The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome.

Downscaling Chlorophyll-a (Chl-a) concentration derived from satellite image is crucial for refined applications such as water quality monitoring. However, the precision of downscaling is usually constrained by various environmental and geophysical factors. In this paper, we develop a downscaling method for Chl-a concentration to improve precision, especially for inland lakes with different nutrient status and surrounding environment. The method downscales the Sentinel-3 Chl-a concentration from 300 m to 30 m, based on an integration of the multivariate analysis (MVA) and the gradient boosting decision tree (GBDT) model. Firstly, we analyzed 21 Chl-a concentration related indices derived from Landsat-8 TIRS, Sentinel-1 SAR, and Sentinel-2 MSI images, to identify optimal factors for Chl-a concentration variability. Secondly, a GBDT regression model integrated the optimal factors and Sentinel-3 Chl-a concentrations at coarse resolution, is constructed to convey the nonlinear relations between them. Finally, fine-resolution Chl-a concentrations were produced by employing the GBDT regression model to auxiliary factors at fine scale for 12 large inland lakes across China. The results indicated that the proposed MVA-GBDT method effectively inferred the spatial variability of Chl-a concentration with a mean RMSE of 4.505 mg/m 3 , an improvement of 5% ~ 39% over other downscaling methods. Furthermore, for lakes with large water quality heterogeneity, the method led to a cross validation RMSE and a difference in accuracy of 5.371 mg/m 3 and 0.866 mg/m 3 , respectively. In addition, this study examined the significance of the auxiliary factors and found that the NDCI (normalized difference chlorophyll index) and WST (water surface temperature) were the two most important factors for MVA-GBDT to detect coarse spatial resolution of Chl-a concentration, particularly for NDCI in lakes with high nutrient contrasts. These findings contribute to the generation of fine-scale Chl-a concentrations in lakes and support related applications.