Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

Lemenkova, Polina; Debeir, Olivier

doi:10.3390/jmse11040871

Cited by 18 publications

(13 citation statements)

References 110 publications

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“…These factors make EVI suitable in areas with dense vegetation such as the GGB. EVI data have been used to monitor vegetation change in other regions with dense vegetation (Adams & Garcia‐Carreras, 2023), monitor changes in Mangrove Forests in the GGB (Lemenkova & Debeir, 2023) and to measure vegetation change across African cities (Yao et al, 2019). EVI data were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite MOD13A1 V6 product at a temporal resolution of 16 days and 250 m spatial resolution from 2000 to 2020.…”

Section: Methodsmentioning

confidence: 99%

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

Nkiaka,

Okafor

2024

Hydrological Processes

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Although considerable effort has been deployed to understand the impact of climate variability and vegetation change on runoff in major basins across Africa, such studies are scarce in the Gulf of Guinea Basin (GGB). This study combines the Budyko framework and elasticity concept along with geospatial data to fill this research gap in 44 nested sub‐basins in the GGB. Annual rainfall from 1982 to 2021 show significant decreasing and increasing trends in the northern and southern parts of the GGB, respectively. Annual potential evapotranspiration (PET) also shows significant increasing trends with higher magnitudes observed in the northern parts of the GGB. Changing trends in climate variables corroborates with shift to arid and wetter conditions in the north and south, respectively. From 2000 to 2020 vegetation cover estimated using enhanced vegetation index (EVI) shows significant increasing trends in all sub‐basins including those experiencing a decline in annual rainfall. Vegetation composition measured using vegetation continuous fields (VCFs) from 2000 to 2020 show an increase in tree canopy cover (TC), a decline in short vegetation cover and marginal changes in bare ground cover (BG). Elasticity coefficients show that a 10% increase in annual rainfall and PET may lead to a 33% increase and 24% decline in runoff, respectively. On the other hand, a 10% increase in EVI may lead to a 4% decline in runoff while a 10% increase in TC, SV and BG may reduce runoff by 4% and increase runoff by 3% and 2%, respectively. Even though changes are marginal, decomposing vegetation into different parameters using EVI and VCFs may lead to different hydrological effects on runoff which is one of the novelties of this study that may be used for implementing nature‐based solutions. The study also demonstrates that freely available geospatial data together with analytical methods are a promising approach for understanding the impact of climate variability and vegetation change on hydrology in data‐scarce regions.

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Section: Methodsmentioning

confidence: 99%

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

Nkiaka,

Okafor

2024

Hydrological Processes

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“…At the same time, ANN methods and scripting libraries are promising tools for cartographic tasks and image processing for mapping areas of coastal lagoons, which are notable for the high complexity of land cover patterns and the heterogeneity of landscapes [39][40][41][42][43][44]. In this regard, GRASS GIS presents a powerful cartographic toolset that includes diverse modules that can be used for satellite image processing [45]. Hence, besides the traditional general-purpose programming languages, GRASS GIS is also used for creation.…”

Section: Research Gapmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

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“…The programming is implemented in the GRASS GIS as scripts. This automates the repeated steps of image processing to improve the workflow [101,102]. The data processing was performed on the MacBook Apple Sonoma (arm64 architecture).…”

Section: Softwarementioning

confidence: 99%

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Lemenkova

2024

Coasts

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Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.

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Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

Cited by 18 publications

References 110 publications

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

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