Identification of Priority Areas for Ecological Restoration in Eastern Pará, Brazil

Abstract

This study aimed to identify priority areas for the passive restoration of the Arauaí river basin, municipality of Moju, Pará, using a multicriteria model. This basin is located in a region with intense land use dynamics and expansion of oil palm cultivation, and with high forest loss and fragmentation. The Weighted Linear Combination method was used, with the aggregation of five criteria (Natural Erosion Vulnerability, Potential Land Use and Cover to Passive Restoration, Proximity to the Primary Forest, Forest cover deficit in PPAs, and Distance from Roads and Highways). This analysis allowed to evaluate different biophysical classes and types of land use that affect the selection of areas for restoration. A total of 207.82 km² of priority areas for passive restoration were identified in the studied basin, with about 80% of its priority areas having medium to very high priority. The final map generated proved to be a useful instrument in the environmental management of restoration plans in the Amazon watershed.

Multi-Criteria Prioritization of Watersheds for Post-Fire Restoration Using GIS Tools and Google Earth Engine: A Case Study from the Department of Santa Cruz, Bolivia

“…The weights in the WLC method represent the importance of an indicator relative to the others and control trade-offs between indicators. Instead of assigning fixed weights to the analysis as in other studies [12,43,45,48], we created scenarios by varying criteria weights to observe the trade-offs.…”

“…Populated areas and productive areas were prioritized to reflect stakeholders' preference to restore ecosystem services in the vicinity of urban centers and human activities. Generally, studies do not prioritize locations close to roads or urban areas [45,48] as these are considered a disturbance that reduces the restoration's feasibility factor [13].…”

Spatial-temporal evolution of landscape degradation on the Guamá River Basin, Brazil

“…For that matter, for more elaborate evaluations on the veracity of spatial classification of secondary vegetation, the Kappa index (Hudson and Ramm, 1987) was calculated, as it measures the accuracy of spatial data by using an error matrix correlating the classes identified in the mapping to those identified in the field work. Values above 0.8 are considered excellent (Landis and Koch, 1977;Almeida and Vieira, 2019;Silva and Vieira, 2020).…”

Identification of Ecological Restoration Priority Areas Integrating Human Activity Intensity and Multi-Criteria Decision Analysis

“…Previous studies have tried some methods to identify the ERP areas. This includes evaluating the regional ecological environment and ecological security based on land use, ecosystem service, vegetation coverage, and other ecological indicators, and finding areas with more fragile ecological environment or lower ecological security for priority restoration [8][9][10]. Some other studies try to select the priority areas by using methods such as landscape ecology theory and multi-decision criteria analysis [11][12][13].…”