Coastal Wetlands: Alteration and Remediation (original) (raw)
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2015
The Lower Parana River floodplain wetlands are dominated by herbaceous communities. Dominant macrophyte species have been classified in Plant Functional Types, summarizing their main structural and functional features and their expected responses to the environment. In a previous work, a polarimetric RADARSAT-2 C-Band scene was classified with an unsupervised per-pixel approach on the coherence matrix (a progressive Wishart H/ classifier), but a relatively low global accuracy (58.2%) and Kappa index (50.4%) were obtained. In this work, we assessed a supervised object-based Random Forests classifier on the same scene. Based in previous works in other areas, we expected a higher accuracy for the Random Forests classifier than for the Wishart one. However, we obtained a even lower global accuracy (55.2%) and Kappa index (40.6%). Also, most of the areas were assigned to Plant Functional Type A (corresponding to bulrush marshes). We compared the classifiers and discuss possible reasons f...
The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands
Remote Sensing
Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test resu...
Random Forest Algorithm for Land Cover Classification
2016
Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest ...
In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geo-scientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objective has been to develop a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines-SVMs and Random Forests-RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land cover classification in a location where both wetland and non-wetland classes exist.
2019
Sentinel-2 land monitoring constellation mission aims to generate products similar with the Landsat-8 images, the world’s longest continuously acquired collection of space-based land earth observation data. Though both sensors share similar spectral characteristics, their Relative Spectral Response Filters (RSRFs) are not identical. It is consequently important to assess whether and to what extent endproducts, such as land use maps, may vary between these two sensors. For this purpose, the random forest classifier was applied over a semi-arid environment in the Eastern Mediterranean (Cyprus). Initially the Sentinel-2 image was sampled to the Landsat-8 spatial resolution. Then, two different classification strategies have been followed: the first one using an equal (balance) training sample between the 11 land use classes, while the second classification was based on a random training sample. In addition, land use maps were also generated based on maximum likelihood, mahalanobis dist...
A Multiple Classifier System for Supervised Classification of Remote Sensing Data
J. Duhok Univ., (Pure and Eng. Sciences),, 2011
In this paper a new scheme of multiple classifier system (MCS) for remote sensing data classification is proposed. It includes four member classifiers; maximum likelihood, minimum distance and two differently trained supervised artificial neural networks of type adaptive resonance theory ART_II. The system is based on newly developed method of integration, named Local Ranking (LK). This method is categorized as dynamic classifier selection (DCS) approach and based on ranking the classifiers for each class on the basis of pre-estimation of the class mapping accuracy from training data. The system is applied to multi-spectral image, taken by landsat-7 with ETM+ sensor, of Duhok city in Kurdistan region to classify seven cover types (residential area, water surface, dense vegetation, less dense vegetation, spars vegetation, wet soil and dry soil). The results have shown the superiority of the system performance over the performance of the individual classifiers in term of class and average accuracy. The increase in system performance for the seven classes compared to the highest class accuracy provided by any of the individual classifiers was 1.59%, 0.0%, 0.7%, 5.14%, 13.21%, 4.67%, 2.02% respectively. While the increase in the average accuracy compared to the highest average accuracy provided by maximum likelihood classifier was 4.30%. This increase correspond to an area of (10.15) km2. The efficiency of the local ranking (LR) method is compared to the well known methods, local accuracy (LA) and majority voting (MV). The results have shown the superiority of the developed method (LR) over LA and MV in terms of average and class accuracy. The average accuracies of LR, LA and MV were (94.68%), (91.54%) and (91.57%) which correspond to (3.14%) and (3.11%) improvements in the favor of LR over LA and MV. These improvements are equivalent to areas of (7.4) km2 and (7.34) km2. For individual class accuracy, LR method provided highest accuracy for three classes, LA method provided highest accuracy for two classes and MV provided highest accuracy for only one class, while all three methods provided same accuracy for one class.
Coasts, 2024
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.