Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery.

Plastic waste monitoring technology based on Earth observation satellites is one approach that is currently under development in various studies. The complexity of land cover and the high human activity around rivers necessitate the development of studies that can improve the accuracy of monitoring plastic waste in river areas. This study aims to identify illegal dumping in a river area using the adjusted plastic index (API) and Sentinel-2 satellite imagery data. Rancamanyar River has been selected as the research area; it is one of the tributaries of Citarum Indonesia and is an open lotic-simple form, oxbow lake type river. Our study is the first attempt to construct an API and random forest machine learning using Sentinel-2 to identify the illegal dumping of plastic waste. The algorithm development integrated the plastic index algorithm with the normalized difference vegetation index (NDVI) and normalized buildup indices. For the validation process, the results of plastic waste image classification based on Pleiades satellite imagery and Unmanned Aerial Vehicle (UAV) photogrammetry was used. The validation results show that the API succeeded in improving the accuracy of identifying plastic waste, which gave a better correlation in the r-value and p-value by + 0.287014 and + 3.76 × 10-26 with Pleiades, and + 0.143131 and + 3.17 × 10-10 with UAV.

Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau.

Distinguishing the vegetation dynamics induced by anthropogenic factors and identifying the major drivers can provide crucial information for designing actionable and practical countermeasures to restore degraded grassland ecosystems. Based on the residual trend (RESTREND) method, this study distinguished the vegetation dynamics induced by anthropogenic factors from the effects of climate variability on the Mongolian Plateau during 1993-2012 using vegetation optical depth (VOD) and normalized difference vegetation index (NDVI), which measure vegetation water content in aboveground biomass and chlorophyll abundance in canopy cover respectively; afterwards, the major drivers within different agricultural zones and socio-institutional periods were identified by integrating agricultural statistics with statistical analysis techniques. The results showed that grasslands in Mongolia and the grazing zone of Inner Mongolia Autonomous Region (IMAR), China underwent a significant human-induced decrease in aboveground biomass during 1993-2012 and 1993-2000 respectively, which was attributable to the rapid growth of livestock densities stimulated by livestock privatization and market factors; by contrast, grasslands in these two regions did not experience a concurrent human-induced reduction in canopy greenness. Besides, the results indicated that grasslands in the grazing zone of IMAR underwent a significant human-induced increase in aboveground biomass since 2000, which was attributable to the reduced grazing pressure induced by China's ecological restoration programs; concurrently, grasslands in this region also experienced a remarkable increase in canopy greenness, however, this increase was found not directly caused by the decreased stocking densities. Furthermore, the results revealed that the farming and semi-grazing/farming zone of IMAR underwent a significant human-induced increase in both aboveground biomass and canopy greenness since 2000, which was attributable to the intensified grain production stimulated by market factors, open grazing regulation and confined feeding popularization. These findings suggest that China's grassland restoration practice has important implications for Mongolia to reverse the severe and continuous grassland degradation in the future.

Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo.

New plantations can either cause deforestation by replacing natural forests or avoid this by using previously cleared areas. The extent of these two situations is contested in tropical biodiversity hotspots where objective data are limited. Here, we explore delays between deforestation and the establishment of industrial tree plantations on Borneo using satellite imagery. Between 1973 and 2015 an estimated 18.7 Mha of Borneo's old-growth forest were cleared (14.4 Mha and 4.2 Mha in Indonesian and Malaysian Borneo). Industrial plantations expanded by 9.1 Mha (7.8 Mha oil-palm; 1.3 Mha pulpwood). Approximately 7.0 Mha of the total plantation area in 2015 (9.2 Mha) were old-growth forest in 1973, of which 4.5-4.8 Mha (24-26% of Borneo-wide deforestation) were planted within five years of forest clearance (3.7-3.9 Mha oil-palm; 0.8-0.9 Mha pulpwood). This rapid within-five-year conversion has been greater in Malaysia than in Indonesia (57-60% versus 15-16%). In Indonesia, a higher proportion of oil-palm plantations was developed on already cleared degraded lands (a legacy of recurrent forest fires). However, rapid conversion of Indonesian forests to industrial plantations has increased steeply since 2005. We conclude that plantation industries have been the principle driver of deforestation in Malaysian Borneo over the last four decades. In contrast, their role in deforestation in Indonesian Borneo was less marked, but has been growing recently. We note caveats in interpreting these results and highlight the need for greater accountability in plantation development.