Results 11 to 20 of about 3,078,415 (263)

Boreal forest biomass classification with TanDEM-X [PDF]

, 2012
High spatial resolution X-band interferometric SAR data from the TanDEM-X, in the operational DEM generation mode, are sensitive to forest structure and can therefore be used for thematic boreal forest classification of forest environments.
Kostas Papathanassiou, Papathanassiou, Kostas, Torano Caicoya, Astor, Hajnsek, Irena, Florian Kugler, Astor Torano Caicoya, Kugler, Florian, Irena Hajnsek   +7 more
core   +1 more source

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

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

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

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
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   +21 more sources

Random Forest for Malware Classification

CoRR, 2016
The challenge in engaging malware activities involves the correct identification and classification of different malware variants. Various malwares incorporate code obfuscation methods that alters their code signatures effectively countering antimalware detection techniques utilizing static methods and signature database.
Felan Carlo C. Garcia, Felix P. Muga II
openaire   +2 more sources

Determination Of Stratification Boundary For Forest And Non Forest Multitemporal Classification To Support Redd In Sumatera Islan [PDF]

, 2013
Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia.
Tatik Kartika, Trisakti, Bambang, Sari, Inggit Lolita, Inggit Lolita Sari, Kartika, Tatik, Bambang Trisakti   +5 more
core   +1 more source

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Remote Sensing, 2021
Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development ...
Lin Wang, Yuzhen Zhou, Qiao Hu, Zhenghong Tang, Yufeng Ge, Adam Smith, Tala Awada, Yeyin Shi   +7 more
doaj   +1 more source