Browsing by Author "Pradeep Kumar Garg"

Now showing 1 - 2 of 2

Exploring the Capabilities of Sentinel-2 Data in Vegetation Health/Stress Mapping
(Institute of Electrical and Electronics Engineers Inc., 2019) Gaurav Shukla; Rahul Dev Garg; Pradeep Kumar Garg; Hari Shankar Srivastava; Pradeep Kumar; Bijayananda Mohanty
The freely available imagery from Sentinel-2, with 13 optical narrow bands in moderate-to-high spatial resolution (10, 20, and 60m) attracts the research community to explore the potentiality of bands for different land services. This study examined the capabilities of Sentinel-2 in vegetation health/stress monitoring, especially, narrow red-edge bands and their significance in deriving vegetation health/stress using different vegetation parameters such as leaf water contents, level of carotenoid pigment and chlorophyll.The study area is Keoladeo National Park (KNP), Bharatpur, Rajasthan, India. National park is selected to utilize the diversity of park to examine the suitability of bands for vegetation health/stress mapping. The availability of bands for the wavelength at 500μm, 445μm, 705μm, 750μm, 680μm, 819μm, and 1599μm have shown the importance for vegetation's health/stress mapping. Furthermore, the relative amounts of vegetation health scaled from 1 (unhealthy) to 9 (healthy) are also calculated. Comparative analysis with different indexes is also discussed in KNP. Results of this study show the significance of the Sentinel-2 red-edge bands in vegetation health/stress mapping. © 2019 IEEE.
Using multi-source data and decision tree classification in mapping vegetation diversity
(Springer Science and Business Media B.V., 2018) Gaurav Shukla; Rahul Dev Garg; Pradeep Kumar; Hari Shanker Srivastava; Pradeep Kumar Garg
This study acknowledges the problem of land cover demarcation in diverse vegetation condition. The Normalized Difference Vegetation Index is used for the preparation of base map. Further identification of mix and incorrect classes was done using ground truth. Radar data in combination with optical indices are used. In different NDVI classes, σ°RV with additional criteria on Normalized Difference Water Index successfully demarcated waterlogged area, polarization ratio σ°RV/σ°RH and backscattering coefficient σ°RH are found suitable to separate bare land from dry grass land, sparse and dense scrub could be separated by − (σ°RV + σ°RH)/2 and NDVI is efficient to identify dense vegetation. The study area is taken as Keoladeo National Park in Bharatpur, India. Statistical similarity between ground truth and classified class has been assessed using Jaccard coefficient (JC), Jaccard distance (JD), Dice coefficient (DC) and F-score. High similarity values of JC, JD, DC and F-score are achieved for all land cover types except bare land. Although, dry grassland showed low value of F-score; the reason could be low precision of class. The overall accuracy (87.17%), producer’s accuracy (86.39%), user’s accuracy (85.81%) and Kappa Coefficient (0.84) are also utilized to analyze performance of classifier. © 2018, Korean Spatial Information Society.