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

Abstract

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.

Opportunities in farming research from an operations management perspective

“…Similarly, with free Earth Observation (EO) data availability, scholars are now able to address questions that they were not able to before. For example, by utilizing EO and geographic information system data, Poortinga et al (2020) run simulations to examine the effect of traditional drivers of change on land use and predict the chance of changes in different areas and how it may affect the agriculture and sustainability practices in those regions. Changes in climatic conditions too will behoove farmers to seriously examine their prefarming decisions and look for less resource/water-intensive alternatives.…”

A review of Google Earth Engine application in mapping aquaculture ponds

“…Overall accuracy and kappa coefficient were 88.42% and 0.84 in 1999, 95.99% and 0.94 in 2008, and 88% and 0.82 in 2018. Poortinga et al [19] studied to identify land cover change in the Mekong River region. From the 1988 to 2018 period, data was extracted using Landsat and MODIS collections, and a random forest machine learning classifier was used in the GEE platform.…”

Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine

“…Satellite derived forest information plays a crucial role in conservation planning and ecosystem monitoring [1][2][3][4]. Forest alert systems provide near-real-time data, strategically guiding the allocation of local forest conservation resources.…”