Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery

Li, Nan; Lu, Dengsheng; Wu, Ming; Zhang, Yinlong; LinYing, Lu

doi:10.1080/01431161.2018.1500731

Cited by 22 publications

(25 citation statements)

References 79 publications

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“…The object-based classification method is an image automatic analysis method. The accuracy of image segmentation significantly affects the classification accuracy [47]. In this study, the multiscale segmentation algorithm in eCognition Developer software is used for segmentation.…”

Section: Image Segmentationmentioning

confidence: 99%

Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

Yanshuang

Zhang

2019

Forests

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The identification of tree species is one of the most basic and key indicators in forest resource monitoring with great significance in the actual forest resource survey and it can comprehensively improve the efficiency of forest resource monitoring. The related research has mainly focused on single tree species without considering multiple tree species, and therefore the ability to classify forest tree species in complex stand is not clear, especially in the subtropical monsoon climate region of southern China. This study combined airborne hyperspectral data with simultaneously acquired LiDAR data, to evaluate the capability of feature combinations and k-nearest neighbor (KNN) and support vector machine (SVM) classifiers to identify tree species, in southern China. First, the stratified classification method was used to remove non-forest land. Second, the feature variables were extracted from airborne hyperspectral image and LiDAR data, including independent component analysis (ICA) transformation images, spectral indices, texture features, and canopy height model (CHM). Third, random forest and recursion feature elimination methods were adopted for feature selection. Finally, we selected different feature combinations and used KNN and SVM classifiers to classify tree species. The results showed that the SVM classifier has a higher classification accuracy as compared with KNN classifier, with the highest classification accuracy of 94.68% and a Kappa coefficient of 0.937. Through feature elimination, the classification accuracy and performance of SVM classifier was further improved. Recursive feature elimination method based on SVM is better than random forest. In the spectral indices, the new constructed slope spectral index, SL2, has a certain effect on improving the classification accuracy of tree species. Texture features and CHM height information can effectively distinguish tree species with similar spectral features. The height information plays an important role in improving the classification accuracy of other broad-leaved species. In general, the combination of different features can improve the classification accuracy, and the proposed strategies and methods are effective for the identification of tree species at complex forest type in southern China.

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Section: Image Segmentationmentioning

confidence: 99%

Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

Yanshuang

Zhang

2019

Forests

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“…In previous research, single-date images were mostly used because of the difficulty in obtaining images of different seasons due to poor weather conditions, especially in tropical and subtropical regions [17,18]. Seasonal vegetation information has proven valuable in improving vegetation classification [19]. In the subtropical Lin'an District mountains, Xi et al [20] used multi-temporal Landsat images to extract hickory plantations by developing subpixel indices from linear spectral mixture analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Wang and Lu [25] used the expert-rule based approach based on Chinese Gaofen (GF-1) and ZiYuan (ZY-3) satellite images to successfully map Torrya forest distribution with an overall accuracy of 84.4%. High spatial resolution data with multi-temporal features are especially valuable for improving vegetation classification accuracy [19]. Reis and Tasdemir [26] applied QuickBird data during the growing and deciduous seasons to identify hazelnut in northeastern Turkey and found that accuracy increased by 9% compared to using single-season data.…”

Section: Introductionmentioning

confidence: 99%

Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data

et al. 2019

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The global availability of high spatial resolution images makes mapping tree species distribution possible for better management of forest resources. Previous research mainly focused on mapping single tree species, but information about the spatial distribution of all kinds of trees, especially plantations, is often required. This research aims to identify suitable variables and algorithms for classifying land cover, forest, and tree species. Bi-temporal ZiYuan-3 multispectral and stereo images were used. Spectral responses and textures from multispectral imagery, canopy height features from bi-temporal stereo imagery, and slope and elevation from the stereo-derived digital surface model data were examined through comparative analysis of six classification algorithms including maximum likelihood classifier (MLC), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The results showed that use of multiple source data—spectral bands, vegetation indices, textures, and topographic factors—considerably improved land-cover and forest classification accuracies compared to spectral bands alone, which the highest overall accuracy of 84.5% for land cover classes was from the SVM, and, of 89.2% for forest classes, was from the MLC. The combination of leaf-on and leaf-off seasonal images further improved classification accuracies by 7.8% to 15.0% for land cover classes and by 6.0% to 11.8% for forest classes compared to single season spectral image. The combination of multiple source data also improved land cover classification by 3.7% to 15.5% and forest classification by 1.0% to 12.7% compared to the spectral image alone. MLC provided better land-cover and forest classification accuracies than machine learning algorithms when spectral data alone were used. However, some machine learning approaches such as RF and SVM provided better performance than MLC when multiple data sources were used. Further addition of canopy height features into multiple source data had no or limited effects in improving land-cover or forest classification, but improved classification accuracies of some tree species such as birch and Mongolia scotch pine. Considering tree species classification, Chinese pine, Mongolia scotch pine, red pine, aspen and elm, and other broadleaf trees as having classification accuracies of over 92%, and larch and birch have relatively low accuracies of 87.3% and 84.5%. However, these high classification accuracies are from different data sources and classification algorithms, and no one classification algorithm provided the best accuracy for all tree species classes. This research implies the same data source and the classification algorithm cannot provide the best classification results for different land cover classes. It is necessary to develop a comprehensive classification procedure using an expert-based approach or hierarchical-based classification approach that can employ specific data variables and algorithm for each tree species class.

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“…The complicated and vulnerable ecosystems of coastal wetlands make the traditional manual investigation could not work in realistic applications, because many coastal wetlands are inaccessible for humans and field measurements are difficult and time consuming [11,12]. Satellite remote sensing techniques provide an important and effective data source for mapping and monitoring coastal wetlands, because of its unique characteristics in an easy data acquisition, spatially continuous coverage and short revisiting periods [4,13,14]. During the last several decades, the fast development of satellite remote sensing sensors has greatly enhanced our capability to map coastal wetlands, that is, panchromatic sensors [13,15], Synthetic Aperture Radar (SAR) [16], multispectral sensors [17,18] and even hyperspectral imagers [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…René R. Colditz explored 14 spectral indices and their appropriate thresholds for water mapping in coastal wetlands and told us that MODIS green (band 4) and short wave infrared (band 6) "MNDWI6" perform the best of all [35]. Moreover, supervised classifiers are usually employed to make the pixel-based classification on spectral signatures, for example, Statistical classifiers like maximum likelihood classifier (MLC) [13] and machine learning like support vector machine (SVM) [36] and random forest (RF) [37]. Reschke combined multi-temporal Landsat imagery with high resolution satellite data and adopted the RF algorithm to extract subpixel information of coastal wetland classes [38].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

et al. 2019

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Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

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Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery

Cited by 22 publications

References 79 publications

Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

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