Oil palm trees are important economic crops in tropical areas. Accurate knowledge of the number of oil palm trees in a plantation area is important to predict the yield of palm oil, manage the growing situation of the palm trees and maximise their productivity. In this study, we propose a novel automatic method for detection and enumeration of individual oil palm trees using images from unmanned aerial vehicles (UAVs). This method required three major steps. First, images from UAVs were classified as vegetation or non-vegetation by the support vector machine (SVM) classifier. Second, a feature descriptor based on the histogram of oriented gradient (HOG) was designed for palm trees and used to extract features for machine learning. Finally, a SVM classifier was trained and optimised using the HOG features from positive (i.e., oil palm trees) and negative samples (i.e., objects other than oil palm trees). The trained classifier was then applied to detect individual oil palm trees using adaptive moving windows that allowed it to also return the crown size of each oil palm tree. The method was trained at one site and validated independently at four other sites with different situations. The overall accuracy of palm tree detection was 99.21% at the training site and 99.39%, 99.06%, 99.90% and 94.63% at the four validation sites; the last one was for the most challenging site, in which palm trees were mixed with other trees. These tests confirm the effectiveness of the proposed method. The simplicity and great efficiency of the proposed method allow it to support oil palm tree counting for large areas using imagery from UAVs.

Chang'E‐5 is China's first lunar sample‐return mission, which will be launched in 2019. Understanding the distribution of rocks and craters in the candidate landing region is important for selecting suitable landing sites and studying the surface geology. This paper first separately investigates rock abundance and crater density in the candidate landing region, then provides a joint analysis of them, for the purposes of identifying potential hazards for safe landing and their geological implications. The results indicate that in the region, rocks are mostly concentrated around rocky ejecta craters. About 90% of the region has a rock abundance (the fractional area covered by rocks) of less than 1%. The average crater density is about 250 craters (≥ 100 m in diameter) per 100 km²; on average, 13.5% of the region is covered by craters. The surface ages of geologic units in the region estimated using crater size‐frequency distribution indicate that the eastern part of the region is younger than the western part. The joint analysis of rock abundance and crater density identifies local areas that are relatively unfavorable for safe landing. The joint analysis also indicates an exponential relationship between overall rock abundance and crater density, and a roughly linear relationship between overall rock abundance and surface age. Furthermore, the joint analysis indicates an inverse correlation between rock abundance and the relative maturation of craters. The presented research and results will be helpful for identifying suitable landing sites for the Chang'E‐5 lander. They also provide fresh insights into lunar surface geology.

Impact craters are common surface features on planetary surfaces. Their distribution offers important clues regarding geological and temporal processes on the Moon. Numerous endeavors have generated global catalogs of lunar craters; however, most of the existing catalogs only contain large craters (larger than several kilometers in diameter), and none of them offer three‐dimensional (3D) morphometric information. In this study, we first present a machine‐learning approach for automatic crater detection from digital elevation models (DEMs). Our crater detection approach can achieve a detection rate of about 85%. We also present an approach for extracting 3D morphometric information of craters based on the topography. These approaches were applied to producing a global crater data set covering the entire lunar surface that includes approximately 1.32 million craters (≥1 km). Verification was performed against previously published catalogs (Head et al., 2010, https://doi.org/10.1126/science.1195050 ; Robbins, 2019, https://doi.org/10.1029/2018je005592 ), with about 23% of our craters transferred from the previous catalogs after a rim‐fitting process. The crater catalog includes 3D morphometric data on the craters such as depths. Global analyses of craters based on this improved catalog indicate that the lunar mare and highlands have distinctive crater density differences of small craters (1–5 km). Craters of 2.5–5 km have reached saturation in several local regions in the highlands. Small craters (1–5 km) on the lunar mare are deeper than those on the highlands. The data in our comprehensive crater catalog can support various other lunar scientific studies.