vary across the landscape. Souza Jr. et al. (2002) Landsat TM5 e ETM+ Local Map forest canopy damage associated with logging and burning Enhances forest canopy damaged areas. Requires two pairs of images and does not separate natural and anthropogenic forest changes. Alencastro Graça et al. (2005) Landsat TM5 Local Map total logging area (canopy damage, clearings and undamaged forest) Relatively simple to implement and satisfactorily estimate the total logging area. Free software available. It has not been tested in very large areas and segmentation rules may vary across the landscape. Asner et al., 2005 Landsat TM5 e ETM+ Three states of the Brazilian Amazon (PA, MT and AC) Map total logging area (canopy damage, clearings and undamaged forest) Fully automated and standardized to very large areas. Requires very high computation power, and pairs of images to forest change detection. Tested only with Landsat ETM+ Souza Jr., 2005b Landsat TM5 e ETM+ Local Map forest canopy damage associated with logging and burning Enhances forest canopy damaged areas. It has not been tested in very large areas and does not separate logging from burning damages. Landsat TM5 Local Stone and Lefebvre (1998) Landsat TM5 Local Matricardi et al. (2001) Landsat TM5 Brazilian Amazon Santos et al. (2002) Landsat TM5 Brazilian Amazon Souza Jr. e Barreto (2000) Matricardi et al. (2001) Monteiro et al. (2003) Silva et al. (2003) Landsat TM5 e ETM+ Souza Jr. et al. (2003) Change Detection CLAS NDFI+CCA Advantages Map total logging area Watrin e Rocha (1992) Decision Tree Image Segmentation Objective LAB - human dimension book, Chapter 3 (in prep.) Selective Logged and Forests Forest in Landsat Images 2000 1998 Selective logging and burning Selective logging 2001 1999 Old selective logging and burning Old Selective logging R5, G4, B3 Souza Jr. et al., (2003) Image Processing Steps (1) PRE-PROCESSING Image Registration Radiance Conversion Estimate Visibility and water vapor Landsat Correct Haze? Yes No Atmospheric Correction (ACORN) CCA NDFI ≤ 0.75 (3) SMA Canopy Damage Soil ≥ 10% 1 pixel ≤ Area ≤ 4 pixels Souza Jr. et al. (2005), RSE Landsat NPV Pixel Purity Index - (PPI) 40 million GV SVDC Soil Extract Patios Reflectance Space Apply Carlotto’s Technique (4) Enhance and Detect Canopy Damage NDFI (2) Build Spectral Library Shade GV + NPV + Soil + Shade = 1 pixels Visualization Scatter matrix Spectral curves Generic Image Endmembers Haze Correction Contaminated Image Corrected Image Ji-Parana, 231/67 – R3, G2, B1 Generic Image Endmembers Souza Jr. et al. (2005), RSE Mapping Selective Logging with Landsat Image Soil Fraction Roads Logged Forest 226/68 - 2001 (Sinop - MT) (Souza Jr. et al., 2005) Mapping Selective Logging with Landsat Image NPV Fraction Roads Logged Forest 226/68 - 2001 (Sinop - MT) (Souza Jr. et al., 2005) Mapping Selective Logging with Landsat Image GV Fraction Roads Logged Forest 226/68 - 2001 (Sinop - MT) (Souza Jr. et al., 2005) Mapping Selective Logging with Landsat Image NDFI (Normalized Difference Fraction Index) Logged Forest 226/68 - 2001 (Sinop - MT) Roads (Souza Jr. et al., 2005) Mapping Burned Forests with Landsat Image NDFI 226/68 - 2000 (Sinop - MT) (Souza Jr. et al., 2005) Mapping Burned Forests with Landsat Image NDFI 226/68 - 2001 (Sinop - MT) (Souza Jr. et al., 2005) Mapping Burned Forests with Landsat Image NDFI 226/68 - 2003 (Sinop - MT) (Souza Jr. et al., 2005) Standardized NDFI (2001) Rondônia Contextual Classification Algorithm - CCA Step 1: •Find log landings Soil > 10% •Find regions and calculate area 1 ≤ Area ≤ 4 pixels Step 2: •Grow a canopy damage region around log landings •Search for NDFI neighboring cell NDFI values If NDFI > 0.75 then Intact Forest if 0 ≤ NDFI ≤ 0.75 then Canopy Damage Soil Fraction Log Landings Canopy Damage CCA Results Conventional Logging Logged and Burned Souza Jr. et al. (2005), RSE Temporal Mapping Frequency Canopy Damage – Sinop, 226/68 2004 Canopy Damage 20-year Time Series – Sinop, 226/68 2004 Logging Damage Intensity 1 1 0.8 0.8 1 ML CL 0.8 Forest 0.4 0.6 CDF 0.6 CDF CDF 0.6 0.4 0.4 ML 0.2 0.2 CL ML 0.2 CL Forest Forest 0 0 20 40 GV 60 0 60 80 70 80 90 0 100 0 5 GVshd GVshd GV 1 10 NPV 15 20 NPV 1 1 0.8 0.6 0.6 0.4 CL Forest 0.8 0.6 CDF 0.8 CDF CDF ML 0.4 0.4 ML 0.2 0 ML 0.2 CL Forest 0 5 10 15 Soil 20 25 Soil Monteiro et al., (2007), SBSR 0 0.2 0.7 0.8 NDFI NDFI 0.9 1 0 CL Forest 0.8 1 NDVI NDVI 1.2 1.4 Class Separability Nonmechanized Logging Intact Class Mean Stdev. Mean Stdev. Managed Logging Mean Stdev. Conventional Logging Mean Stdev. Logged and Burned Mean Stdev. GV 40a 4 41a 5 41a 5 38b 9 25c 7 NPV 6a 2 5a 2 6a 2 10bc 4 11bd 3 Soil 2a 1 1a 1 3ab 1 4bc 3 7d 3 Shade 51a 3 53a 5 51ab 4 49bc 3 56d 3 NDFI 0.84a 0.08 0.87a 0.07 0.79b 0.07 Tukey test at P<0.01 0.58c 0.24 0.49d 0.22 Forest Fragments, Ji-Paraná - RO Forest Fragments Characterization Characterizing Forest Fragments 114 Alta Floresta – MT 227/67 - 2000 61 Fragments Inventoried 62 29 R=Soil, G=GV, B=NPV Characterizing Forest Fragments 114 Alta Floresta – MT 227/67 - 2000 61 Fragments Inventoried 62 29 INPE vs. Imazon INPE Total Gross Deforestation 2001 (16,186 km²) Deforestation Forest INPE vs. Imazon IMAZON Total Gross Deforestation 2001 (13.915 km²) Deforestation Forest Land Cover Classification NDFI NDFI NDFI NDFI NDFI Shade Fraction (%) Genetic Decision Tree Classifier NDFI NDFI Land Cover Mapping Legend Forest Green pasture/regeneration Pasture/agriculture Rock Savanna Soil Urban Clouds Shade Water Mapping Unofficial Roads Landsat TM - B3 Brandão Jr. and Souza Jr. (2006) Mapping Unofficial Roads Human Pressure - Aggregated Thanks!
