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Open AccessArticle

Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region

Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA
Asian Disaster Preparedness Center, SM Tower, 24th Floor, 979/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok 10400, Thailand
Mekong River Commission Secretariat, 184 Fa Ngoum Road Unit 18, Ban Sithane Neua, Sikhottabong District, Vientiane 01000, Laos
Geospatial Analysis Lab, University of San Francisco, 2130 Fulton St., San Francisco, CA 94117, USA
Authors to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1472;
Received: 12 March 2020 / Revised: 19 April 2020 / Accepted: 30 April 2020 / Published: 6 May 2020
(This article belongs to the Section Environmental Remote Sensing)
Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model. Data samples were created from the SERVIR-Mekong land cover data series. Data sets were created for 2 categories both containing 8 classes. The 2 categories included—any generic class to change into a specific one and vice versa. Classes included the following: Aquaculture; Barren; Cropland; Flooded Forest; Mangroves; Forest; Plantations; Wetlands; and Urban. The training points were used to sample a series of satellite-derived surface reflectance products and other data layers such as information on slope, distance to road and census data, which represent the drivers of change. The classifier was trained in binary mode and showed a clear separation between change and no change. An independent validation dataset of historical change pixels show that all median change probabilities are greater than 80% and all lower quantiles, except one, are greater than 70%. The 2018 probability change maps show high probabilities for the Plantations and Forest classes in the ‘Generic to Specific’ and ’Specific to generic’ category, respectively. A time-series analysis of change probability shows that forests have become more likely to convert into other classes during the last two decades, across all countries. We successfully demonstrated that historical change patters combined with big data and machine learning technologies are powerful tools for predictive change analytics on a planetary scale. View Full-Text
Keywords: land cover change; land use planning; Mekong; remote sensing; machine learning land cover change; land use planning; Mekong; remote sensing; machine learning
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Poortinga, A.; Aekakkararungroj, A.; Kityuttachai, K.; Nguyen, Q.; Bhandari, B.; Soe Thwal, N.; Priestley, H.; Kim, J.; Tenneson, K.; Chishtie, F.; Towashiraporn, P.; Saah, D. Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region. Remote Sens. 2020, 12, 1472.

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