Using multi-source data and decision tree classification in mapping vegetation diversity

Shukla, Gaurav; Garg, Rahul; Kumar, Pradeep; Srivastava, H. S.; Garg, Pradeep

doi:10.1007/s41324-018-0200-4

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…The use of remote sensing technology has become the main approach for vegetation ecological resource surveys and environmental monitoring due to the corresponding real-time, repeatability, and wide-coverage advantages [2][3][4]. With the development of remote sensing technology, visible light, multispectral, hyperspectral, and other sensors have been widely used in the remote sensing of vegetation [5,6], and more hyperspectral and high-resolution information has been obtained than ever before, greatly improving the accuracy of image classification [7,8].…”

Section: Introductionmentioning

confidence: 99%

Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification

et al. 2019

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To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF_0°) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF_0° was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF_PP) and on the cross-principal plane (BRDF_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF_PP features performed better than BRDF_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10° and −20° were most favorable for vegetation classification (solar position: zenith angle 28.86°, azimuth 169.07°) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring.

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Section: Introductionmentioning

confidence: 99%

Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification

et al. 2019

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“…The forests of Korea comprise coniferous forest (41%), broadleaved forest (27%), and mixed stand forest (29%). (7,8) They are vulnerable to fire, diseases, and pests. Recently, in Jeju Special Self-Governing Province and Gyeongsangbuk-do, the widespread distribution of BURSXY has caused much damage.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vegetation Infection Information Using Unmanned Aerial Vehicle with Optical Sensor

Jung¹,

Park²

2019

Sensors and Materials

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The forests (approx. 640000 ha) of Korea comprise coniferous forest (41%), broad-leaved forest (27%), and mixed stand forest (29%). They appear to be vulnerable to fire, diseases, and pests. The pine tree is one of the typical Korean species of trees. It was more than 50% of the whole forest area of the country in the 1960s, but the area of pine tree forests has been reduced to 30% because of recent changes in the forest ecosystem and damage caused by diseases and insect pests. In particular, pine wilt disease is currently spreading over Korea. In this study, an unmanned aerial vehicle (UAV) with an optical sensor was used to detect infected trees and to support big data on forest management. Red, green, and blue (RGB) images and near-infrared (NIR) images were acquired using UAV. The infected trees were detected using the RGB images, and the normalized difference vegetation index (NDVI) values were calculated using NIR images. The NDVIs of infected trees were lower than those of non-infected ones, and infected trees that were not detected as infected ones in the RGB images also have lower NDVIs than the neighboring trees that were detected as being infected. Through further research, if a distinct feature of the NDVI of infected trees is discovered, it will be helpful for the early detection of infected trees. Hence, this research is expected to be applied to the detection of infected trees and to support big data on forest management.

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Comprehensive geospatial mapping and monitoring of an eastern coalfield in India

Kumar,

Choudhary,

Kumar

et al. 2024

Discov Geosci

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Geospatial mapping and monitoring are crucial for designing slopes in opencast mining, as failure can result in significant economic and life loss. To overcome these issues, the present investigation evaluated the Digital Elevation Model (DEM) generated using high resolution Cartosat-1 satellite imagery. Simultaneously, mapping of shaded relief map, aspect map and slope maps was done for the monitoring of Shatabdi opencast coal mine of Jharia region, an eastern coalfield region of India. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades. The constructed aspect map encompasses the range between 0 and 360 degrees of direction, with categorization and representation through distinct colors. The slope map was classified into various categories based on degrees of slope, including very gentle, gentle, moderate, moderately steep, steep, and very steep slopes. The maps were ground validated using Differential Global Positioning System (DGPS) collected data points by field visit in the region. For opencast mines to operate safely and profitably, slope stability in different stages of mining must be mapped and monitored. The study synthesizes data from diverse sources, highlighting the role of geospatial technology in addressing multiple research gaps within the mining industries prior to leveraging Cartosat-1 satellite data. Slope mapping and monitoring of coalfield regions are crucial to reducing construction costs, mitigation of natural hazard risks like flooding and landslides, and effective conservation of natural resources such as soils, vegetation, and water systems. The present study benefits policymakers, environmental planners and government in formulating policies to ensure safety, reliability, and enhance the economic growth of workers and the country.

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Using multi-source data and decision tree classification in mapping vegetation diversity

Cited by 5 publications

References 36 publications

Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification

Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification

Analysis of Vegetation Infection Information Using Unmanned Aerial Vehicle with Optical Sensor

Comprehensive geospatial mapping and monitoring of an eastern coalfield in India

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