This study was conducted to reduce the bias associated with using ICESat-2 data in canopy height estimation and mapping by integrating the features of Sentinel-1&2. The parameters derived from Sentinel-1 and Sentinel-2 images were used for the canopy height modeling through the gradient boosting regression model. The ICESat-2-based canopy height served as the training data while the ALS data was used for validating the accuracy of the predicted canopy height product. The result showed a low correlation between the ICESat-2 and ALS-based canopy height. Nevertheless, the accuracy of the predicted canopy height was increased, from 14.08m to 13.89m based on RMSE and from 55.62% to 54.86% based on PRMSE, when the features of Sentinel-1 was integrated with the features of Sentinel-2. Further, low canopy height values tend to be overpredicted while high canopy height tend to be underpredicted. Thus, canopy height correction models can be developed to augment issues of low accuracy associated with canopy height modeling using ICESat-2.

Forest resources inventory and monitoring are essential for efficient management of natural resources. Remote sensing-based mapping technology provides geospatially explicit information on forest ecosystems. Generally, the attributes of forests derived from moderate satellite images show spatial and classification inaccuracy, introducing uncertainty in formulating forest dynamics and misleading inappropriate management plans. This study examines the efficiency of low-cost UAVs in generating orthoimages for differentiating bamboo from broadleaf species in forest succession. The results show that the generated very high-resolution orthoimage can reveal detailed spatial features of both bamboo and tree crowns, providing an excellent opportunity to differentiate individual bamboo crowns from trees and therefore help derive bamboo expansion and degradation over the forest.

k nearest neighbor (kNN) algorithm, a non-parametric and one of the machine learning techniques had been popularly used for stand-level AGB estimation. kNN algorithm has two distinct parameters which are concerning the distance and the number of nearest neighbors (k) and the distance of target plots with reference plots. This study was conducted to assess the effects of the number of k, reference plots density, and distance of target plots with reference plots on the performance of the kNN algorithm in estimating stand-level AGB information. Using the surface reflectance of the Sentinel-2 image, the effects of the number of k (3, 5, 8, 11), reference plots density (high=3932 pixels, low=656 pixels), and the distance of the target and reference plots (2km, 5km, and 10km) to the prediction error of the kNN algorithm were assessed. The performance of the kNN was assessed using the RMSE and RMSPE as evaluation criteria. The 3-way factorial ANOVA was used to determine the main and interaction effects of the variables. The results showed that the number of k, reference plot density, and the distance of target plots with reference plots have a significant effect on the prediction error of the kNN algorithm. The PRMSE increases with the increasing distance and decreased with the increased number of k. Further, the PRMSE was low when the reference plot density was high. The PRMSE can be improved by as much as 13% when the reference plot density is sufficient and when an optimal number of k and distance are used for the parameters.

Global warming is becoming more and more obvious hastening the effect of climate change and at the same time affecting the growth of plants. Under global warming, the growth of vegetation is affected by climatic factors such as temperature and rainfall, which have also led to changes in the structure of the timberline and its distribution along the altitudes. In order to observe the trend of timberline in Taiwan under global warming, this study focused on Yushan peaks as study site, which represents the Taiwan's alpine ecosystem, using multi-temporal Landsat 8 satellite images that were acquired in 2013, 2016 and 2019 and meteorological data. The satellite images were atmospherically corrected using the FLAASH and pansharpened using the NNDiffuse technique. The different land cover in Yushan namely forest, low vegetation, bareland, and river were classified using the SVM supervised classification. The changes in the distribution of trees in different periods with the changes in the meteorological data were identified, classified, analysed and compared. The results of the study show that the current impact of global warming to climate change and on Taiwan's timberline trend is caused mainly by temperature. The rising temperature overtime had caused the timberline to shift towards the higher altitudes.

Agricultural crops monitoring covering large-scale areas with high-efficiency is of great importance in sustainable agriculture management to combat the demand for agricultural crops brought by the exponential growth of population worldwide. The study was conducted to assess the promising potential of segmenting cloud free and high-resolution images for watermelon fruit monitoring using the combination of Unmanned Aerial Vehicle (UAV) orthophotographs and Support Vector Machine (SVM) supervised machine learning model. The digital images of the watermelon field were captured using UAV DJI Mavic 2 Pro through Pix4D operation and were orthorectified to generate an orthophotograph of the study site using the Agisoft Metashape software. After the segmentation using the edge segmentation and full lambda algorithm, the segmented objects were classified using the SVM technique with 150 and 100 segmented blocks as training samples representing the fruit of the watermelon and the non-fruit, respectively. The result revealed that use of high-resolution digital imagery and the application of machine learning techniques could be a tool to a wide area accounting of agricultural crops as indicated by the overall classification accuracy of 91% and a Kappa coefficient of 0.79.