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

Ate Poortinga,David Saah,Hannah Priestley,Aekkapol Aekakkararungroj,Jiwon Kim,Nyein Soe Thwal,Quyen Nguyen,Kritsana Kityuttachai,Peeranan Towashiraporn,Farrukh Chishtie,Karis Tenneson,Biplov Bhandari

doi:10.3390/rs12091472

Ate Poortinga, David Saah + Show 10 more

Open Access

https://doi.org/10.3390/rs12091472

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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.

Highlights

Over the past few decades, population and economic dynamics have driven major land cover transitions
This study has developed a novel means to simulate and analyze land cover dynamics using Earth Observation (EO), ground data and machine learning
Our machine learning-based approach is governed only by historical drivers of change, demonstrated by utilizing the land cover time-series maps generated by Regional Land Cover Monitoring System (RLCMS) for the Mekong region

Summary

Introduction

Over the past few decades, population and economic dynamics have driven major land cover transitions. There are a variety of approaches to simulate future land cover change trajectories These approaches include simple system representations with a few driving factors, to complex simulations based on a more profound understanding of specific interactions [7]. Examples of different models are listed in reviews such as Verburg et al [10], Schaldach and Priess [11], Matthews et al [12]. These models can be data intensive and the final results are mostly reliant on specific decisions made by the operator

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