Determination of plastic leakage sources and pathways is essential in plastic pollution mitigation. Finding ways to stem land-sourced plastic waste leakage requires understanding its sources. Spatial analysis conducted in a geographic information system (GIS) environment and remote sensing investigation uncovered insights into the distribution of plastic leakage in the lower Mekong River basin (LMRB). The main objectives of this approach were: (i) to map plastic leakage density using multi-source geospatial data; and (ii) to identify plastic leakage source hotspots and their accumulation pathways by incorporating hydrological information. Mapping results have shown that plastic leakage density was highly concentrated in urban areas with a high intensity of human activities. In contrast, the major pathways for plastic leakage source hotspots were the high morphometric areas directly influenced by facilities, infrastructure, and population. The overall efforts in this study demonstrate the effectiveness of the proposed novel method used for predicting plastic leakage density and its sources from land-based activities. It is also accomplished using multi-geospatial data with GIS-based analysis to produce a graphical model for plastic leakage waste density in each region that non-technical personnel can easily visualize. The proposed method can be applied to other areas beyond the LMRB to improve the baseline information on plastic waste leakage into the river.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.