Abstract
As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.
Highlights
The development of simple methods for land use and land cover classification is essential to allow researchers and policy-makers to understand land use and land use changes, carbon emissions, and ecosystem dynamics at scale
Based on the spatial resolution and the value of each Landsat pixel (30 m), the harmonic-fitted mean Enhanced Vegetation Index (EVI) and the original mean EVI values were calculated annually and monthly (Table 2 and Figure 5). This resulted in the formation of 722 mean EVI profiles for 12 land cover categories, which described the behaviors of the land cover categories vegetation index spectral response at the SOS and EOS as well as during leaf-shedding phenology (LSP) and LFP
EVI meatnhephSOenSotloogEiOcaSl pbheahsae vainodrsitfriosmals2o01a4n–i2n0d1ic7a.tiTonheohf itghhe livgehgetteadtiolinghletafb-flluueshcionglosreiansodnic. aTthees the SOS to EOS phhigahsleigahnteddidtairskablosxoraepnreinsednitcsathtieopnhoasfetohfesevpeagraettiaontioofnthleeavfe-gfleutasthioinnigndseexasporonf.ileTshoef ahllig12hllaigndhted dark box reprecosveenrtcsattehgeorpiehsadsuerionfgstehpe amriadt-idorny LoSfPthpehavsee.getation index profiles of all 12 land cover categories during thTehemoidbt-adirnyedLSmPeapnhaEsVeI. phenological profiles of the forest and cropland categories show the high-peak vegetation index values accrued at the end of the growing season from August–October and the mid EVI values observed during the growing season (May–July) and dry season (November to January) from 2000–2003 (TM) and from 2014–2017 (OLI) (Figure 9a,b)
Summary
The development of simple methods for land use and land cover classification is essential to allow researchers and policy-makers to understand land use and land use changes, carbon emissions, and ecosystem dynamics at scale. This study was designed to determine the vegetation thresholds for individual land cover categories through an analysis of the phenological behaviors of major land cover categories for specific seasons and to develop a robust phenology-based threshold classification (PBTC) approach to be used for a single EVI classification method to map the land cover categories This could allow continuous monitoring with the aid of cloud-computing GEE classification techniques. We used Landsat TM (from 2000–2001), Landsat ETM 7 (2002–2003), and Landsat 8 OLI imagery (from 2014–2017) to assess the phenological behaviors of the selected land cover categories and applied very first PBTC method to assess the mid-LSP season Landsat composite single EVI classification, which was used to classify the selected land cover categories in Cambodia using the fast cloud-computing GEE platform
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