Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752889 | PMC |
| http://dx.doi.org/10.14358/pers.74.3.311 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Woody species diversity, structure and community distribution along environmental gradients of Seqela Dry Afromontane forest in Northwestern Ethiopia.
PLoS One
January 2025
School of Geography, Geology and the Environment, Institute for Environmental Futures, University of Leicester, Leicester, United Kingdom.
Dry evergreen Afromontane forests are severely threatened due to the expansion of agriculture and overgrazing by livestock. The objective of this study was to investigate the composition of woody species, structure, regeneration status and plant communities in Seqela forest, as well as the relationship between plant community types and environmental variables. Systematic sampling was used to collect vegetation and environmental data from 52 (20 m x 20 m) (400 m2) plots.
View Article and Find Full Text PDFEffects of a Habitat Integrity Gradient on the Diversity of Odonates in the Legal Amazonia Zone of the Brazilian State of Maranhão.
Neotrop Entomol
January 2025
Lab of Environmental Sciences and Biodiversity, Univ Estadual do Maranhão, São Luís, Maranhão, Brazil.
The diverse ecosystems of the Amazon biome play a vital role in the maintenance of biodiversity and delivering essential ecosystem services at both local and global levels. Small-bodied generalist insects, such as those from the order Odonata, contribute significantly to these services and are recognized as sensitive bioindicators of environmental quality. The present study evaluated the diversity and distribution of adult odonates in the Legal Amazonia zone of the Brazilian state of Maranhão, to identify the key environmental drivers shaping local odonate communities.
View Article and Find Full Text PDFAdvancing biomonitoring of eDNA studies with the Anaconda R package: Integrating soil and One Health perspectives in the face of evolving traditional agriculture practices.
PLoS One
January 2025
Équipe ' Sol & Végétation' (SolVeg), Institut Agronomique néo-Calédonien (IAC), Nouméa, New Caledonia.
Soil health and One Health are global concerns, necessitating the development of refined indicators for effective monitoring. In response, we present the Anaconda R Package, a novel tool designed to enhance the analysis of eDNA data for biomonitoring purposes. Employing a combination of different approaches, this package allows for a comprehensive investigation of species abundance and community composition under diverse conditions.
View Article and Find Full Text PDFStudy of spectral overlap and heterogeneity in agriculture based on soft classification techniques.
MethodsX
June 2025
Department of Biological and Pharmaceutical Environmental Sciences and Technologies, University of Campania "L. Vanvitelli", Via Antonio Vivaldi, 43, Caserta 81100, CE, Italy.
This study explores the application of fuzzy soft classification techniques combined with vegetation indices to address spectral overlap and heterogeneity in agricultural image processing. The methodology focuses on the integration of three key vegetation indices: Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Modified Chlorophyll Absorption in Reflectance Index (MCARI), with Modified Possibilistic C-Means (MPCM) clustering. The analysis involves preprocessing the image data, calculating the vegetation indices, and applying the MPCM algorithm to perform soft classification, allowing pixels to belong to multiple classes with varying degrees of membership.
View Article and Find Full Text PDFPotential plant extinctions with the loss of the Pleistocene mammoth steppe.
Nat Commun
January 2025
Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany.
During the Pleistocene-Holocene transition, the dominant mammoth steppe ecosystem across northern Eurasia vanished, in parallel with megafauna extinctions. However, plant extinction patterns are rarely detected due to lack of identifiable fossil records. Here, we introduce a method for detection of plant taxa loss at regional (extirpation) to potentially global scale (extinction) and their causes, as determined from ancient plant DNA metabarcoding in sediment cores (sedaDNA) from lakes in Siberia and Alaska over the past 28,000 years.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!