Identifying mangroves through knowledge extracted from trained random forest models: An interpretable mangrove mapping approach (IMMA)

Zhao, Chenyang; Jia, Mingming; Wang, Zongming; Mao, Dehua; Wang, Yeqiao

doi:10.1016/j.isprsjprs.2023.05.025

Cited by 27 publications

(2 citation statements)

References 75 publications

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“…Identifying and selecting classification features represent a decisive factors for the success of mangrove remote sensing classification. With the increased support of multisource remote sensing big data, the feature variables currently used for mangrove forest recognition include electromagnetic spectral features, spatial features, temporal features, as well as other auxiliary geoscientific features such as digital elevation models (DEMs) [10]. On the one hand, a single type or source of data is insufficient to effectively express the complex features of mangrove forests and cannot fully meet the requirements.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Shen,

Miao,

Wang

et al. 2023

Remote Sensing

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Mangrove forests, mostly found in the intertidal zone, are among the highest-productivity ecosystems and have great ecological and economic value. The accurate mapping of mangrove forests is essential for the scientific management and restoration of mangrove ecosystems. However, it is still challenging to perform the rapid and accurate information mapping of mangrove forests due to the complexity of mangrove forests themselves and their environments. Utilizing multi-source remote sensing data is an effective approach to address this challenge. Feature extraction and selection, as well as the selection of classification models, are crucial for accurate mangrove mapping using multi-source remote sensing data. This study constructs multi-source feature sets based on optical (Sentinel-2) and SAR (synthetic aperture radar) (C-band: Sentinel-1; L-band: ALOS-2) remote sensing data, aiming to compare the impact of three feature selection methods (RFS, random forest; ERT, extremely randomized tree; MIC, maximal information coefficient) and four machine learning algorithms (DT, decision tree; RF, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient-boosting machine) on classification accuracy, identify sensitive feature variables that contribute to mangrove mapping, and formulate a classification framework for accurately recognizing mangrove forests. The experimental results demonstrated that using the feature combination selected via the ERT method could obtain higher accuracy with fewer features compared to other methods. Among the feature combinations, the visible bands, shortwave infrared bands, and the vegetation indices constructed from these bands contributed the greatest to the classification accuracy. The classification performance of optical data was significantly better than SAR data in terms of data sources. The combination of optical and SAR data could improve the accuracy of mangrove mapping to a certain extent (0.33% to 4.67%), which is essential for the research of mangrove mapping in a larger area. The XGBoost classification model performed optimally in mangrove mapping, with the highest overall accuracy of 95.00% among all the classification models. The results of the study show that combining optical and SAR remote sensing data with the ERT feature selection method and XGBoost classification model has great potential for accurate mangrove mapping at a regional scale, which is important for mangrove restoration and protection and provides a reliable database for mangrove scientific management.

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Section: Introductionmentioning

confidence: 99%

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Shen,

Miao,

Wang

et al. 2023

Remote Sensing

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“…The utilization of traditional field survey methods to map the spatial distribution of mangroves poses challenges in data collection and mapping quality due to limitations, such as time-consuming and labor-intensive (Pandey et al, 2019;Wang et al, 2020;Zhao et al, 2023a). Remote sensing has become indispensable for the monitoring and management of mangrove, finding applications in extent mapping, species classification, community structure analysis, and biomass estimation (Giri, 2016;Pham et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mangrove forest mapping from object-oriented multi-feature ensemble classification using Sentinel-2 images

Zhang,

Xia,

Dai

et al. 2023

Front. Mar. Sci.

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Accurate mapping of mangrove forests is crucial for understanding their ecosystem function and developing effective management policies. However, the absence of an operational multi-feature fusion approach and an ensemble classification system restricts the achievement of this goal. This study aims to develop an object-oriented multi-feature ensemble classification scheme (OMEC). First, an enhanced mangrove spectral index (EMSI) is established by analyzing the spectral reflectance differences between mangrove forests and other land cover types. Sentinel-2 images are segmented into objects using the multi-resolution segmentation method. Then, spectral, textural, and geometric features are extracted, and these features (including EMSI) are inputted into the nearest neighbor classifier to implement mangrove classification. The experiment was conducted in three typical mangrove areas in China using Sentinle-2 images. The results demonstrate that EMSI exhibits good spectral separability for mangroves and performs well in the ensemble classification scheme. The overall accuracy of mangrove classification exceeds 90%, with a Kappa coefficient greater than 0.88. The object-oriented multi-feature ensemble classification scheme significantly improves accuracy and exhibits excellent performance. The method enhances the accuracy of mangrove classification, enriches the approach to mangrove remote sensing interpretation, and offers data support and scientific references for the restoration, management, and protection of coastal wetlands.

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Integration of machine learning and remote sensing for assessing the change detection of mangrove forests along the Mumbai coast

Sawant,

Bonala,

Joshi

et al. 2024

J Earth Syst Sci

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Identifying mangroves through knowledge extracted from trained random forest models: An interpretable mangrove mapping approach (IMMA)

Cited by 27 publications

References 75 publications

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Mangrove forest mapping from object-oriented multi-feature ensemble classification using Sentinel-2 images

Integration of machine learning and remote sensing for assessing the change detection of mangrove forests along the Mumbai coast

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