Spatial Analysis of the Drivers, Characteristics, and Effects of Forest Fragmentation

Abstract

Building on the existing literature, this study examines whether specific drivers of forest fragmentation cause particular fragmentation characteristics, and how these characteristics can be linked to their effects on forest-dwelling species. This research uses Landsat remote imaging to examine the changing patterns of forests. It focuses on areas which have undergone a high level of a specific fragmentation driver, in particular either agricultural expansion or commodity-driven deforestation. Seven municipalities in the states of Rondônia and Mato Grosso in Brazil are selected as case study areas, as these states experienced a high level of commodity-driven deforestation and agricultural expansion respectively. Land cover maps of each municipality are created using the Geographical Information System software ArcGIS Spatial Analyst extension. The resulting categorical maps are input into Fragstats fragmentation software to calculate quantifiable fragmentation metrics for each municipality. To determine the effects that these characteristics are likely to cause, this study uses a literature review to determine how species traits affect their responses to forest fragmentation. Results indicate that, in areas that underwent agricultural expansion, the remaining forest patches became more complex in shape with longer edges and lost a large amount of core area. This negatively affects species which are either highly dispersive or specialist to core forest habitat. In areas that underwent commodity-driven deforestation, it was more likely that forest patches would become less aggregated and create disjunct core areas. This negatively affects smaller, sedentary animals which do not naturally travel long distances. This study is significant in that it links individual fragmentation drivers to their landscape characteristics, and in turn uses these to predict effects on species with particular traits. This information will prove useful for forest managers, particularly in the case study municipalities examined in this study, in deciding which species require further protection measures. The methodology could be applied to other drivers of forest fragmentation such as forest fires.

Spatial metrics of deforestation in Central Sulawesi, Indonesia

“…The rate of forest fragmentation tends to increase in Palu City, while the Banggai Islands District shows a decrease. Forest fragmentation is a picture of a forest ecosystem that will be isolated which will have an impact on limiting movement between fauna, and followed by limitations in the processes of an ecosystem [28].…”

Local Fractal Connections to Characterize the Spatial Processes of Deforestation in the Ecuadorian Amazon

“…Values range from 0 (adjacent patches) to infinity. m [27,28,30,[38][39][40] CLUMPY Clumpiness index: measures the degree to which the landscape class is aggregated or clumped given its total area. This is the measure of patch aggregation.…”

“…none [37,[39][40][41][42] * Authors using the given metric for the characterization of the deforestation process in the Amazon region.…”

“…none [37,[39][40][41][42] *Authors using the given metric for the characterization of the deforestation process in the Amazon region. Two of the five spatial processes are defined as low-impact land transformation: (1) perforation, which is related to the dispersed agricultural plots, houses, and orchards of colonists inside the primary forest patches, and (2) dissection, which is a process characterized by spatial objects such as roads, power lines, irrigation infrastructure, and open channels, among another objects that dissect primary forest, with a constant width of the deforested strip.…”

Study on Spatiotemporal Characteristic and Mechanism of Forest Loss in Urban Agglomeration in the Middle Reaches of the Yangtze River

“…Over the years, various mathematical and spatial analysis methods have been applied to analyze forest loss. The Analytical Hierarchy Process (AHP), Time Series Analysis (TSA), Data Envelopment Analysis (DEA), and other mathematical methods have been used to analyze the trend characteristics, causes, and action mechanisms of forest loss [36][37][38]. These methods often use administrative regions as the analysis unit, which is effective for macro analysis but not for analyzing micro-spatial change.…”