Assessment of the three factors affecting Myanmar’s forest cover change using Landsat and MODIS vegetation continuous fields data

Long-term observation of the earth is essential for studying the factors affecting global environmental changes.Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information.In this study,we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm.Our results show that Myanmar’s forests have declined 0.68%annually over this six-year period.We validated our derived change results and found the overall accuracy to be greater than 88%.We also assessed forest loss from protected areas,areas close to roads,and areas subject to fire,which were most likely to lose forested area.The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion,fire,and the construction of highways.This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar.

Individual tree extraction from terrestrial laser scanning data via graph pathing

Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time-consuming manual assistance and quality control.This study presents a new fully automatic approach to extract single trees from large-area TLS data.This data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest types.Results:We demonstrated the proposed method on two openly available datasets.First,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots.Second,we successfully extracted 270 trees from one hectare temperate forest.Quantitative validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,respectively.Conclusions:Our method allows automated access to individual tree level information from TLS point clouds.The proposed method is free from restricted assumptions of forest types.It is also computationally efficient with an average processing time of several seconds for one million points.It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.