Unmanned Aerial Systems (UAS) have emerged as a capable platform for measuring vegetation health, structure and productivity. Products derived from UAS imagery typically have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail. This study uses UAS-captured imagery from the Chobe Enclave of northern Botswana. Flights were conducted across a gradient of savanna sites classified as grass-, shrub-, or tree-dominated. We compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. Sensor types were also compared, to determine whether multispectral data improves estimates of vegetation structure at the expense of spatial resolution. We found that leveraging multispectral reflectance information aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. Comparisons are made between two crown delineation techniques, and the efficacy of each technique within savanna environments is discussed. The methods presented hold potential to inform field sampling protocols and UAS-based techniques for autonomous crown delineation in future dryland systems research. These findings advance research for field and remote sensing analyses assessing degradation in heterogeneous landscapes where varying vegetation structure has implications on land use and land functions.
【 预 览 】
附件列表
Files
Size
Format
View
A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment.
PDF
download
878987797
028-85220240
