Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010

Abstract

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 106 km2. The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests.

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

“…This approach takes advantages of the high-revisit characteristics of the MODIS sensor, which increase the possibility of capturing good quality images from optical sensors. Annual MODIS time-series have been used in previous research [3,12,[31][32][33], and most of them are not in tropical mountainous regions. It should be mentioned, however, that the coarse spatial resolution (500 m) may produce some inaccurate results from mixing pixels of small-area land cover types, such as narrow rivers.…”

“…The phenological characteristics of different land cover types were used to discriminate evergreen forests from other land cover types during the key phenology phase. Considering the effects of mixing pixels caused by outside factors such as clouds, shadows, fragments, the minimum and maximum 3% were selected from 30-m Landsat NDVI, EVI, and LSWI time-series data of different land cover types [3]. This strategy was also used for analysis of the key phenology phase for extracting tropical evergreen forests.…”

“…Five typical land cover types (evergreen forests, rubber plantations, built-land, farmlands, and water bodies) were classified in the study area. NDVI, EVI, and LSWI are calculated using formulas (1)- (3). Land cover data of 500-m MODIS MCD12Q1 was used to compare with the resultant map of tropical evergreen forests [27].…”

“…Tropical forests play a very important role in global carbon cycles, water and heat fluxes [2,3], biodiversity, and water and soil conservation, and produce half of the total Net Primary Production (NPP) of the world. However, across the world many tropical forests are being converted to farmlands, pastures, or other land cover types driven by the increasing demand for food, energy, and economic activity, among others [4].…”

Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

“…The authors found that SAR-based texture information combined with VNIR optical data led to an improved classification of vegetation. ALOS PALSAR and phenological information from the MODIS sensor were used by Qin et al (2016) to map forests in Monsoon Asia using decision tree algorithms. L-band SAR helped in reducing the limitations of frequent cloud coverage and improved separability of evergreen shrubs and crops from forests.…”