Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

Abstract

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

Impacts of Urban Expansion Forms on Ecosystem Services in Urban Agglomerations: A Case Study of Shanghai-Hangzhou Bay Urban Agglomeration

“…For spectral characteristics, in addition to spectral bands of Landsat data (i.e., blue, green, red, near infrared, shortwave infrared 1, and shortwave infrared 2), the normalized difference vegetation index (NDVI) [45] and the enhancement vegetable index (EVI) [46] were used to distinguish vegetation from other land use types; the normalized difference build index (NDBI) [47] was applied to assist the monitoring of urban construction land and rural settlement; and the normalized difference water index (NDWI) [48] was used to detect water bodies. Simultaneously, topography greatly impacts the distribution of different land use types [49]. Therefore, we calculated the elevation and slope using DEM.…”

“…where R is the number of threat factors; Y r is the set of grid cells on r's raster map; w r is the impact weight of threat factor r; A x is the accessible grid x; S jr indicates the relative sensitivity of landscape type j to threat factor r-the value is 0-1; i rxy is the impact distance of threat factor r, which can be divided into linear or exponential function of distance from threats to habitats [49].…”

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

“…The system represents a state-of-the-art workflow and visualization scheme meant to be adopted as a forest data source by national institutions and park managers. However, given that the tree cover concept includes secondary vegetation, tree plantations, and some crops as forests, further adaptation through refinement and processing is needed for its local use [23][24][25]. Vargas et al [26] reported that Terra-I and GLAD alerts underestimate the number and extent of deforestation events occurring on the ground due to persistent cloud cover and a mismatch between MODIS spatial resolution and the small size of forest clearings (for Terra-I).…”