The most important part in forest inventory based on remote sensing data is individual tree identification, because only when the tree is identified, we can try to determine its characteristic features. The objective of research is to explore remote sensing methods to determine individual tree position using LIDAR and digital aerial photography in Latvian forest conditions. The study site is a forest in the middle of Latvia at Jelgava district (56º39' N, 23º47' E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. A LIDAR data is 1.4 to 9 p/m2 depending on the altitude. Image data is RGB (Red, Green, and Blue), NIR (Near Infrared) and PAN (Panchromatic) spectrum with 20 to 50 cm pixel resolution depending on the altitude. Image processing was made using Fourier transform and RGB colour segmentation. LIDAR data are processed with DBSCAN algorithm, global maximum algorithm, and local maximum algorithm. Field measurement's parameters were tree coordinates, species, height, diameter at breast height, crown width, and length. Best results on both ALS and ADS data were achieved using local maximum methods.
Organizations from different fields show more and more interest towards an effective solution, which would allow integrating and combining services and products from different providers into the single mobile or smartcard application for easy and comfortable usage by clients. For reaching these demands the need for certain service emerges, which would allow transformation of developer's knowledge to the technological solution in a form of application. We propose to solve these issues with smartlets, role distribution and integrated payment pool for business services. Proposed integrated payment pool was used to design Norvik Bank A-card product in Latvia where multiple payment applications were integrated into single smartcard.
Precision of the forest inventory still is one of the most important problems in the forestry nowadays. The aim of this research was to estimate the results of the combined forest inventory (CFI), using high spatial resolution aerial images in the planned areas of clear-cuts, comparing the results with the calipering and production files of harvesters. Testing of algorithms showed considerable difference in results between the CFI, forest inventory data and harvester production data. CFI results and production data had a close correlation with R 2 =0.83. Comparing CFI calculated growing stock with production data, the average relative error amounted to 10.7%, which means the possibility for integration of these results into the forest inventory system. Comparing to CFI, there is a weak correlation between forest inventory and production data with R 2 =0.34. The results indicate that LiDAR CFI technology can be used in the forecasting of the forest management, offering precise information about potential amount and economic value of assortments.
Two technologies-LiDAR (Light Detection and Ranging) and aero photography-have a huge potential in forest taxation, which is a process of gathering different parameters of specific region. Both technologies can be used for finding different parameters of interest, such as number of trees, tree height and other. This paper presents the results of usage analysis for LiDAR and aero photography, and describes their possibilities. Also it contains an analysis of tree identifying algorithms and describes ways of their usage in different processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.