Amazon deforestation: Rates and patterns of land cover change and fragmentation in Pando, northern Bolivia, 1986 to 2005

Abstract

Much research has focused on deforestation in the Amazon, particularly with proximity to roads and population centers as proximate causes. This research presents the analysis of rates and patterns of land cover change in Pando, northern Bolivia, an area with most of its tropical humid forest still intact. Using a decision tree classifier, five forest/non-forest (FNF) classifications were created for 1986, 1991, 1996, 2000, and 2005 from 40 Landsat images that were preprocessed and mosaicked. FNF trajectory images were created for each date pair to indicate areas of stable forest and non-forest, and areas and rates of de/reforestation. Mean patch size, perimeter-area ratio, fractal dimension, and aggregation index metrics were calculated for the FNF trajectory images based on increasing buffer distances from road and along the main access road. In 2005, forest covered 95% of the area in Pando. Large areas of aggregated deforestation occur nearest the department capital of Cobija, along the border with Brazil, and about 50 km west and east of Cobija along the principal access road. Deforestation becomes patchier with increased distance from the population center and laterally from the road. Multiple non-linear relationships exist between the fragmentation metrics and distance from road. The results have implications for understanding and managing the spatial contiguity of these forests, which provide valuable ecological services as well as the livelihood base for many inhabitants.

Roads as Drivers of Change: Trajectories across the Tri‑National Frontier in MAP, the Southwestern Amazon

“…Derived data products of tasseled cap brightness, greenness, and wetness indices were calculated along with a mid-infrared vegetation index and a three-by-three moving window variance for each mosaic date as input to a decision tree classification. Due to striping in the visible and thermal bands in many of the available Landsat images, only the near-and mid-infrared bands were used along with the derived products to create a forest, pasture and bare-built classification, regrouped here to a forest and non-forest classification for each mosaic date (see [35] for more detail on processing of imagery). The image striping problem limited the spectral data available, and due to this problem, we created derived products, rather than use much of the raw image bands, as these derived products did not have the striping problem (e.g., TCA analysis which is a form of Principal Components Analysis (PCA) itself a technique commonly used to destripe images, and so the first three components of the TCA were striping free, and used in concert with the unaffected image bands).…”

“…Compumine software was used, which is a data mining software which predicts the specified land cover classes, and was established here using a split-sample validation, where we used 85% of our training sample points to create the decision rules and tree classifier and the remaining 15% were used to test the tree output. The rules, once developed and tested for accuracy (each year was analyzed separately and percentage accuracy of the rules were 98-99.8% accurate, see [35] for more detailed information) were then incorporated into the ERDAS Knowledge Engineer rule-based classifier to create each year's land cover classifications. Classification accuracy was assessed using over 350 training samples collected during fieldwork from 2005 to 2006 and Kappa coefficient and overall percent accuracy for each class and for the overall classification, with resulting accuracies for 2005 of greater than 90%.…”

Remote Sensing-Based Fractal Analysis and Scale Dependence Associated with Forest Fragmentation in an Amazon Tri‑National Frontier

“…Significant attention has been drawn to areas of tropical forest cover, and specifically the Amazon region across the past few decades due to the alarming rates of clearing which have been occurring in the Amazon region [3][4][5][6][7][8][9][10][11][12][13]. Such losses have profound impacts on biodiversity, global carbon storage, potential future and current climate changes, all of which have significant impacts on the resilience of social-ecological systems [14].…”

“…Window analysis with designed convolution kernels has been widely applied to measure land cover pattern changes between time lags [31][32][33][34][35]. Alternatively, buffer analysis can be used to calculate land cover pattern changes but along multiple distance lags [12,36,37]. However, window analysis cannot capture the detailed information as the kernel operation blurs the images inevitably, and likewise, buffer analysis cannot characterize the spatial patterns within each buffer zone.…”

Private and communal lands? The ramifications of ambiguous resource tenure and regional integration in Northern Bolivia

“…The models are strong, notably due to a positive effect of having more land and being born in a rural area. Further, the integration effect is indeed strong: households farther from Cobija and the IOH had significantly less pasture area (Marsik et al 2011). However, results for formalization, credit and conflict were insignificant.…”