New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

Roy, P. S.; Behera, Mukunda Dev; Murthy, M. S. R.; Roy, Arijit; Singh, Sarnam; Kushwaha, S. P. S.; Jha, Chandra Shekhar; Sudhakar, S.; Joshi, P. K.; Reddy, C. Sudhakar; Gupta, Stutee; Pujar, G. S.; Dutt, C. B. S.; Srivastava, Vijay Kumar; Porwal, M. C.; Tripathi, Poonam; Singh, J. S.; Chitale, Vishwas; Skidmore, Andrew K.; Rajshekhar, G.; Kushwaha, Deepak; Karnatak, Harish; Saran, Sameer; Amarnath, Giriraj; Padalia, Hitendra; Kale, Manish; Nandy, Subrato; Jeganathan, C.; Singh, C. P.; Biradar, Chandrashekhar; Pattanaik, Chiranjibi; Singh, Dharmendra Kumar; Devagiri, G. M.; Talukdar, Gautam; Panigrahy, Rabindra K.; Singh, Harnam; Sharma, J. R.; Haridasan, K.; Trivedi, Shivam; Singh, Kumar Hemant; Kannan, L.; Daniel, Mc Inerney; Misra, Mandvi; Niphadkar, Madhura; Nagabhatla, Nidhi; Prasad, Nupoor; Tripathi, Om Prakash; Prasad, P. Rama Chandra; Dash, Pushpa; Qureshi, Qamer; Tripathi, S. K.; Ramesh, B.R.; Gowda, Balakrishnan; Tomar, Sanjay; Romshoo, Shakil Ahmad; Giriraj, Shilpa; Ravan, Shirish; Behera, Soumit K.; Paul, Subrato; Das, Ashesh Kumar; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, Uma; Menon, A. R. R.; Srivastava, Gaurav; Neeti, Neeti; Sharma, Subrat; Mohapatra, U. B.; Peddi, Ashok; Rashid, Irfan; Salroo, Irfan; Krishna, P. Hari; Hajra, P. K.; Varghese, A. O.; Matin, Shafique; Chaudhary, Swapnil A.; Ghosh, Sonali; Lakshmi, Udaya; Rawat, Deepshikha; Ambastha, Kalpana; Malik, Akhtar H.; Devi, B. Sarojini; Gowda, Balakrishna; Sharma, Kiran; Mukharjee, Prashant; Sharma, Ajay; Davidar, Priya; Ravikumar, Raju; Katewa, S. S.; Kant, Shashi; Raju, Vatsavaya S.; Uniyal, Bhumika; Debnath, Bijan; Rout, D. K.; Thapa, Rajesh; Joseph, Shijo; Chhetri, Pradeep; Ramachandran, Reshma M.

doi:10.1016/j.jag.2015.03.003

Cited by 155 publications

(88 citation statements)

References 32 publications

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“…However, the easternmost and western most grids were excluded from our analysis because of incompleteness (Figure 1 b-e). To analyse the distribution of species diversity, we used the plant diversity database generated in project entitled 'An Indian national biodiversity characterization at landscape level'; in consideration with the forest vegetation type distribution Images from Indian remote sensing satellites were utilized for vegetation-type delineation and mapping 11 . On-screen visual interpretation technique was adopted for vegetation and land-cover mapping, as it provided optimum accuracy (>90%) due to control over individual polygon delineations 11 .…”

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confidence: 99%

Plant Species Richness Pattern across India's Longest Longitudinal Extent

Roy

Panda

2016

Current Science

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Although the longitudinal pattern of biological diversity may not be as striking as the latitudinal pattern, there are climatic gradients associated with habitat, topography and disturbance, which may generate variation in biological diversity along a longitudinal extent. We analysed the pattern of plant diversity using field data gathered from a national level 'biodiversity characterization at landscape level' project, along the longest longitudinal extent of India, varying from desert to wet tropics. Across a 3 latitudinal band (25-27N lat.), the aridity decreased drastically from west to east, and the vegetation varied from tropical thorn, tropical deciduous, tropical semievergreen to tropical evergreen forests. In general, the species diversity is maximum in the east, minimum in the centre and intermediate in the west. The proportion (with respect to total number of species of each corresponding grid) of woody trees increased by half and shrubs increased by one-third, whereas herbal proportion decreased by half from western to eastern grid. The major predictor of species richness was the number of forest vegetation types, followed by topography (ruggedness index); together these explained 63% of the variance as revealed from generalized linear modelling analysis. The study concludes that, from west to east, (i) the vegetation type varies from tropical thorn to tropical moist/wet evergreen forest corresponding to aridity; (ii) there is monotonic drastic increase of woody tree, moderate increase of shrub, and drastic decrease of herb species, and (iii) plant diversity has a nonlinear distribution pattern attributed mainly to variation in the number of forest vegetation types followed by abiotic topography and climate. The study was possible due to the availability of national plant diversity database that followed a uniform field sampling design.

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confidence: 99%

Plant Species Richness Pattern across India's Longest Longitudinal Extent

Roy

Panda

2016

Current Science

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“…To make realistic changes, experiments with high-resolution regional climate models with real-time LULCC, in-situ and gridded soil moisture datasets are required [25,26] over Indian region. Nevertheless, the findings of this study using a regional climate model puts forward a fresh attempt to assess the impact of desertification and tropical deforestation on Indian monsoon meteorology.…”

Section: Discussionmentioning

confidence: 99%

Simulated Impact of Desertification and Deforestation on Indian Monsoon Rainfall and Surface Fluxes: RegCM4.0 Simulations

Lodh¹

2017

J Ecosyst Ecogr

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Impact of extensive desertification in northwest India, Himalayan glaciers depletion and tropical deforestation over Indian subcontinent and Southeast Asia, on Indian monsoon circulations, precipitation, surface fluxes is being studied. In this research paper, by changing vegetation types in the bats coupled -regCM4.0, the model impact of desertification and deforestation on Indian monsoons is investigated. By performing these sensitivity experiments (extended desertification, and tropical deforestation) it is found that over India, monsoon precipitation is significantly decreased at local and large scales. Decreased surface roughness length and increase in albedo because of desertification/deforestation in the model results in origination of anomalous westerly winds and subsidence, decreasing turbulent flow, decreasing rainfall over land and strengthening over the seas and consequently increases the temperature over land. Further, the hydrological and atmospheric water cycle gets weak because precipitation decreases. Thus any form of deforestation and desertification happening over tropical regions has a severe impact on Indian summer monsoon atmospheric circulations and precipitation. Lodh [9,10], studies impact of impact of afforestation and irrigation intensification on Indian monsoon precipitation and circulations, respectively. In this present research work attempts are made to study increase in desertification and deforestation in the RegCM4.0 model by proxy land use and land cover change induced sensitivity experiments, and to arrive at conclusions in addition to previous studies. Thus, identification of possible changes on regional precipitation and circulation patterns in the Indian monsoon regime through the sensitivity experiments is planned, using a regional climate model. Also, the mechanisms of interaction between land surface and vegetation (via desertification and deforestation) are to be understood. In this paper, the methodology, brief about the sensitivity experiments and their period of study is described in Section 2. "Result and Discussions" of the sensitivity experiment about effect of extensive desertification and deforestation is described in Section 3, and the "Summary and Conclusions" are described in Section 4. MethodologyThe model used in the present study is again ICTP regional climate model (RCM) installed over the Cordex South Asia domain (12°E to 138°E and 33°S to 55°N) at 90 km horizontal resolution [8,11]. The details of the parameterization schemes and domain of the regional climate model are described in the Lodh [10]. Brief about the desertification and deforestation sensitivity experiments and their period of studyThe desertification and deforestation sensitivity experiments are performed to study the critical impact of anthropogenic human activities by conducting landuse and land cover changes in the model, on Indian summer monsoon precipitation and circulation pattern (Figure 1). They are: (a) In the first (DEFOR EXP 1) sensitivity experiment the model landuse p...

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“…Total 15,565 ground control points were used to assess the accuracy of products available globally (i.e., GlobCover, Holdridge's life zone map and potential natural vegetation (PNV) maps). It is prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil [66].…”

Section: Indiamentioning

confidence: 99%

“…These maps have on uniqness i.e., these maps are of different temporal resolution cropping season, spatial resolution, spectral resolution, method of classification etc. The unified land cover map of India at spatial resolution of 23.5 m 2 by [66] in 2015. The satellite images used for north India, Wester-Ghats, central India etc.…”

Section: Nepalmentioning

confidence: 99%

“…Although remote sensing is advancing rapidly and cropland maps should improve accordingly, the quest for a more finely-detailed cropland map is a long-term solution and is not likely to provide improved products over the short-term. Although, 30 m 2 land cover maps are now starting to appear, their overall accuracy is still short of that required for food security applications [28,66,67]. Moreover, high resolution imagery may be needed to identify croplands but the final resolution of a useful global cropland extent map could still be medium resolution, such as 250 m 2 .…”

Section: Nepalmentioning

confidence: 99%

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A Review of the Available Land Cover and Cropland Maps for South Asia

Patil¹,

Gumma²

2018

Agriculture

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A lack of accuracy, uniqueness and the absence of systematic classification of cropland categories, together with long-pending updates of cropland mapping, are the primary challenges that need to be addressed in developing high-resolution cropland maps for south Asia. In this review, we analyzed the details of the available land cover and cropland maps of south Asia on national and regional scales in south Asia and on a global scale. Here, we highlighted the methodology adopted for classification, datasets used, classification system used for classifying different land covers and croplands and the resolution of datasets available. This listed review of different available datasets can help the reader to know which datasets to be used in their study and to understand which methodology to be chosen to further developing the accurate high-resolution land cover and cropland maps for advanced studies and for better understanding of ground reality in a timely updated version. We tried to identify the major concerns, particularly the inadequacy of knowledge regarding the spatial distribution of major crop types within south Asia, which hinder policy and strategic investment and delay the efforts to improve food security for a rapidly growing human population at a time of constant market instability and changing global climate. The overall focus of this paper is on reviewing the need for timely updated high-resolution cropland maps of south Asia.

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New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

Cited by 155 publications

References 32 publications

Plant Species Richness Pattern across India's Longest Longitudinal Extent

Plant Species Richness Pattern across India's Longest Longitudinal Extent

Simulated Impact of Desertification and Deforestation on Indian Monsoon Rainfall and Surface Fluxes: RegCM4.0 Simulations

A Review of the Available Land Cover and Cropland Maps for South Asia

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