Unmanned aerial vehicle images in the machine learning for agave detection.

In this study, six supervised classification algorithms were compared. The algorithms were based on cluster analysis, distance, deep learning, and object-based image analysis. Our objective was to determine which of these algorithms has the highest overall accuracy in both detection and automated estimation of agave cover in a given area to help growers manage their plantations. An orthomosaic with a spatial resolution of 2.5 cm was derived from 300 images obtained with a DJI Inspire 1 unmanned aerial system. Two training classes were defined: (1) sites where the presence of agaves was identified and (2) "absence" where there were no agaves but other plants were present. The object-oriented algorithm was found to have the highest overall accuracy (0.963), followed by the support-vector machine with 0.928 accuracy and the neural network with 0.914. The algorithms with statistical criteria for classification were the least accurate: Mahalanobis distance = 0.752 accuracy and minimum distance = 0.421. We further recommend that the object-oriented algorithm be used, because in addition to having the highest overall accuracy for the image segmentation process, it yields parameters that are useful for estimating the coverage area, size, and shapes, which can aid in better selection of agave individuals for harvest.

Waterhole detection using a vegetation index in desert bighorn sheep (Ovis canadensis cremnobates) habitat.

In arid ecosystems, desert bighorn sheep are dependent on natural waterholes, particularly in summer when forage is scarce and environmental temperatures are high. To detect waterholes in Sierra Santa Isabel, which is the largest area of desert bighorn sheep habitat in the state of Baja California, Mexico, we used the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) from Sentinel-2 satellite images. Waterhole detection was based on the premise that sites with greater water availability, where NDVI was higher, can be identified by their density of vegetation greenness. For the detected waterholes, we estimated the escape terrain (presence of cliffs or steep, rocky slopes) around each by the vector ruggedness measure to determine their potential use by desert bighorn sheep based on the animals' presence as documented by camera traps. We detected 14 waterholes with the NDVI of which 11 were known by land owners and 3 were unrecorded. Desert bighorn were not detected in waterholes with high values of escape terrain, i.e., flat areas. Waterhole detection by NDVI is a simple method, and with the assistance and knowledge of the inhabitants of the Sierra, it was possible to confirm the presence each waterhole in the field.