ForestGapR: An r Package for forest gap analysis from canopy height models. Issue 8 (20th June 2019)

• Record Type: Journal Article
• Authors: Silva, Carlos A.; Valbuena, Ruben; Pinagé, Ekena R.; Mohan, Midhun; de Almeida, Danilo R. A.; North Broadbent, Eben; Jaafar, Wan Shafrina Wan Mohd; de Almeida Papa, Daniel; Cardil, Adrian; Klauberg, Carine
• Editors: Graham, Laura
• Is Part Of: Methods in ecology and evolution. Volume 10:Issue 8(2019)
• Page Start: 1347
• Page End: 1356
• Publication Date: 2019-06-20
• Subjects: airborne laser scanning -- change detection -- forest canopy gaps -- forest ecosystem -- forest gap analysis -- mapping -- r -- remote sensing; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13211
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: In forest ecosystems, many functional processes are governed by local canopy gap dynamics, caused by either natural or anthropogenic factors. Quantifying the size and spatial distribution of canopy gaps enables an improved understanding and predictive modelling of multiple environmental phenomena. For instance knowledge of canopy gap dynamics can help us elucidate time‐integrated effects of tree mortality, regrowth and succession rates, carbon flux patterns, species heterogeneity and three‐dimensional spacing within structurally complex forest ecosystems. Airborne Laser Scanning (ALS) has emerged as a technology that is well‐suited for mapping forest canopy gaps in a wide variety of forest ecosystems and across spatial scales. New technological and algorithmic advances, including ALS remote‐sensing, coupled with optimized frameworks for data processing and detection of forest canopy gaps, are allowing an enhanced understanding of forest structure and functional processes. This paper introduces ForestGapR, a cutting‐edge open source r package for forest gap analysis from canopy height models derived from ALS and other remote sensing sources. The ForestGapR package offers tools to (a) automate forest canopy gap detection, (b) compute a series of gap statistics, including gap‐size frequency distributions and spatial distribution, (c) map gap dynamics (when multitemporal ALS data are available) and (d) convert forest canopy gaps detected into raster or vector layers as per user requirements. As case studies, we run ForestGapR on ALS data collected over four different tropical forest regions worldwide. We hope this new package will enable further research towards understanding the distribution, dynamics and role of canopy gaps not only in tropical forests, but in other forest types elsewhere.