Results 11 to 20 of about 223,989 (265)

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

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

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

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

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

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

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

Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia

Remote Sensing, 2018
The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite ...
Georgy Rybakov, Jussi Peuhkurinen, Petri Latva-Käyrä, Maria Villikka, Sanna Sirparanta, Alexander Kolesnikov, Virpi Junttila, Tuomo Kauranne   +7 more
doaj   +1 more source