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1 Amani, M., Ghorbanian, A., Ahmadi, S.A. et al. (2020). Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

2 Aparicio, N., Villegas, D., Araus, J. et al. (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science 42 (5): 1547–1555.

3 Avery, T. and Berlin, G. (1992). Fundamentals of Remote Sensing and Airphoto Interpretation. New York: Macmillan.

4 Baldwin, R.A. (2009). Use of maximum entropy modeling in wildlife research. Entropy 11 (4): 854–866.

5 Bashir, M., Hata, T., Tanakamaru, H. et al. (2008). Satellite‐based energy balance model to estimate seasonal evapotranspiration for irrigated sorghum: a case study from the Gezira scheme, Sudan. Hydrology and Earth System Sciences 12 (4): 1129–1139.

6 Bedoui, C. (2020). Study of desertification sensitivity in Talh region (Central Tunisia) using remote sensing, GIS and the MEDALUS approach. Geoenvironmental Disasters 7 (1): 1–16.

7 Blazquez, C.H. and Edwards, G.J. (1986). Spectral reflectance of healthy and diseased watermelon leaves. Annals of Applied Biology 108 (2): 243–249.

8 Bogdanov, A., Karpov, A., Demina, N. et al. (2018). Improving forest monitoring by using cloud technologies as an element of sustainable forest management. Current problems in remote sensing of the earth from space [Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa] 15 (1): 89–100.

9 Bolliger, J., Kienast, F., Zimmermann, N. et al. (2000). Risks of global warming on montane and subalpine forests in Switzerland–a modeling study. Regional Environmental Change 1 (3–4): 99–111.

10 Bryan, B.A., King, D., Ward, J. et al. (2011). Modelling and mapping agricultural opportunity costs to guide landscape planning for natural resource management. Ecological Indicators 11 (1): 199–208.

11 Burkhard, B., Kroll, F., Nedkov, S., and Müller, F. (2012). Mapping ecosystem service supply, demand and budgets. Ecological Indicators 21: 17–29.

12 Chen, P., Haboudane, D., Tremblay, N. et al. (2010). New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment 114 (9): 1987–1997.

13 Curran, P.J., Dungan, J.L., and Gholz, H.L. (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology 7 (1–4): 33–48.

14 Disney, M., Lewis, P., and Saich, P. (2006). 3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sensing of Environment 100 (1): 114–132.

15 Dubayah, R.O. and Drake, J.B. (2000). LiDAR remote sensing for forestry. Journal of Forestry 98 (6): 44–46.

16 Elhag, M., Psilovikos, A., Manakos, I., and Perakis, K. (2011). Application of the SEBS water balance model in estimating daily evapotranspiration and evaporative fraction from remote sensing data over the Nile Delta. Water Resources Management 25 (11): 2731–2742.

17 Esteves, T., Kirkby, M., Shakesby, R. et al. (2012). Mitigating land degradation caused by wildfire: application of the PESERA model to fire‐affected sites in Central Portugal. Geoderma 191: 40–50.

18 Georgoussis, H., Babajimopoulos, C., Panoras, A. et al. (2009). Regional scale irrigation scheduling using a mathematical model and GIS. Desalination 237 (1–3): 108–116.

19 Gitelson, A.A. and Merzlyak, M.N. (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 148 (3–4): 494–500.

20 Guerif, M., Houlès V., and Baret F. (2007). Remote sensing and detection of nitrogen status in crops. Application to precise nitrogen fertilization. 4. International Symposium on Intelligent Information Technology in Agriculture.

21 Jensen, J.R. and Im, J. (2007). Remote sensing change detection in urban environments. In: Geo‐spatial Technologies in Urban Environments (eds. R.R. Jensen, J.D. Gatrell and D. McLean), 7–31. Springer.

22 Kokaly, R.F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment 75 (2): 153–161.

23 Kumar, L. and Mutanga, O. (2018). Google earth engine applications since inception: usage, trends, and potential. Remote Sensing 10 (10).

24 Kumar, M., Kalra, N., Khaiter, P. et al. (2019a). PhenoPine: a simulation model to trace the phenological changes in Pinus roxhburghii in response to ambient temperature rise. Ecological Modelling 404: 12–20. https://doi.org/10.1016/j.ecolmodel.2019.05.003.

25 Kumar, M., Padalia, H., Nandy, S. et al. (2019b). Does spatial heterogeneity of landscape explain the process of plant invasion? A case study of Hyptis suaveolens from Indian Western Himalaya. Environmental Monitoring and Assessment 191 (Suppl. 3): 794. https://doi.org/10.1007/s10661‐019‐7682‐y.

26 Kumar, M., Singh, H., Pandey, R. et al. (2019c). Assessing vulnerability of forest ecosystem in the Indian Western Himalayan region using trends of net primary productivity. Biodiversity and Conservation 28 (8–9): 2163–2182.

27 Lamqadem, A., Saber, H., and Pradhan, B. (2018). Quantitative assessment of desertification in an arid oasis using remote sensing data and spectral index techniques. Remote Sensing 10 (12).

28 Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J. (2002). LiDAR remote sensing for ecosystem studies: LiDAR, an emerging remote sensing technology that directly measures the three‐dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. BioScience 52 (1): 19–30.

29 López‐Vicente, M., Poesen, J., Navas, A., and Gaspar, L. (2013). Predicting runoff and sediment connectivity and soil erosion by water for different land use scenarios in the Spanish Pre‐Pyrenees. Catena 102: 62–73.

30 Mahajan, S. and Panwar, P. (2005). Land use changes in Ashwani Khad watershed using GIS techniques. Journal of the Indian Society of Remote Sensing 33 (2): 227.

31 Manfreda, S., McCabe, M., Miller, P., and Lucas, R. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sensing 10 (4).

32 Melesse, A.M. and Graham, W.D. (2004). Storm runoff prediction based on a spatially distributed travel time method utilizing remote sensing and GIS 1. JAWRA Journal of the American Water Resources Association 40 (4): 863–879.

33 Miller, J., Hare, E., and Wu, J. (1990). Quantitative characterization of the vegetation red edge reflectance 1. An inverted‐Gaussian reflectance model. Remote Sensing 11 (10): 1755–1773.

34 Oisebe, P.R. (2012). GIS and Natural Resource Management. Retrieved 01/17, 2021.

35 Olokeogun, O.S. and Kumar, M. (2020). An indicator based approach for assessing the vulnerability of riparian ecosystem under the influence of urbanization in the Indian Himalayan city, Dehradun. Ecological Indicators 119: 106796.

36 Park, J.H. and Hur, Y.T. (2012). Development and application of GIS based K‐DRUM for flood runoff simulation using radar rainfall. Journal of Hydro‐Environment Research 6 (3): 209–219.

37 Pearson, R.G., Raxworthy, C.J., Nakamura, M., and Townsend Peterson, A. (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography 34 (1): 102–117.

38 Pen Uelas, J., Filella, I., Lloret, P. et al. (1995). Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing 16 (14): 2727–2733.

39 Philipson, P., Pierson D.C., and Lindell T. (2003). Evaluation of Swedish lake water quality modeling from remote sensing. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, International Society for Optics and Photonics.

40 Phillips, S.J., Anderson R.P., and Schapire R.E. (2004). A maximum entropy approach to species distribution modeling. Proceedings of the twenty‐first international conference on Machine learning.

41 Phillips, S.J., Dudík, M., and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190 (3–4): 231–259.

42 Pokhriyal, P., Rehman, S., Krishna, G.A. et al. (2020). Assessing forest cover vulnerability in Uttarakhand, India using analytical hierarchy process. Modeling Earth Systems and Environment https://doi.org/10.1007/s40808‐019‐00710‐y.

43 Polevshchikova, I. (2019). Disturbance analyses of forest cover dynamics using remote sensing and GIS. IOP Conference Series: Earth and Environmental Science, IOP Publishing.

44 Rasooli, S., Bonyad, A.E., and Pir Bavaghar, M. (2018). Forest fire vulnerability map using remote sensing data, GIS and AHP analysis (case study: Zarivar Lake surrounding area). Caspian Journal of Environmental Sciences 16 (4): 369–377.

45 Rautiainen, M. and Stenberg, P. (2005). Application of photon recollision probability in coniferous canopy reflectance simulations. Remote Sensing of Environment 96 (1): 98–107.

46 Raxworthy, C.J., Martinez‐Meyer, E., Horning, A. et al. (2003). Predicting distributions of known and unknown reptile species in Madagascar. Nature 426 (6968): 837–841.

47 Ritchie, J.C. and Cooper, C.M. (1991). An algorithm for estimating surface suspended sediment concentrations with landsat mss digital data 1. JAWRA Journal of the American Water Resources Association 27 (3): 373–379.

48 Ritchie, J.C., Schiebe, F.R., Cooper, C.M., and Harrington, J.A. Jr. (1994). Chlorophyll measurements in the presence of suspended sediment using broad band spectral sensors aboard satellites. Journal of Freshwater Ecology 9 (3): 197–206.

49 Robinson, L., Elith, J., and Hobday, A. (2011). Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Global Ecology and Biogeography 20 (6): 789–802.

50 Saidi, S., Bouri, S., Dhia, H.B., and Anselme, B. (2009). A GIS‐based susceptibility indexing method for irrigation and drinking water management planning: application to Chebba–Mellouleche Aquifer, Tunisia. Agricultural Water Management 96 (12): 1683–1690.

51 San Juan, R.F.d.V. and Domingo‐Santos, J.M. (2018). The role of GIS and LiDAR as tools for sustainable forest management. GIS‐An Overview of Applications 1: 124–148.

52 Shaharum, N.S.N., Shafri, H.Z.M., Gambo, J., and Abidin, F.A.Z. (2018). Mapping of Krau wildlife reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sensing Applications: Society and Environment 10: 24–35.

53 Sharp, E., Perry, C., Scharen, A. et al. (1985). Monitoring cereal rust development with a spectral radiometer. Phytopathology 75 (8): 936–939.

54 Shrestha, H.L. (2020). Geospatial solutions for forest management: a case study from Nepal. In: Handbook of Research on the Conservation and Restoration of Tropical Dry Forests (eds. R. Bhadouria, S. Tripathi, P. Srivastava and P. Singh), 268–283. IGI Global.

55 Shrivastava, P., Tripathi, M., and Das, S. (2004). Hydrological modelling of a small watershed using satellite data and GIS technique. Journal of the Indian Society of Remote Sensing 32 (2): 145–157.

56 Singh, H., Kumar, N., Kumar, M., and Singh, R. (2020). Modelling habitat suitability of western tragopan (Tragopan melanocephalus) a range‐restricted vulnerable bird species of the Himalayan region, in response to climate change. Climate Risk Management 29: 100241.

57 Singh, R.K., Sinha, V.S.P., Joshi, P.K., and Kumar, M. (2020a). A multinomial logistic model‐based land use and land cover classification for the south Asian Association for Regional Cooperation nations using moderate resolution imaging spectroradiometer product. Environment, Development and Sustainability: 1–22.

58 Singh, R.K., Sinha, V.S.P., Joshi, P.K., and Kumar, M. (2020b). Modelling agriculture, forestry and other land use (AFOLU) in response to climate change scenarios for the SAARC nations. Environmental Monitoring and Assessment 192 (4): 1–18.

59 Steddom, K., Bredehoeft, M., Khan, M., and Rush, C. (2005). Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Disease 89 (2): 153–158.

60 Steinfeld, C. and Kingsford, R.T. (2013). Disconnecting the floodplain: earthworks and their ecological effect on a dryland floodplain in the Murray–Darling Basin, Australia. River Research and Applications 29 (2): 206–218.

61 Steininger, M.K. (1996). Tropical secondary forest regrowth in the Amazon: age, area and change estimation with thematic mapper data. International Journal of Remote Sensing 17 (1): 9–27.

62 SuperMap (2019). AI GIS + Remote Sensing Enhances the Ability of Natural Resources Monitoring. Retrieved 18 January 2021, from https://www.supermap.com/en‐us/news/?82_718.html.

63 Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8 (2): 127–150.

64 Vemu, S. and Pinnamaneni, U.B. (2011). Estimation of spatial patterns of soil erosion using remote sensing and GIS: a case study of Indravati catchment. Natural Hazards 59 (3): 1299–1315.

65 Vittala, S.S., Govindaiah, S., and Gowda, H.H. (2008). Prioritization of sub‐watersheds for sustainable development and management of natural resources: an integrated approach using remote sensing, GIS and socio‐economic data. Current Science: 345–354.