The mountain systems of the Hindu Kush Himalaya (HKH) are changing rapidly due to climatic change, but an overlooked component is the subnival ecosystem (between the treeline and snow line), characterized by short‐stature plants and seasonal snow. Basic information about subnival vegetation distribution and rates of ecosystem change are not known, yet such information is needed to understand relationships between subnival ecology and water/carbon cycles. We show that HKH subnival ecosystems cover five to 15 times the area of permanent glaciers and snow, highlighting their eco‐hydrological importance. Using satellite data from the Landsat 5, 7 and 8 missions, we measured change in the spatial extent of subnival vegetation from 1993 to 2018. The Landsat surface reflectance‐derived Normalized Difference Vegetation Index product was thresholded at 0.1 to indicate the presence/absence of vegetation. Using this product, the strength and direction of time‐series trends in the green pixel fraction were measured within three regions of interest. We controlled for cloud cover, snow cover and evaluated the impact of sensor radiometric differences between Landsat 7 and Landsat 8. Using Google Earth Engine to expedite data processing tasks, we show that there has been a weakly positive increase in the extent of subnival vegetation since 1993. Strongest and most significant trends were found in the height region of 5,000–5,500 m a.s.l. across the HKH extent: R2 = .302, Kendall's τ = 0.424, p < .05, but this varied regionally, with height, and according to the sensors included in the time series. Positive trends at lower elevations occurred on steeper slopes whilst at higher elevations, flatter areas exhibited stronger trends. We validated our findings using online photographs. Subnival ecological changes have likely impacted HKH carbon and water cycles with impacts on millions of people living downstream, but the strength and direction of impacts of vegetation expansion remain unknown.

(2019) Unmanned aerial vehicle (UAV) derived structure-frommotion photogrammetry point clouds for oil palm (Elaeisguineensis) canopy segmentation and height estimation,

Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

Quantifying the timing of vegetation phenology is critical for monitoring and modelling ecosystem responses to environmental change. Phenological processes have been studied from landscape to global scales using Earth observing satellite data, and at local scale by in situ surveys of individual plants. Now, data acquired from multi-spectral sensors on drone platforms provide flexible opportunities for monitoring phenology from individual plants to small ecosystem scales efficiently, allowing community and species level information to be derived. We captured a time-series of drone-acquired normalized difference vegetation index (NDVI) data with a multi-spectral sensor (Parrot Sequoia, (Parrot, France)) over a highly heterogeneous ecosystem in Cornwall, UK, during a period of spring green-up. We monitored NDVI trajectories at the individual crown and species' level. For deciduous crowns, we derived metrics representative of spring phenological stages: Start-of-spring (SOS), middle-ofspring green-up (MOG) and start-of-peak greenness (SOP) using a logistic function. While the exact timing of SOS, MOG and SOP appeared susceptible to understorey effects and saturation of the NDVI, relative timing of green-up for a subset of species was plausible in relation to phenological observations from an extended geographic region and in situ plant area index (PAI) measurements. In evergreen vegetation (Pinus spp.) subtle changes were also detected through the growing season. The impact of illumination differences was analysed for image pairs during leaf-off and leaf-on conditions. While significant, these effects were small (mean absolute NDVI deviation of up to 0.034 for leaf-off, 0.013 for leaf-on conditions), meaning that data captured under both constant direct and diffuse irradiance conditions can be used together and that cloudy conditions should not lead to data gaps. We conclude that the capability of drone-mounted multi-spectral instruments for spatio-temporal characterization of crown-level phenology shows great promise for improving the understanding of intra-and inter-species differences in strategy, and offers an efficient means of doing so over areas of a few hectares.

Imaging spectroscopy based methods offer unique capabilities for retrieving narrow-band vegetation indices which can be empirically related to functional traits of plants. However, in areas with complex topography, illumination effects affect the retrieval of such indices from high spatial resolution airborne or satellite data. Irradiance components at the pixel level are determined by atmospheric composition, as well as instantaneous illumination-surface-sensor geometries. An accurate pixel-wise description of direct and diffuse irradiance components is necessary to perform atmospheric corrections, finally resulting in improved surface reflectances and hence products. We assess three atmospheric