Vegetation Analysis and Land Use Land Cover Classification of Forest in Uttara Kannada District India Using Remote Sensign and Gis Techniques

Koppad, A. G.; Janagoudar, B. S.

doi:10.5194/isprs-archives-xlii-4-w5-121-2017

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Forest resources fulfil many functions and constitute an important element of human life. Accurate information on the status of forest resources and their constant monitoring at local, regional and global scales is crucial for their sustainable management [1]. Information on forest dynamics is essential for deriving an extent and rate of deforestation [2].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Waśniewski

Hościło

Zagajewski

et al. 2020

Forests

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This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

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Section: Introductionmentioning

confidence: 99%

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Waśniewski

Hościło

Zagajewski

et al. 2020

Forests

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“…The optimized soil adjusted vegetation index (OSAVI) has a similar formula to that of NDVI but is used to minimize the influence of soil reflectance [33], a concern in many arid and semiarid regions with high amounts of bare soil. Another example of an index potentially useful in studying western juniper is the total ratio vegetation index (TRVI) [34], which was developed to address different vegetation characteristics in arid and semiarid ecosystems (e.g., juniper woodlands).Other techniques used in image analysis such as classification have been utilized extensively for the detection and assessment of vegetation [35][36][37]. Pixel-based image analysis has been used for weed detection [38] and vegetation identification in mixed plant communities [39].…”

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confidence: 99%

“…Other techniques used in image analysis such as classification have been utilized extensively for the detection and assessment of vegetation [35][36][37]. Pixel-based image analysis has been used for weed detection [38] and vegetation identification in mixed plant communities [39].…”

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confidence: 99%

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Durfee

Ochoa

Mata-González

2019

Forests

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Monitoring vegetation characteristics and ground cover is crucial to determine appropriate management techniques in western juniper (Juniperus occidentalis Hook.) ecosystems. Remote-sensing techniques have been used to study vegetation cover; yet, few studies have applied these techniques using unmanned aerial vehicles (UAV), specifically in areas of juniper woodlands. We used ground-based data in conjunction with low-altitude UAV imagery to assess vegetation and ground cover characteristics in a paired watershed study located in central Oregon, USA. The study was comprised of a treated watershed (most juniper removed) and an untreated watershed. Research objectives were to: (1) evaluate the density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed; and, (2) assess the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover. Groundbased measurements were used to assess vegetation features in each watershed and as a means to verify analysis from aerial imagery. Visual imagery (red, green, and blue wavelengths) and multispectral imagery (red, green, blue, near-infrared, and red-edge wavelengths) were captured using a quadcopter-style UAV. Canopy cover in the untreated watershed was estimated using two different methods: vegetation indices and support vector machine classification. Supervised classification was used to assess juniper sapling density and vegetation cover in the treated watershed. Results showed that vegetation indices that incorporated near-infrared reflectance values estimated canopy cover within 0.7% to 4.1% of ground-based calculations. Canopy cover estimates at the untreated watershed using supervised classification were within 0.9% to 2.3% of ground-based results. Supervised classification applied to fall imagery using multispectral bands provided the best estimates of juniper sapling density compared to imagery taken in the summer or to using visual imagery. Study results suggest that low-altitude multispectral imagery obtained using small UAV can be effectively used to assess western juniper density and canopy cover. western juniper in particular has arisen in two primary forms: through the encroachment of juniper into areas previously dominated by sagebrush (Artemisia tridentata Nutt.), and through increases in the density of juniper in areas where it was relatively sparse [3]. Historically, juniper was largely found in areas with lower fire risk [4]. Intensive grazing, reduced fire occurrence, and favorable wetter climate conditions have all been cited as reasons for the vast juniper expansion observed in the late 19th and early 20th century [3,5].Juniper expansion is a concern in many rangeland areas as it may lead to reduced water availability for other types of vegetation. Increased juniper canopy cover has been associated with increased bare ground and decreased shrub, forb, and grass cover [6] and reductions in vegetation production and diversity [7]. Several studies [6,[8][9][10][11...

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Classification of Remotely Sensed Data Using Fisher’s Linear Discriminant

Shivakumar,

Raghudathesh

2024

Lecture Notes in Electrical Engineering

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Vegetation Analysis and Land Use Land Cover Classification of Forest in Uttara Kannada District India Using Remote Sensign and Gis Techniques

Cited by 4 publications

References 8 publications

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Classification of Remotely Sensed Data Using Fisher’s Linear Discriminant

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