Improving tree cover estimates for fine-scale landscape ecology

• Published in: Landscape ecology Vol. 33; no. 10; pp. 1691 - 1696
• Main Authors: Mendenhall, Chase D., Wrona, Anna M.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2018; Springer Nature B.V
• Subjects: automation; Biomedical and Life Sciences; Canopies; canopy; Costa Rica; Deforestation; Digitization; Ecological monitoring; Ecology; Environmental Management; Forests; global change; Habitat fragmentation; habitats; Identification methods; Internet; Landsat; Landsat satellites; Landscape; Landscape Ecology; Landscape/Regional and Urban Planning; landscapes; Life Sciences; Mapping; Nature Conservation; Pattern recognition; Polls & surveys; Remote sensing; Short Communication; Surveys; Sustainable Development; Test procedures; Trees; Variance; Costa Rica; Deforestation; Conservation science; Countryside biogeography; Google Earth Engine; Land use classification; Forest cover; Biodiversity
• ISSN: 0921-2973; 1572-9761
• DOI: 10.1007/s10980-018-0704-2

Context Mapping the presence of trees is an important tool for assessing tree-covered habitats, their changes, and calculating variables, like forest area and fragmentation. Objective Despite the popularity of automated pattern recognition to make tree cover maps, their accuracy and precision are rarely tested or compared to more modest methods, like human-based pattern recognition to identify tree cover. Methods Here, we test the performance of two computer-generated tree mapping products, the Global Change Forest database and the Carnegie Landsat Analysis System, against ground surveys and a human-made tree cover map created using Google Earth to hand digitize the presence and absence of trees in a diversified agricultural region in Costa Rica (934 km 2 ). Results The human-made tree cover map properly classified 100% ground survey sites and explained 81% of the variance in percent of canopy cover values from the field. The Global Change Forest database misclassified 18 of 23 ground survey sites in deforested locations and explained 6% of the variance in percent of canopy cover values from ground surveys. The Carnegie Landsat Analysis System misclassified 9 of 23 ground survey sites in deforested locations and explained 38% of the variance in percent of canopy cover values from the field. Conclusions Our results suggest that the Global Change Forest database overestimated tree cover by of 20% and the Carnegie Landsat Analysis System by 1%. We caution landscape ecologists working at fine spatial scales against using computer-generated tree cover, especially in the partially forested lands that increasingly cover the planet.