A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling.

The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods-such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys-are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.

Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model.

As one of the four most important woody oil-tree in the world, Camellia oleifera has significant economic value. Rapid and accurate acquisition of C. oleifera tree-crown information is essential for enhancing the effectiveness of C. oleifera tree management and accurately predicting fruit yield. This study is the first of its kind to explore training the ResU-Net model with UAV (unmanned aerial vehicle) images containing elevation information for automatically detecting tree crowns and estimating crown width (CW) and crown projection area (CPA) to rapidly extract tree-crown information. A Phantom 4 RTK UAV was utilized to acquire high-resolution images of the research site. Using UAV imagery, the tree crown was manually delineated. ResU-Net model's training dataset was compiled using six distinct band combinations of UAV imagery containing elevation information [RGB (red, green, and blue), RGB-CHM (canopy height model), RGB-DSM (digital surface model), EXG (excess green index), EXG-CHM, and EXG-DSM]. As a test set, images with UAV-based CW and CPA reference values were used to assess model performance. With the RGB-CHM combination, ResU-Net achieved superior performance. Individual tree-crown detection was remarkably accurate (Precision = 88.73%, Recall = 80.43%, and F1score = 84.68%). The estimated CW (R 2 = 0.9271, RMSE = 0.1282 m, rRMSE = 6.47%) and CPA (R 2 = 0.9498, RMSE = 0.2675 m2, rRMSE = 9.39%) values were highly correlated with the UAV-based reference values. The results demonstrate that the input image containing a CHM achieves more accurate crown delineation than an image containing a DSM. The accuracy and efficacy of ResU-Net in extracting C. oleifera tree-crown information have great potential for application in non-wood forests precision management.

[Temporal and spatial variation of MODIS vegetation indices in Hunan Province].

Based on MODIS images and by using the algorithm of maximum value composite (MVC), the monthly vegetation indices (VIs) in 2005 in Hunan Province were obtained. Through the analysis of the MODIS VIs, Hunan Province was divided into six districts to describe the spatial distribution of the VIs, and by using the monthly mean temperature and rainfall data collected from 5 climatic monitoring stations in this province, the temporal variation of the VIs was analyzed. The results showed that the spatial distribution of MODIS VIs was positively correlated with vegetation cover, and appeared regional characteristics. The MODIS VIs varied with season, and the curves of their monthly mean values were downwards opening quadratic parabolas, with the maximum appeared in July. The value of MODIS EVI was smaller than that of MODIS NDVI. MODIS VI was mainly affected by monthly mean temperature, but this effect was decreased with decreasing latitude. The variation pattern of MODIS EVI was more apparent than that of MODIS NDVI, i. e. , the quadratic parabola of MODIS EVI was smoother, going gradually from minimum to maximum and then going down, while that of MODIS NDVI had tiny fluctuations on both sides of the maximum point.