884 M. I. Mahmoud et al.
FIG. 1 Locations of Mount Nlonako, Ebo Forest and various human activities within the study area in the Littoral Region of Cameroon. Data for intact forest were derived from the Intact Forest Landscapes (2000–2014) datasets, and data for tree cover loss from the Hansen/ UND/Google/USGS and NASA global dataset.
the Planet Labs Inc. application interface (Planet Team, 2017) in conjunction with human land-use interpretations (roads, logging, land clearing and oil palm plantations). These data were then used to assess land-use/land-cover change of natural forest in the study region. Cloud-cover and shadow effects masked parts of the study area, preventing land-cover monitoring of the entire Littoral Region through optical remote sensing.
Categorization and accuracy assessment
We categorized a cloud-free subset of Landsat images from 1975 and 2017 (covering 13,845 km2) that served as the effec- tive study area for this research (Fig. 1). We recognized six major land-cover categories: water body, urban area, cleared land, disturbed vegetation, natural forest and cloud cover. Our land-cover categorization was conducted using the random forest algorithm, which is a machine-learning supervised image classifier (Breiman, 2001). Random homo- geneous training and testing sampleswere collectedmanually from the 1975 and 2017 images for categorization. We optimized the accuracy and area of the 1975 and
2017 land-cover maps using the error-adjusted approach (Olofsson et al., 2014). The three steps involved in the approach to determine the map accuracies were sampling, response design and accuracy analysis. As a result, the producer’s accuracy, user’s accuracy, overall accuracy and confidence intervals were generated for the final land- cover maps produced.
Spatio-temporal analysis and mapping fine-scale drivers of deforestation
Weused a three-stage analysis to identify retrospectively the human disturbances (deforestation, land clearing and oil palm plantation development) that threatened forest cover and biodiversity in the study area of the Littoral Region. Firstly, based on the land-cover maps for 1975 and 2017 we investigated hindcast land-cover change to derive and quan- tify land-cover type change. The change detection approach was used to compute uniform temporal conversion from land cover in an initial state to a subsequent land use, and depict irregular variation verified to be human disturbance and alteration of natural forest. Secondly, landscape patterns were quantified using the
largest patch index computed from the land-cover maps of 1975 and 2017. The index provides quantitative informa- tion at the patch, class and landscape levels (Pardini, 2004). We focused on the largest patch index as a useful indicator of fragmentation or aggregation of the land-cover category of interest (in this case natural forest cover). The index is ex- pressed as a percentage of land-cover patch. As the value of the largest patch approaches 0, the size of the corresponding patch type decreases. The largest patch index is equal to 100% when the entire landscape is made up of a single patch that corresponds to the patch type (McGarigal & Marks, 1995). Hypothetically, a decline in the index from a maximal score of 100% (equivalent to 1) for an intact land- scape implies that larger fragments are being re-fragmented, reaching 0 when all fragments are ultimately converted to a
Oryx, 2020, 54(6), 882–891 © 2019 Fauna & Flora International doi:10.1017/S0030605318000881
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