Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

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.

Global Site Selection for Astronomy

“…This fact can also be deduced from Table 1. CC can be recorded with many different methods: by synoptic naked-eye observations (Lau & Crane 1997;Bréon & Colzy 1999;Norris 1999), by using lidar and radar methods (Intrieri et al 2002), by using an "All Sky Camera" (Ackerman & Cox 1981;Pfister et al 2003;Long et al 2006) or using satellite monitoring (Venkatappa et al 2019).…”

Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data

“…Hence, sub-pixel analysis may be useful to overcome these mixed pixel problems in future studies. In addition, compared to LULC mapping, using a Google Earth Engine-like platform [56,57], the proposed method is still a time-consuming and laborious work.…”

Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

“…Satellite remote sensing technologies provide an alternative approach to MRV tasks required for the REDD+ scheme in developing countries in the tropics, where the availability of field measurements is sparse [19]. In recent years, phenology-based classification methods using spectral remote sensing data and the enhanced vegetation index (EVI) have shown satisfactory accuracy [20][21][22][23][24]. Previous research [24,25] has shown promising results with high reliability and accuracy for vegetation [26], cropland [27] and land cover mapping [24,28].…”

“…In recent years, phenology-based classification methods using spectral remote sensing data and the enhanced vegetation index (EVI) have shown satisfactory accuracy [20][21][22][23][24]. Previous research [24,25] has shown promising results with high reliability and accuracy for vegetation [26], cropland [27] and land cover mapping [24,28]. The most recent studies classified land cover categories and changes using the moderate to high spatial resolution of the Landsat remote sensing data [24,29,30].…”