Fig. 149: Architecture of the Model

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Abbreviations

for spectrum generation. Æ Satellite data- (CHRIS) acquired (24/9/2017) for the second field trip had 70% cloud. Since there is no alternative data that matches with this field inventory, the same data was used after removing the cloud from CHRIS data using the mono-regression method. The values were replaced using the Sentinel data of the same date. Æ The data (cloud-free) relating to the field – 3 was acquired on 24-05-2018. Æ Noise removal, and atmospheric and geometric correction processes were conducted using scripting and regular expressions for all three seasons. Æ An interface was made for the leaf upscaling using a new approach. The input parameters were biochemical values of the leaf species from the lab and on-field parameters (primary being leaf spectrum) which is upscaled to canopy level and then the spectrum is recreated concerning the upscaled biochemical parameters.

Species Canopy upscaling

Æ The model considers leaf spectra along with derived canopy properties in estimating canopy spectra.

The canopy spectrum starts at 400nm. Æ It is considered that 480 nm – 750 nm is the chlorophyll retrieval region; 376nm-406nm, 1300nm1450nm, 1778nm – 1949nm are water absorption regions; 1700nm to 1750nm is for nitrogen retrieval, 2100nm – 2300nm is for lignin and cellulose absorption retrieval together. Æ In general, the water absorption bands are considered from 1300nm – 1450nm, but water can affect the 376nm-406nm region due to their presence in leaf pigments; and the the1778nm-1949nm region due to the presence of protein and nitrogen. Æ The canopy level estimates of selected biophysical and biochemical parameters were integrated to result in canopy-level spectra using a semi-analytical search-based expert knowledge-driven polynomial modelling, considering the spectral sensitivity of canopy parameters across the optical, and electromagnetic spectrum. Æ While upscaling, parametric regression is applied to a few spectral bands to maintain the pattern of the spectrum.

Fig. 149: Architecture of the Model

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Fig. 149: Architecture of the Model

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References

Fig. 165: Pramanik, S.; Butsch, C.; Punia, M. Post-liberal urban dynamics in India— The case of Gurugram, the ‘Millennium City’. Remote Sens. Appl. Soc. Environ. 2021, 22, 100504

Quantifying Past, Present, and Future Urban Dynamics for Sustainable Urban Development

Systematic Mapping of Avian Biodiversity through Public Participation

Lake Monitoring and Management System for Sustaining Minor Irrigation in Arid and Semi-Arid Environments

Fig. 150: Interface of the Model

Fig. 158: Some snapshots from the field survey. A) dense urban landscape near Jamma Masjid in Old Delhi; conducting field survey B) near Uttam Nagar metro station in West Delhi; C) near Preet Vihar in East Delhi; D) abundance of urban green space near Red Fort

Fig. 151: LMMS Solution Architecture based on OGC Standards

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Integrated Geospatial Information Framework