A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment

Abstract

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

Figure 1. The false color composite of PALSAR 50-m orthorectified mosaic imagery (R/G/B  =  HH/HV/HH-HV) in Southeast Asia in 2009.

Country names were labeled as Myanmar (A), Thailand (B), Laos (C), Vietnam (D), Cambodia (E), Malaysia (F), Brunei (G), Indonesia (H), Philippines (I), Singapore (J), and East Timor (K). The inset graphs show forest, cropland, water body, and built-up land, respectively. The PALSAR 50-m mosaic data was unavailable in the West Papua and Papua regions.

Figure 2. The spatial distribution of geo-referenced field photos in the study area, as hosted in (A) the Global Geo-referenced Field Photo Library.

The circle size indicates the number of the field photos. The figure also shows the search options, selected photos with GPS locations, and the link to extract the MODIS time series data. More information can be found in the data portal (www.eomf.ou.edu/photos). (B) The Regions of Interest (ROIs) used for the algorithm training and results validation, which were acquired by referring to the field photos shown in Fig. 2A and Google Earth.

Figure 3. The resultant land cover map of Southeast Asia based on the PALSAR 50-m orthorectified mosaic data in 2009 and the decision tree algorithm.

Figure 4. The spatial distribution of forest cover in the Southeast Asia from (A) PALSAR 2009 forest map, (B) GlobCover 2009 forest map, and (C) MCD12Q1 2009 forest map.

The blue box in A shows the region missing PALSAR data (the West Papua and Papua regions).

Figure 5. The comparison between the three fractional forest maps at a spatial resolution of 1.5-km by 1.5-km gridcell: (A) PALSAR – MCD12Q1 and (B) PALSAR – GlobCover.

The maps show the differences between two maps. The inset histograms show frequencies at various levels of difference between two maps. The scatter plots show the comparison in forest area at the sub-national level among PALSAR, MCD12Q1 and GlobCover in 2009. The data from the four provinces (Irian Jaya Barat, Maluku, Maluku Utara, and Papua) are excluded due to missing PALSAR data.

Figure 6. The spatial distribution of forest fragmentation in Southeast Asia with (A) 9×9 pixel window, (B) 21×21 pixel window, and (C) 101×101 pixel window.

The stacked-bar histogram charts (D, E, and F) under the maps show the percent areas of five forest fragmentation categories corresponding with the forest fragmentation maps (A, B, and C).

Figure 7. Visual interpretation and comparison of different land cover products in a region mixed with natural forest and oil palm plantation.

A) the location of the case region in Borneo Island, Southeast Asia; B) the false color composited graph of Landsat 5 image (30 m, path/row = 117/56, R/G/B  =  Band NIR/Red/Green) on August 11, 2009; C) the false color composited graph of PALSAR image (R/G/B  =  HH, HV, HH/HV) in 2009; D) PALSAR-based land cover map (50 m) in 2009 from this study; E) GlobCover 2009 land cover map (300 m); and F) MCD12Q1 2009 land cover map (500 m). The differences of these three products in separating natural forest and oil palm plantation are obvious, MCD12Q1 considers oil palm plantation as forest, while GlobCover and PALSAR don't, and PALSAR has better performance in separating natural forest and oil palm plantation.