Local-scale accuracy assessment of vegetation cover change maps derived from Global Forest Change data, ClasLite, and su
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ORIGINAL PAPER
Local-scale accuracy assessment of vegetation cover change maps derived from Global Forest Change data, ClasLite, and supervised classifications: case study at part of Riau Province, Indonesia Sanjiwana Arjasakusuma 1
&
Muhammad Kamal 1 & Muhammad Hafizt 2 & Hernandea Frieda Forestriko 1
Received: 16 August 2017 / Accepted: 15 May 2018 # Società Italiana di Fotogrammetria e Topografia (SIFET) 2018
Abstract Massive deforestation in Indonesia drives the need for proper monitoring using appropriate technology and method. The continuing mission of Landsat sensor extends the observation to almost 30 years back, initiating the ability to monitor the dynamics of vegetation intensively. By taking the advantage of the Landsat archive, advanced semi-automatic classification method, namely ClasLite developed by Asner et al. (J Appl Remote Sens 3:33543–33543, 2009) and a new end-product of 30 m Global Forest Cover Change cover (GFC) datasets developed by (Hansen et al. in Science 342:850–853, 2013a), offered the ability to easily monitor deforestation and forest degradation with little or few knowledge of mapping. This study aims to assess the performance of these newly available products of GFC and the ClasLite method against the traditional pixel-based supervised classification of minimum distance to mean (MD), maximum likelihood (ML), spectral angle mapper (SAM), and random forest (RF). Visual image interpretation of pan-sharpened Landsat was carried out to measure the accuracy of each final map. Result demonstrated that GFC and CLaslite performance has 3 to 18% higher overall accuracy for mapping vegetation cover change compared with the conventional supervised analysis using MD, ML, SAM, and RF with ClasLite as the most accurate method with 78.14 ± 2%. Further adjustment of the cover change map of GFC by using forest extent from ClasLite was able to increase the accuracy of the original GFC data by 10%. Therefore, GFC and ClasLite ensure the ability to monitor vegetation cover change accurately in a simple manner. Keywords Accuracy assessment . Vegetation dynamics . ClasLite . GFC . Supervised classification
Introduction Land use and land cover change (LULCC) has been identified as one of the culprits in greenhouse gas (GHG) emission. Among the shifts in the LULCC, vegetation cover changes such as deforestation and reforestation contribute significantly Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12518-018-0226-2) contains supplementary material, which is available to authorized users. * Sanjiwana Arjasakusuma [email protected] 1
Remote Sensing Laboratory, Geographical Information Science Department, Faculty of Geography, Gadjah Mada University, Sekip Utara, Bulaksumur, Sinduadi, Sleman District, Yogyakarta, Daerah Istimewa Yogyakarta 55281, Indonesia
2
Oceanography Research Center, Indonesia Institute of Sciences, East Ancol, North Jakarta 11048, Indonesia
to the trade of carbon in the atmosphere. Deforestation accounts
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