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Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery
- Source :
- Remote Sensing, Vol 13, Iss 2508, p 2508 (2021), Remote Sensing; Volume 13; Issue 13; Pages: 2508
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.
- Subjects :
- Very high resolution
Object-oriented programming
010504 meteorology & atmospheric sciences
mixed forests
very-high-resolution imagery
object-based image analysis
multiresolution segmentation
semi-automatic classification
forest mapping
Italy
Science
0211 other engineering and technologies
02 engineering and technology
01 natural sciences
Forest resource
Spatial ecology
General Earth and Planetary Sciences
Resource use
Environmental science
Segmentation
Spatial extent
Cartography
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 20724292
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....f336acda1433bb07e95fc8719a636d29