scholarly journals Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data

2017 ◽  
Vol 9 (5) ◽  
pp. 400 ◽  
Author(s):  
Kaja Kandare ◽  
Michele Dalponte ◽  
Hans Ørka ◽  
Lorenzo Frizzera ◽  
Erik Næsset
Keyword(s):  
Specific Volume ◽  
Laser Scanning ◽  
Hyperspectral Data ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Species Specific

scholarly journals Large-area inventory of species composition using airborne laser scanning and hyperspectral data

Silva Fennica ◽  
2021 ◽  
Vol 55 (4) ◽  
Author(s):  
Hans Ørka ◽  
Endre Hansen ◽  
Michele Dalponte ◽  
Terje Gobakken ◽  
Erik Næsset
Keyword(s):  
Forest Management ◽  
Species Composition ◽  
Laser Scanning ◽  
Hyperspectral Data ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Small Scale ◽  
Large Area ◽  
Airborne Laser ◽  
Species Specific

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m collected over 350 km of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.22Picea abiesPinus sylvestris

Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs

2008 ◽  
Vol 38 (7) ◽  
pp. 1750-1760 ◽  
Author(s):  
Petteri Packalén ◽  
Matti Maltamo
Keyword(s):  
Weibull Distribution ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Aerial Photographs ◽  
Diameter Distribution ◽  
Tree Level ◽  
Nearest Neighbour ◽  
Airborne Laser ◽  
Species Specific ◽  
Diameter Distributions

The use of diameter distributions originates from a need for tree-level description of forest stands, which is required, for example, in growth simulators and bucking. Diameter distribution models are usually applied, since measuring empirical diameter distributions in practical forest inventories is too laborious. This study investigated the ability of remote sensing information to predict species-specific diameter distributions. The study was carried out in Finland in a typical managed boreal forest area. The tree species considered were Scots pine ( Pinus sylvestris L.), Norway spruce ( Picea abies (L.) Karst.), and deciduous trees as a group. Growing stock was estimated using the k-MSN method using airborne laser scanning data and aerial photographs. Two approaches were compared: first, the nearest neighbour approach based on field measured trees was used as such to predict diameter distribution, and second, a theoretical diameter distribution approach in which the parameters of the Weibull distribution are predicted using the k-MSN estimates was applied. Basically, all test criteria indicated that the diameter distribution based on nearest neighbour imputed trees outperforms the Weibull distribution, but care must be taken to ensure that the modelling data are comprehensive enough.

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