Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

• Main Authors: Frederick N. Numbisi, Frieke Van Coillie
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Remote Sensing
• Subjects: Sentinel-1; SAR; cocoa agroforests; Digital Hemispherical Photographs (DHPs); canopy gap; spatial correlation
• Online Access: https://www.mdpi.com/2072-4292/12/24/4163

A reliable estimation and monitoring of tree canopy cover or shade distribution is essential for a sustainable cocoa production via agroforestry systems. Remote sensing (RS) data offer great potential in retrieving and monitoring vegetation status at landscape scales. However, parallel advancements in image processing and analysis are required to appropriately use such data for different targeted applications. This study assessed the potential of Sentinel-1A (S-1A) C-band synthetic aperture radar (SAR) backscatter in estimating canopy cover variability in cocoa agroforestry landscapes. We investigated two landscapes, in Center and South Cameroon, which differ in predominant vegetation: forest-savannah transition and forest landscape, respectively. We estimated canopy cover using in-situ digital hemispherical photographs (DHPs) measures of gap fraction, verified the relationship with SAR backscatter intensity and assessed predictions based on three machine learning approaches: multivariate bootstrap regression, neural networks regression, and random forest regression. Our results showed that about 30% of the variance in canopy gap fraction in the cocoa production landscapes was shared by the used SAR backscatter parameters: a combination of S-1A backscatter intensity, backscatter coefficients, difference, cross ratios, and normalized ratios. Based on the model predictions, the VV (co-polarization) backscatter showed high importance in estimating canopy gap fraction; the VH (cross-polarized) backscatter was less sensitive to the estimated canopy gap. We observed that a combination of different backscatter variables was more reliable at predicting the canopy gap variability in the considered type of vegetation in this study—agroforests. Semi-variogram analysis of canopy gap fraction at the landscape scale revealed higher spatial clustering of canopy gap, based on spatial correlation, at a distance range of 18.95 m in the vegetation transition landscape, compared to a 51.12 m spatial correlation range in the forest landscape. We provide new insight on the spatial variability of canopy gaps in the cocoa landscapes which may be essential for predicting impacts of changing and extreme (drought) weather conditions on farm management and productivity. Our results contribute a proof-of-concept in using current and future SAR images to support management tools or strategies on tree inventorying and decisions regarding incentives for shade tree retention and planting in cocoa landscapes.