Abstract
Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.
Highlights
The mapping of cocoa commodity cropland is essential to the quantification of its ecosystem services and the disservices related to tropical forest cover loss
We summarize our conclusions as follows: 1. For the same window size and an invariant direction, reducing the synthetic aperture radar (SAR) image’s grey level or quantization marginally improved the texture-based classification accuracy, but significantly reduced the uncertainty in discriminating cocoa agroforests from other vegetation cover types
The magnitude of forest fragmentation by cocoa agroforests, which is concealed by vegetation indices from spectral reflectance, can be reliably mapped using texture measures from C-band SAR images
Summary
The mapping of cocoa commodity cropland is essential to the quantification of its ecosystem services and the disservices related to tropical forest cover loss. Agricultural land expansion, predominantly for oil palm, rubber, and cocoa plantations, contributes significantly to tropical deforestation [1,2,3]. These commodity cropping lands provide different ecological services in terms of carbon sequestration, habitat provision, and the conservation of biodiversity [4,5]. Cocoa production was responsible for 57% of the global agricultural land expansion rate (132,000 ha year−1) in the period 2000–2013 [2] Such figures need validation, at the national level, through mapping of actual cropping lands
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