Remote sensing‐based landscape indicators for the evaluation of threatened‐bird habitats in a tropical forest. Issue 13 (18th May 2017)

• Record Type: Journal Article
• Authors: Singh, Minerva; Tokola, Timo; Hou, Zhengyang; Notarnicola, Claudia
• Is Part Of: Ecology and evolution. Volume 7:Issue 13(2017:Jul.)
• Page Start: 4552
• Page End: 4567
• Publication Date: 2017-05-18
• Subjects: forest patches -- habitat suitability -- IUCN Red listed birds -- Laos -- production forest -- remote sensing; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.2970
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Avian species persistence in a forest patch is strongly related to the degree of isolation and size of a forest patch and the vegetation structure within a patch and its matrix are important predictors of bird habitat suitability. A combination of space‐borne optical (Landsat), ALOS‐PALSAR (radar), and airborne Light Detection and Ranging (LiDAR) data was used for assessing variation in forest structure across forest patches that had undergone different levels of forest degradation in a logged forest—agricultural landscape in Southern Laos. The efficacy of different remote sensing (RS) data sources in distinguishing forest patches that had different seizes, configurations, and vegetation structure was examined. These data were found to be sensitive to the varying levels of degradation of the different patch categories. Additionally, the role of local scale forest structure variables (characterized using the different RS data and patch area) and landscape variables (characterized by distance from different forest patches) in influencing habitat preferences of International Union for Conservation of Nature (IUCN) Red listed birds found in the study area was examined. A machine learning algorithm, MaxEnt, was used in conjunction with these data and field collected geographical locations of the avian species to identify the factors influencing habitat preference of the different bird species and their suitable habitats. Results show that distance from different forest patches played a more important role in influencing habitat suitability for the different avian species than local scale factors related to vegetation structure and health. In addition to distance from forest patches, LiDAR‐derived forest structure and Landsat‐derived spectral variables were important determinants of avian habitat preference. The models derived using MaxEnt were used to create an overall habitat suitability map (HSM) which mapped the most suitable habitat patches for sustaining all the avian species. This work also provides insight that retention of forest patches, including degraded and isolated forest patches in addition to large contiguous forest patches, can facilitate bird species retention within tropical agricultural landscapes. It also demonstrates the effective use of RS data in distinguishing between forests that have undergone varying levels of degradation and identifying the habitat preferences of different bird species. Practical conservation management planning endeavors can use such data for both landscape scale monitoring and habitat mapping. Abstract : Multiple types of remote sensing data were used to characterize variation in forest structure in Laos and to identify the different factors that influence the habitat suitability for endangered birds. Both landscape and local forest‐structure variables were found to be important for identifying habitat preferences of different avian species in a human‐modified ecosystem of Laos. Proximity to forest patches (which is a landscape scale driver) was found to be the most important factor in helping retain endangered avian species in tropical forest–agricultural matrix ecosystems in Laos.