Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.

Excessive emissions of greenhouse gases — of which carbon dioxide is the most significant component, are regarded as the primary reason for increased concentration of atmospheric carbon dioxide and global warming. Terrestrial vegetation sequesters 112–169 PgC (1PgC = 10¹⁵g carbon) each year, which plays a vital role in global carbon recycling. Vegetation carbon sequestration varies under different land management practices. Here we propose an integrated method to assess how much more carbon can be sequestered by vegetation if optimal land management practices get implemented. The proposed method combines remotely sensed time-series of net primary productivity datasets, segmented landscape-vegetation-soil zones, and distance-constrained zonal analysis. We find that the global land vegetation can sequester an extra of 13.74 PgC per year if location-specific optimal land management practices are taken and half of the extra clusters in ~15% of vegetated areas. The finding suggests optimizing land management is a promising way to mitigate climate changes.

Aims: Unmanned aerial vehicles (UAVs), i.e. drones, have recently emerged as cost-effective and flexible tools for acquiring remote sensing data with fine spatial and temporal resolution. It provides a new method and opportunity for plant ecologists to study issues from individual to regional scales. However, as a new method, UAVs remote sensing applications in plant ecology are still challenged. The needs of plant ecology research and the application development of UAVs remote sensing should be better integrated.Methods: This report provides a comprehensive review of UAV-based remote sensing applications in plant ecology to synthesize prospects of applying drones to advance plant ecology research.Important Findings: Of the 400 references, 59% were published in remote sensing journals rather than in plant ecology journals, reflecting a substantial gap between the interests of remote sensing experts and plant ecologists. Most of the studies focused on UAV remote sensing’s technical aspects, such as data processing and remote sensing inversion, with little attention on answering ecological questions. There were 61% of studies involved community-scale research. RGB and multispectral cameras were the most used sensors (75%). More ecologically meaningful parameters can be extracted from UAV data to better understand the canopy surface irregularity and community heterogeneity, identify geometrical characteristics of canopy gaps and construct canopy chemical assemblies from living vegetation volumes. More cooperation between plant ecologists and remote sensing experts is needed to promote UAV remote sensing in advancing plant ecology research.