Characterising social-ecological drivers of landuse/cover change in a complex transboundary basin using singular or ensemble machine learning

Kavhu, Blessing; Mashimbye, Zama Eric; Luvuno, Linda

doi:10.1016/j.rsase.2022.100773

Cited by 2 publications

(11 citation statements)

References 122 publications

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“…The workflow of the study design is depicted in Figure 2. This study builds from our previous work [41,46]. In Kavhu et al [46], we found that post-feature selected and climate-based regionalization improved the accuracy of LULC classification for both Machine Learning and Deep learning techniques within the Okavango, a transboundary basin.…”

Section: Methodssupporting

confidence: 58%

“…The most accurate LULC product was based on the Deep neural network (DNN) classification. For Kavhu et al [41], the utility of machine learning techniques and ensemble modeling to explain the social-ecological drivers of LULC within the Okavango basin was investigated. Social-ecological drivers of LULC were characterized.…”

Section: Methodsmentioning

confidence: 99%

“…The most accurate LULC product and the social-ecological drivers generated in previous studies are used in this study. For more detailed descriptions of methods for LULC classification, change detection, and the characterization of social-ecological drivers, readers should refer to Kavhu et al [46] and Kavhu et al [41].…”

Section: Methodsmentioning

confidence: 99%

“…where Category A have transitions from natural classes to artificial classes, Category B have transitions from artificial classes to natural classes, and Category C have transitions from natural classes to another natural class-adopted from Kavhu et al [41].…”

Section: Table 1 Shows a Summary Of Categories Of Lulc Transitions Ba...mentioning

confidence: 99%

“…The CT requires a resistance layer which is commonly derived from suitability layers in many ecological studies [38][39][40]. This study exploits a LULC susceptibility (synonym to suitability) layer generated based on an ensemble of machine learning (ML) in our previous study [41] to compute a resistance layer for use in CT. Understanding the connectivity pathways of LULC change would aid in the development of targeted measures to address the distribution and allocation of resources, for example, water resources.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

2022

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Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources.

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Section: Methodssupporting

confidence: 58%

Section: Methodsmentioning

confidence: 99%