A comprehensive review of tree cover mapping using satellite sensor data
Abstract Trees serve manifold ecosystem functions including climate change mitigation, biodiversity conservation, landscape restoration etc. yet are facing threats globally due to human intervention. As a result, effective conservation initiatives require quantifying both present and past extents of...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
|
| Series: | Discover Geoscience |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44288-025-00201-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849344645560008704 |
|---|---|
| author | Md. Shamim Reza Saimun M. Mahmudur Rahman |
| author_facet | Md. Shamim Reza Saimun M. Mahmudur Rahman |
| author_sort | Md. Shamim Reza Saimun |
| collection | DOAJ |
| description | Abstract Trees serve manifold ecosystem functions including climate change mitigation, biodiversity conservation, landscape restoration etc. yet are facing threats globally due to human intervention. As a result, effective conservation initiatives require quantifying both present and past extents of the tree cover. Remote sensing technologies coupled with machine learning techniques appear to be effective in mapping and monitoring tree cover for the past few decades and offering advantages over traditional approaches. Despite extensive research on vegetation, mangroves, forest health, and urban forests, a comprehensive review focusing solely on remote sensing’s role in tree cover mapping is lacking. This review aims to fill that gap by providing an overview of the studies that mapped tree cover using remote sensing, and discusses spatial context, satellite sensors, classification approaches utilized for mapping tree cover. Literature search using Google Scholar showed that such studies are prevalent in every continent to major climatic domain. From coarse to high resolution satellite data (e.g., Landsat, Sentinel, MODIS, Worldview, ALOS PALSAR etc.) are used independently or with an integration depending on the purpose, availability, and economical feasibility. While Landsat has gained more popularity due to its historical record and free availability, it faces limitations in identifying small fragments of tree cover. A wide range of tree cover mapping methodologies are available, and can be classified into pixel-based or object-based to supervised or unsupervised classification approaches which include machine learning techniques, such as Support Vector Machine (SVM), Decision Tree (DT), Nearest Neighbour (NN), Maximum Likelihood (ML), Artificial Neural Network (ANN), Ensemble etc. However, challenges exist in mapping tree cover using remote sensing. Future research should focus on improving classification performance by leveraging multi-source, high-resolution, multi-temporal, and multi-sensor data, embracing the evolving capabilities of remote sensing technologies along with artificial intelligence to enhance accuracy and ensure reliability in tree cover mapping. |
| format | Article |
| id | doaj-art-6e46fdfa46a5455795a5657253e27091 |
| institution | Kabale University |
| issn | 2948-1589 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Geoscience |
| spelling | doaj-art-6e46fdfa46a5455795a5657253e270912025-08-20T03:42:37ZengSpringerDiscover Geoscience2948-15892025-08-013112310.1007/s44288-025-00201-xA comprehensive review of tree cover mapping using satellite sensor dataMd. Shamim Reza Saimun0M. Mahmudur Rahman1Department of Forestry and Environmental Science, School of Agriculture and Mineral Sciences, Shahjalal University of Science and TechnologyBangladesh Space Research and Remote Sensing Organization (SPARRSO)Abstract Trees serve manifold ecosystem functions including climate change mitigation, biodiversity conservation, landscape restoration etc. yet are facing threats globally due to human intervention. As a result, effective conservation initiatives require quantifying both present and past extents of the tree cover. Remote sensing technologies coupled with machine learning techniques appear to be effective in mapping and monitoring tree cover for the past few decades and offering advantages over traditional approaches. Despite extensive research on vegetation, mangroves, forest health, and urban forests, a comprehensive review focusing solely on remote sensing’s role in tree cover mapping is lacking. This review aims to fill that gap by providing an overview of the studies that mapped tree cover using remote sensing, and discusses spatial context, satellite sensors, classification approaches utilized for mapping tree cover. Literature search using Google Scholar showed that such studies are prevalent in every continent to major climatic domain. From coarse to high resolution satellite data (e.g., Landsat, Sentinel, MODIS, Worldview, ALOS PALSAR etc.) are used independently or with an integration depending on the purpose, availability, and economical feasibility. While Landsat has gained more popularity due to its historical record and free availability, it faces limitations in identifying small fragments of tree cover. A wide range of tree cover mapping methodologies are available, and can be classified into pixel-based or object-based to supervised or unsupervised classification approaches which include machine learning techniques, such as Support Vector Machine (SVM), Decision Tree (DT), Nearest Neighbour (NN), Maximum Likelihood (ML), Artificial Neural Network (ANN), Ensemble etc. However, challenges exist in mapping tree cover using remote sensing. Future research should focus on improving classification performance by leveraging multi-source, high-resolution, multi-temporal, and multi-sensor data, embracing the evolving capabilities of remote sensing technologies along with artificial intelligence to enhance accuracy and ensure reliability in tree cover mapping.https://doi.org/10.1007/s44288-025-00201-xTree cover mappingSatellite sensorRemote sensingClassification |
| spellingShingle | Md. Shamim Reza Saimun M. Mahmudur Rahman A comprehensive review of tree cover mapping using satellite sensor data Discover Geoscience Tree cover mapping Satellite sensor Remote sensing Classification |
| title | A comprehensive review of tree cover mapping using satellite sensor data |
| title_full | A comprehensive review of tree cover mapping using satellite sensor data |
| title_fullStr | A comprehensive review of tree cover mapping using satellite sensor data |
| title_full_unstemmed | A comprehensive review of tree cover mapping using satellite sensor data |
| title_short | A comprehensive review of tree cover mapping using satellite sensor data |
| title_sort | comprehensive review of tree cover mapping using satellite sensor data |
| topic | Tree cover mapping Satellite sensor Remote sensing Classification |
| url | https://doi.org/10.1007/s44288-025-00201-x |
| work_keys_str_mv | AT mdshamimrezasaimun acomprehensivereviewoftreecovermappingusingsatellitesensordata AT mmahmudurrahman acomprehensivereviewoftreecovermappingusingsatellitesensordata AT mdshamimrezasaimun comprehensivereviewoftreecovermappingusingsatellitesensordata AT mmahmudurrahman comprehensivereviewoftreecovermappingusingsatellitesensordata |