Volume 13, Issue 2 (10-2025)
Ecol Iran For 2025, 13(2): 91-101 |
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Bihamta Toosi N, Soffianian A, Fakheran S. (2025). The Application of Landscape Ecology in Monitoring Changes in the Spatial Patterns of Mangrove Ecosystems
(Case Study: Qeshm Mangrove Forests. Ecol Iran For. 13(2), 91-101. doi:10.61882/ifej.2025.575
URL: http://ifej.sanru.ac.ir/article-1-575-en.html
URL: http://ifej.sanru.ac.ir/article-1-575-en.html
1- Faculty of Natural Resources, Sari University of Agriculture and Natural Resources, Sari, Iran
2- Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran
2- Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran
Abstract: (881 Views)
Extended Abstract
Background: Mangrove forests, one of the most important natural habitats, provide many ecosystem services for humans and other living organisms. In recent years, climate change and the development of human activities on the coasts have decreased the area of these forests. The knowledge-based management of forest lands, as a solution to reduce the factors of destruction and create a balance between development and the environment, requires a detailed assessment of changes in the landscape structure in the form of spatial and temporal patterns. This research aims to monitor changes in the spatial patterns of the mangrove forest ecosystem using the landscape ecology approach and remote sensing techniques.
Methods: Landsat TM and OLI satellite images for the years 1985, 2002, 2016, and 2022 were used to prepare land cover maps and assess the state of the mangrove forest ecosystem, considering the changes and programs implemented for the protection and development of human activities. The random forest method was used in the R software to generate land cover maps with four layers: mangrove lands, mudflat areas, tidal lands, and water areas, after preparing educational samples. After classification, the accuracy assessment of the generated map was examined using data collected from field surveys and Google Earth imagery. These maps are applied as base maps to identify and analyze spatial patterns at the class and landscape levels. Using FRAGSTATS software, four measures of class area (CA), percentage of each class (PLAND), spot density (PD) and nearest neighbor distance (ENN) at the class level and four measures of spot number (NP), contiguity (CONTAG), fragmentation (SPLIT), and the largest spot index (LPI) were used to analyze and monitor changes in spatial patterns in the studied period.
Results: The accuracy assessment results indicated that the four maps obtained had an acceptable accuracy ranging from 86 to 88 %. The class area metric (CA) results indicated that the area of mangrove forests decreased from 1985 to 2002, increased from 2002 to 2016, and then decreased again from 2016 to 2022. One of the primary reasons for the decline in mangrove forests between 1985 and 2016 was mangrove wood harvest by villagers for charcoal and fuel. Efforts to restore mangrove forests have been initiated since 2006, and in some regions, seedlings have been manually planted over the past few years. As mangrove areas have diminished, mudflats and tidal lands have increased in their place. The PLAND index results indicated that the abundance of mangroves and mudflats increased while the number of water bodies decreased between 1985 and 2012.
The PD index has increased for forest, swamp, and tidal lands. The increase in the density of forest patches in this area is caused by new vegetation areas and fragmentation of parts near the coast due to sedimentation and rising water levels. The ENN index has been decreasing for mangrove forest lands and tidal lands, and increasing for swamp lands. The decrease in Euclidean nearest neighbor distance (ENN) indicates the destruction of mangrove lands, less dispersion of healthy mangrove lands, and uneven distribution of mangrove patches in the coastal area. The analysis of landscape-level patch numbers indicated a decline in mangrove patches from 1985 to 2002, largely due to their use as fuel and for livestock grazing. The CONTAG metric showed an overall decreasing trend from 1985 to 2022, with a brief increase occurring between 1982 and 2016. The largest spot index (LPI) decreased and the fragmentation index (SPLIT) increased from 1985 to 2022.
Conclusion: Coastal areas are home to vital ecosystems that provide numerous environmental, economic, and social benefits. However, the rapid development of ports and human activities on the Iranian coasts to achieve economic benefits, without considering conservation planning and monitoring the state of coastal ecosystems, can pose a serious threat to the health of these coastal ecosystems in the long term. One of the most important consequences of the destruction of coastal ecosystems is the reduction of biodiversity. Based on the obtained results, it is evident that, even though mangrove forest protection and development programs, such as seedling planting, have been implemented since 2005, human activities in coastal areas have led to the destruction and fragmentation of mangrove forest lands. Given the critical importance of these forests, protection planning and management should be conducted with increased supervision and precision to prevent further destruction of these coastal areas. The findings of this research can aid in organizing and planning the protection of mangrove ecosystems in southern Iran.
Background: Mangrove forests, one of the most important natural habitats, provide many ecosystem services for humans and other living organisms. In recent years, climate change and the development of human activities on the coasts have decreased the area of these forests. The knowledge-based management of forest lands, as a solution to reduce the factors of destruction and create a balance between development and the environment, requires a detailed assessment of changes in the landscape structure in the form of spatial and temporal patterns. This research aims to monitor changes in the spatial patterns of the mangrove forest ecosystem using the landscape ecology approach and remote sensing techniques.
Methods: Landsat TM and OLI satellite images for the years 1985, 2002, 2016, and 2022 were used to prepare land cover maps and assess the state of the mangrove forest ecosystem, considering the changes and programs implemented for the protection and development of human activities. The random forest method was used in the R software to generate land cover maps with four layers: mangrove lands, mudflat areas, tidal lands, and water areas, after preparing educational samples. After classification, the accuracy assessment of the generated map was examined using data collected from field surveys and Google Earth imagery. These maps are applied as base maps to identify and analyze spatial patterns at the class and landscape levels. Using FRAGSTATS software, four measures of class area (CA), percentage of each class (PLAND), spot density (PD) and nearest neighbor distance (ENN) at the class level and four measures of spot number (NP), contiguity (CONTAG), fragmentation (SPLIT), and the largest spot index (LPI) were used to analyze and monitor changes in spatial patterns in the studied period.
Results: The accuracy assessment results indicated that the four maps obtained had an acceptable accuracy ranging from 86 to 88 %. The class area metric (CA) results indicated that the area of mangrove forests decreased from 1985 to 2002, increased from 2002 to 2016, and then decreased again from 2016 to 2022. One of the primary reasons for the decline in mangrove forests between 1985 and 2016 was mangrove wood harvest by villagers for charcoal and fuel. Efforts to restore mangrove forests have been initiated since 2006, and in some regions, seedlings have been manually planted over the past few years. As mangrove areas have diminished, mudflats and tidal lands have increased in their place. The PLAND index results indicated that the abundance of mangroves and mudflats increased while the number of water bodies decreased between 1985 and 2012.
The PD index has increased for forest, swamp, and tidal lands. The increase in the density of forest patches in this area is caused by new vegetation areas and fragmentation of parts near the coast due to sedimentation and rising water levels. The ENN index has been decreasing for mangrove forest lands and tidal lands, and increasing for swamp lands. The decrease in Euclidean nearest neighbor distance (ENN) indicates the destruction of mangrove lands, less dispersion of healthy mangrove lands, and uneven distribution of mangrove patches in the coastal area. The analysis of landscape-level patch numbers indicated a decline in mangrove patches from 1985 to 2002, largely due to their use as fuel and for livestock grazing. The CONTAG metric showed an overall decreasing trend from 1985 to 2022, with a brief increase occurring between 1982 and 2016. The largest spot index (LPI) decreased and the fragmentation index (SPLIT) increased from 1985 to 2022.
Conclusion: Coastal areas are home to vital ecosystems that provide numerous environmental, economic, and social benefits. However, the rapid development of ports and human activities on the Iranian coasts to achieve economic benefits, without considering conservation planning and monitoring the state of coastal ecosystems, can pose a serious threat to the health of these coastal ecosystems in the long term. One of the most important consequences of the destruction of coastal ecosystems is the reduction of biodiversity. Based on the obtained results, it is evident that, even though mangrove forest protection and development programs, such as seedling planting, have been implemented since 2005, human activities in coastal areas have led to the destruction and fragmentation of mangrove forest lands. Given the critical importance of these forests, protection planning and management should be conducted with increased supervision and precision to prevent further destruction of these coastal areas. The findings of this research can aid in organizing and planning the protection of mangrove ecosystems in southern Iran.
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