Results 11 to 20 of about 65,050 (262)

Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

Remote Sensing, 2021
Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction.
Caileigh Shoot, Hans-Erik Andersen, L. Monika Moskal, Chad Babcock, Bruce D. Cook, Douglas C. Morton   +5 more
doaj   +2 more sources

Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data

Remote Sensing, 2019
The global availability of high spatial resolution images makes mapping tree species distribution possible for better management of forest resources.
Zhuli Xie, Yaoliang Chen, Dengsheng Lu, Guiying Li, Erxue Chen   +4 more
doaj   +2 more sources

CLASSIFICATION OF TREES IN HYPERSPECTRAL CANOPY DATA USING MACHINE LEARNING: COMPARATIVE ANALYSIS OF FOREST STRUCTURE COMPLEXITY [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023
The classification of tree species by remote sensing is an important task with a broad range of applications, including forest management, environmental monitoring, and climate change studies.
F. Galdames, P. González, F. Magni-Pérez, S. M. Funk, F. Lepín, R. Saavedra, R. Saavedra, H. J. Hernández   +7 more
doaj   +1 more source

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

Remote Sensing, 2016
We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar’s Tanintharyi Region and estimated the extent of degraded forest for each unique ...
Grant Connette, Patrick Oswald, Melissa Songer, Peter Leimgruber   +3 more
doaj   +1 more source

Forests classification with the use of field guide of European Russia forest types (on the example of Karelia and Karelian isthmus) [PDF]

Вопросы лесной науки, 2018
The forests of Karelia and the Karelian Isthmus were classified by field guide of European Russia forest types, which was developed by L. B. Zaugolnova and V.B Martynenko. The studied forests were classified into five main sections: lichenous, green moss,
A.V. Gornov
doaj   +1 more source

Development of the spectral forest index in the Khangai region, Mongolia using Sentinel-2 imagery

Forest Science and Technology, 2023
Mongolian forests have low productivity and growth and are vulnerable to disturbances. Additionally, it is difficult to control and evaluate the forested areas. Therefore, satellite data and surveillance methods are needed to study mountain forests. This
Bayanmunkh Norovsuren, Batchuluun Tseveen, Tsolmon Renchin, Enkhjargal Natsagdorj   +3 more
doaj   +1 more source

Phylogenetic classification of the world’s tropical forests [PDF]

Proceedings of the National Academy of Sciences, 2018
Significance Identifying and explaining regional differences in tropical forest dynamics, structure, diversity, and composition are critical for anticipating region-specific responses to global environmental change. Floristic classifications are of fundamental importance for these efforts. Here we provide a global tropical forest
Slik, J W F, Franklin, J, Arroyo-Rodriguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, S I, Alves, L F, Anitha, K, Avella, A, Mora, F, Aymard, G A, Baez, S, Balvanera, P, Bastian, M L, Bastin, J F, Bellingham, P J, van den Berg, E, Bispo, P D, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, F Q, Brown, Sandra, Chai, S L, Chazdon, Robin L, Chen, S B, Chhang, P, Chuyong, G, Ewango, C, Coronado, I M, Cristobal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, H S, Davidar, P, DeWalt, S J, Din, H, Drake, D R, Duque, A, Durigan, G, Eichhorn, K, Eler, E S, Enoki, T, Ensslin, A, Fandohan, A B, Farwig, N, Feeley, K J, Fischer, M, Forshed, O, Garcia, Q S, Garkoti, S C, Gillespie, T, Gillet, J F, Gonmadje, C, Granzow-de la Cerda, I, Griffith, D M, Grogan, James, Hakeem, K R, Harris, D J, Harrison, R D, Hector, A, Hemp, A, Homeier, J, Hussain, M S, Ibarra-Manriiquez, G, Hanum, I F, Imai, N, Jansen, P A, Joly, C A, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, D L, Kessler, M, Killeen, T J, Kooyman, R M, Laumonier, Y, Laurance, S G, Laurance, W F, Lawes, M J, Letcher, S G, Lindsell, J, Lovett, J, Lozada, J, Lu, X H, Lykke, A M, Bin Mahmud, K, Mahayani, N P D, Mansor, A, Marshall, Andrew R, Martin, E H, Matos, D C L, Meave, J A, Melo, F P L, Mendoza, Z H A, Metali, F, Medjibe, V P, Metzger, J P, Metzker, T, Mohandass, D, Munguia-Rosas, M A, Munoz, R, Nurtjahy, E, de Oliveira, E L, Onrizal, Parolin, P, Parren, M, Parthasarathy, N, Paudel, E, Perez, R, Perez-Garcia, E A, Pommer, U, Poorter, L, Qie, L, Piedade, M T F, Pinto, J R R, Poulsen, A D, Poulsen, J R, Powers, J S, Prasad, R C, Puyravaud, J P, Rangel, O, Reitsma, J, Rocha, D S B, Rolim, S, Rovero, F, Rozak, A, Ruokolainen, K, Rutishauser, E, Rutten, G, Said, M N M, Saiter, F Z, Saner, P, Santos, B, dos Santos, J R, Sarker, S K, Schmitt, C B, Schoengart, J, Schulze, M, Sheil, D, Sist, P, Souza, A F, Spironello, W R, Sposito, T, Steinmetz, R, Stevart, T, Suganuma, M S, Sukri, R, Sultana, A, Sukumar, R, Sunderland, T, Supriyadi, Suresh, H S, Suzuki, E, Tabarelli, M, Tang, J W, Tanner, E V J, Targhetta, N, Theilade, I, Van Do, T, Van Sam, H, Vandermeer, J H, Verbeeck, H, Vetaas, O R, Adekunle, V, Vieira, S A, Webb, C O, Webb, E L, Whitfeld, T, Wich, S, Williams, J, Wiser, S, Wittmann, F, Yang, X B, Yao, C Y A, Yap, S L, Zahawi, R A, Zakaria, R, Zang, R G, Thomas, D, Van Valkenburg, J, Van Do, Tran, Van Sam, Hoang, Vandermeer, John H, Verbeeck, Hans, Vetaas, Ole Reidar, Adekunle, Victor, Vieira, Simone A, Webb, Campbell O, Webb, Edward L, Whitfield, Timothy, Wich, Serge, Williams, John, Wiser, Susan, Wittmann, Florian, Yang, Xiaobo, Zahawi, Rakan A, Zang, Runguo   +201 more
openaire   +19 more sources

Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method

, 2022
The accurate and effective estimation of forest carbon density is an essential basis for effectively responding to climate change and achieving the goal of carbon neutrality. Aiming at the problem of the significant differences in the forest carbon model
Gui Zhang, Xin Wu, Ziheng Pang, Sanqing Tan, Zhigao Yang   +4 more
core   +1 more source

RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY

Ecology, 2007
Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a ...
Cutler, D. R., Edwards, Thomas C., Jr., Beard, Karen H., Cutler, A., Hess, K. T., Gibson, J. C., Lawler, J. J.   +6 more
openaire   +4 more sources

Randomized Clustering Forests for Image Classification [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Some of the most effective recent methods for content-based image classification work by quantizing image descriptors, and accumulating histograms of the resulting visual word codes. Large numbers of descriptors and large codebooks are required for good results and this becomes slow using k-means.
Moosmann, Frank, Nowak, Eric, Jurie, Frédéric   +2 more
openaire   +6 more sources