Next Article in Journal
Influence of Thorny Bamboo Plantations on Soil Microbial Biomass and Community Structure in Subtropical Badland Soils
Previous Article in Journal
Direct Method of Measuring the pH Value of Wood
Article

Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica

University of Maryland, Baltimore County, Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Forests 2019, 10(10), 853; https://doi.org/10.3390/f10100853
Received: 15 August 2019 / Revised: 5 September 2019 / Accepted: 27 September 2019 / Published: 30 September 2019
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
Global tree cover products are widely used in analyses of deforestation, fragmentation, and connectivity, but are rarely critically assessed. Inaccuracies in these products could have consequences for future decision making, especially in data-poor regions like the tropics. In this study, potential biases in global and regional tree cover products were assessed across a diverse tropical country, Costa Rica. Two global tree cover products and one regional national forest cover map were evaluated along biophysical gradients in elevation, precipitation, and agricultural land cover. To quantify product accuracy and bias, freely available high-resolution imagery was used to validate tree and land cover across these gradients. Although the regional forest cover map was comparable in accuracy to a widely-used global forest map (the Global Forest Change of Hansen et al., also known as the GFC), another global forest map (derived from a cropland dataset) had the highest accuracy. Both global and regional forest cover products showed small to severe biases along biophysical gradients. Unlike the regional map, the global GFC map strongly underestimated tree cover (>10% difference) below 189 mm of precipitation and at elevations above 2000 m, with a larger bias for precipitation. All map products misclassified agricultural fields as forest, but the GFC product particularly misclassified row crops and perennial erect crops (banana, oil palm, and coffee), with maximum tree cover in agricultural fields of 89%–100% across all crops. Our analysis calls into further question the utility of the GFC product for global forest monitoring outside humid regions, indicating that, in tropical regions, the GFC product is most accurate in areas with high, aseasonal rainfall, low relief, and low cropland area. Given that forest product errors are spatially distributed along biophysical gradients, researchers should account for these spatial biases when attempting to analyze or generate forest map products. View Full-Text
Keywords: forest cover; estimation bias; dry forests; logistic regression model; accuracy assessment forest cover; estimation bias; dry forests; logistic regression model; accuracy assessment
Show Figures

Graphical abstract

MDPI and ACS Style

Cunningham, D.; Cunningham, P.; Fagan, M.E. Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica. Forests 2019, 10, 853. https://doi.org/10.3390/f10100853

AMA Style

Cunningham D, Cunningham P, Fagan ME. Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica. Forests. 2019; 10(10):853. https://doi.org/10.3390/f10100853

Chicago/Turabian Style

Cunningham, Daniel; Cunningham, Paul; Fagan, Matthew E. 2019. "Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica" Forests 10, no. 10: 853. https://doi.org/10.3390/f10100853

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop