Abstract
We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia’s biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS.
Highlights
Mapping land cover is one of the key applications of remote sensing [1]
By analysing the importance of each phenological metric on classification accuracy we explore the seasonal differences of rubber plantations and natural forests based on Moderate Resolution Imaging Spectroradiometer (MODIS) time series
The best model resulted in an overall accuracy of 73.5% and included phenological metrics from
Summary
Mapping land cover is one of the key applications of remote sensing [1]. The increased availability of broad scale Earth observation data together with recent developments in multi-temporal analyses techniques have increased the quality of continental to global land cover maps [2], global forest cover maps [3], and global maps of cropland extension [4]. VEGETATION) capture images of the globe at moderate spatial but very high temporal resolution (2–3 days for MERIS and SPOT VEGETATION sensors; daily for AVHRR and MODIS sensors). This high temporal resolution facilitates monitoring dynamic inter- and intra-annual processes on the. Earth surface, which would not be observable using less frequent Earth observation data. This phenological information supports mapping recent land cover and monitoring land cover changes.
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