Assessing the Utility of Uav-Borne Hyperspectral Image and Photogrammetry Derived 3d Data for Wetland Species Distribution Quick Mapping

Li, Q. S.; Wong, Frankie Kwan Kit; Fung, Tung

doi:10.5194/isprs-archives-xlii-2-w6-209-2017

Cited by 22 publications

(16 citation statements)

References 25 publications

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“…Furthermore, hyperspectral sensors have been shrinking in size and weight, and their use onboard of UAVs has become feasible [26][27][28]. Recently, several studies have addressed the use of UAV hyperspectral sensors in vegetation [29,30], crop [31][32][33][34][35][36][37][38][39][40][41][42], forest monitoring [43], and wetland species mapping [44], etc. However, few specific results have been published on mapping mangrove species using UAV hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi'ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

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Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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“…OBIA was discovered to be superior to pixel-based classification by Pande-Chhetri et al [92]. UAS imagery has also been demonstrated to be suitable for species distribution quick mapping [94], as well as for training and validating satellite imagery [90]. Novel techniques for enhancing OBIA have been developed which take advantage of the unique characteristics of UAS data [91].…”

Section: Applications For Wetland Mapping and Hydrologic Modelingmentioning

confidence: 99%

UAS for Wetland Mapping and Hydrological Modeling

Jeziorska

2019

Remote Sensing

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The miniaturization and affordable production of integrated microelectronics have improved in recent years, making unmanned aerial systems (UAS) accessible to consumers and igniting their interest. Researchers have proposed UAS-based solutions for almost any conceivable problem, but the greatest impact will likely be in applications that exploit the unique advantages of the technology: work in dangerous or difficult-to-access areas, high spatial resolution and/or frequent measurements of environmental phenomena, and deployment of novel sensing technology over small to moderate spatial scales. Examples of such applications may be the identification of wetland areas and use of high-resolution spatial data for hydrological modeling. However, because of the large—and growing—assortment of aircraft and sensors available on the market, an evolving regulatory environment, and limited practical guidance or examples of wetland mapping with UAS, it has been difficult to confidently devise or recommend UAS-based monitoring strategies for these applications. This paper provides a comprehensive review of UAS hardware, software, regulations, scientific applications, and data collection/post-processing procedures that are relevant for wetland monitoring and hydrological modeling.

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“…UAS have presented a cost-effective and relatively simple solution to the problems of obtaining aerial data for ecological purposes, with flight planning software that allows aerial surveys, images, and videos to be obtained with little effort by remote operators [8, 9, 10]. Additionally, mangrove forests and other wetlands present areas of high potential for UAS-based surveys, as they remain difficult to access and survey through traditional ground based methods [11, 12, 13, 14, 15, 16, 17].…”

Section: Introductionmentioning

confidence: 99%

Using Unmanned Aerial Systems (UAS) to assay mangrove estuaries on the Pacific coast of Costa Rica

Yaney-Keller¹,

Tomillo²,

Marshall³

et al. 2019

PLoS ONE

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Mangrove forests, one of the world’s most endangered ecosystems, are also some of the most difficult to access. This is especially true along the Pacific coast of Costa Rica, where 99% of the country’s mangroves occur. Unmanned Aerial Systems (UAS), or drones, have become a convenient tool for natural area assessment, and offer a solution to the problems of remote mangrove monitoring. This study is the first to use UAS to analyze the structure of a mangrove forests within Central America. Our goals were to (1) determine the forest structure of two estuaries in northwestern Costa Rica through traditional ground measurements, (2) assess the accuracy of UAS measurements of canopy height and percent coverage and (3) determine whether the normalized difference vegetation index (NDVI) could discriminate between the most abundant mangrove species. We flew a UAS equipped with a single NDVI sensor during the peak wet (Sept–Nov) and dry (Jan–Feb) seasons. The structure and species composition of the estuaries showed a possible transition between the wet mangroves of southern Costa Rica and the drier northern mangroves. UAS-derived measurements at 100 cm/pixel resolution of percent canopy coverage and maximum and mean canopy height were not statistically different from ground measurements (p > 0.05). However, there were differences in mean canopy height at 10 cm/pixel resolution (p = 0.043), indicating diminished returns in accuracy as resolution becomes extremely fine. Mean NDVI values of Avicennia germinans (most abundant species) changed significantly between seasons (p < 0.001). Mean NDVI of Rhizophora racemosa (second most abundant species) was significantly different from A . germinans and dry forest dominant plots during the dry season (p < 0.001), demonstrating NDVI’s capability of discriminating mangrove species. This study provides the first structural assessment of the studied estuaries and a framework for future studies of mangroves using UAS.

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Assessing the Utility of Uav-Borne Hyperspectral Image and Photogrammetry Derived 3d Data for Wetland Species Distribution Quick Mapping

Cited by 22 publications

References 25 publications

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

UAS for Wetland Mapping and Hydrological Modeling

Using Unmanned Aerial Systems (UAS) to assay mangrove estuaries on the Pacific coast of Costa Rica

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