UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application

Abstract

The purpose of this study is to develop an unmanned aerial vehicle (UAV)-based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to quantify six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass-shrub ecosystem. The drone-based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line-point intercept methods) in the spring and summer of 2017. The dronebased estimates showed strong agreements with in situ measurements in cases where deciduous vegetation (mesquite) had leaves with R 2 for bare soil gap size and vegetation height of 0.97 and 0.89 in the summer, respectively. The RMSE of bare soil gap size and vegetation height are 0.2 m and 6.72 cm in the summer, respectively. Based on these results, we found that drone-based remote sensing proved to be an efficient and highly accurate method that serves as a complement to field measurements for rangeland indicator estimation. We discussed the possible applications of drone-based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non-stationarity, and to detect landscape anisotropy.

Vegetation Cover Estimation in Semi-Arid Shrublands after Prescribed Burning: Field-Ground and Drone Image Comparison

“…Overall, the results of our study show a strong correlation (R 2 predictive = 0.817; RMSE = 9.89%, Figure 2) between vegetation cover values estimated from field measurements and those estimated from drone data. This finding is in line with those from studies in shrublands in other ecosystems [3,[49][50][51], reinforcing the use of the drone method as a complementary, and less costly method of estimating the attributes of the vegetation, since it provides reasonably accurate measurement of vegetation structural attributes [21]. Nonetheless, caution should be exercised when using vegetation cover estimates from drone-derived data, since we observed how the accuracy of the estimates varied depending on the vegetation coverage (Figures 3b and 4).…”

“…Drone imagery has been promoted as an alternative to replace arduous and costly field work in remote areas and in adverse weather conditions; however, for this to be possible, there must be a high concordance between results from field data and those obtained with drones [3]. In light of our results, it would be interesting to adapt the cover estimation method (drone-based vs. field measurement-based) to management objectives, and the types of shrubland in our study area.…”

“…Likewise, to evaluate the effect of a prescribed burn, it may be interesting to see how reliable drones are in estimating bare ground, since after a disturbance such as prescribed burns, an increase in the percentage of bare ground is expected. Provided that we are aware of their possible limitations, multispectral images obtained with drones can play a key and complementary role in vegetation monitoring, as some studies have indicated [3,20], particularly in areas with high heterogeneity, such as semi-arid shrublands.…”

“…Vegetation cover, which can be defined as the percentage of ground area covered by green vegetation, is a widely used parameter to describe the amount of vegetation in a given area [1]. The estimation of this parameter is essential in the monitoring and management of arid and semi-arid regions [2,3], due, among other reasons, to its importance in key biophysical processes such as evaporation, transpiration, and photosynthesis [4]. Likewise, it is one of the parameters studied to evaluate vegetation recovery after disturbances, such as fires.…”

A Hybrid Clustering Method with a Filter Feature Selection for Hyperspectral Image Classification

“…One hundred and seventy bands were selected. The study area is in the northern part of San Ysidro city, which contains tree nuts, pepper, grains, and corn [ 35 , 36 ]. The baseline map was made by the high-resolution classification result of the National Agriculture Imagery Program (NAIP) on 25 July 2011.…”