Improving image classification in a complex wetland ecosystem through image fusion techniques

Kumar, Lalit; Sinha, Priyakant; Taylor, Subhashni

doi:10.1117/1.jrs.8.083616

Cited by 27 publications

(6 citation statements)

References 49 publications

(54 reference statements)

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“…31,101 Since remote sensing data are available from a range of sensors, each with its own characteristics and time series, it would be more useful if they were combined or fused to produce a better understanding of the observed site. 103,104,235,236 Studies in the past have shown that the fusion of optical (multi and PAN) and also SAR data resulted in an improved performance for biomass estimation. Multisensor or multiresolution data fusion takes advantage of the strengths of distinct image data for improvement of visual interpretation and quantitative analysis 3 and numerous methods have been developed to integrate spectral and spatial information from different sensors.…”

Section: Image Fusionmentioning

confidence: 99%

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Kumar¹,

Sinha²,

Taylor³

et al. 2015

J. Appl. Remote Sens

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139

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The quantification, mapping and monitoring of biomass are now central issues due to the importance of biomass as a renewable energy source in many countries of the world. The estimation of biomass is a challenging task, especially in areas with complex stands and varying environmental conditions, and requires accurate and consistent measurement methods. To efficiently and effectively use biomass as a renewable energy source, it is important to have detailed knowledge of its distribution, abundance, and quality. Remote sensing offers the technology to enable rapid assessment of biomass over large areas relatively quickly and at a low cost. This paper provides a comprehensive review of biomass assessment techniques using remote sensing in different environments and using different sensing techniques. It covers forests, savannah, and grasslands/rangelands, and for each of these environments, reviews key work that has been undertaken and compares the techniques that have been the most successful. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Image Fusionmentioning

confidence: 99%

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Kumar¹,

Sinha²,

Taylor³

et al. 2015

J. Appl. Remote Sens

Self Cite

139

119

View full text Add to dashboard Cite

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“…The combination of band 6 (swir1, 1560-1660 nm), band 5 (nir, 845-885 nm) and band 4 (red, 630-680 nm) RGB of the Landsat 8 image taken on 26 July 2014 was tested for image segmentation and classification. The multispectral bands (4, 3, 2) and the panchromatic band of ZY 3 images were fused using Gram-Schmidt Spectral Sharpening (Kumar et al, 2014).…”

Section: Object Segmentation and Wetland Delineationmentioning

confidence: 99%

A method for alpine wetland delineation and features of border: Zoigê Plateau, China

et al. 2017

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Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoigê Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoigê Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index (NDVI) in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index (NDWI) of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 km 2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.

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“…There are many studies on wetland mapping over China also, and remote sensing has been used in monitoring changes in the Poyang Lake, Honghe wetlands and Zhalong River wetlands [13][14][15]. Classification algorithms such as conventional decision tree, maximum likelihood, support vector machines (SVM), and artificial neural networks (ANN) have been employed in wetland classification [16][17][18][19][20][21]. With the improved spatial resolution of remotely sensed imagery, the object-oriented strategy has also been proposed [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

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Improving image classification in a complex wetland ecosystem through image fusion techniques

Cited by 27 publications

References 49 publications

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Review of the use of remote sensing for biomass estimation to support renewable energy generation

A method for alpine wetland delineation and features of border: Zoigê Plateau, China

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

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