Spatio-temporal assessment of vegetation health using satellite-based multi-spectral indicators in Hamirpur District of Uttar Pradesh, India
DOI:
https://doi.org/10.12775/bgeo-2026-0013Keywords
VHI, Remote Sensing, PCA, Weighted Overlay, BundelkhandAbstract
Vegetation health is an essential indicator to assess ecosystem stability, environmental sustainability and resistance to climatic stress, especially in semi-arid regions. In this study, the spatio-temporal variation of vegetation health was evaluated between 2015 and 2025 in Hamirpur District of Uttar Pradesh, India, using multi-spectral remotely sensed data. Seven satellite-derived parameters, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Red Edge Index (NDRE), Chlorophyll Index (CI) and Land Surface Temperature (LST), were used to assess the vegetation health. The Principal Component Analysis (PCA) was applied to weight the indicators objectively and a GIS-based weighted overlay method was used to create a composite Vegetation Health Index (VHI). The first principal component accounted for 86.69% of the total variance. The result shows a significant improvement in the vegetation condition, with a mean VHI of 0.331 in 2015, 0.543 in 2020, and 0.633 in 2025. There was a significant decrease in Very Poor (-58.96%) and Poor (-24.25%) vegetation health classes, while there was a remarkable increase in Healthy (+175.60%) and Very Healthy (+310.09%). Seasonal analysis also showed strong resilience of vegetation, especially during winter and post-monsoon. The VHI model was well validated with the secondary data sources. The results show the potential of multispectral, red-edge, moisture and thermal data for vegetation health monitoring and offer valuable knowledge for ecosystem management, climate change adaptation and sustainable environmental planning in semi-arid environments.
References
ABDI H and WILLIAMS LJ, 2010, Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2(4): 433-459. DOI: https://doi.org/10.1002/wics.101.
AFSHARI S, SARLI R, ALCHIN AA, ALIABAD OG, MORADI F, SAEI M, LOMER ARB and NASIRI V, 2025, Trend analysis and interactions between surface temperature and vegetation condition: divergent responses across vegetation types. Environmental Monitoring and Assessment 197(3): 292. DOI: https://doi.org/10.1007/s10661-025-13729-9.
AHLSTRÖM A, XIA J, ARNETH A, LUO Y and SMITH B, 2015, Importance of vegetation dynamics for future terrestrial carbon cycling. Environmental Research Letters 10(5): 054019. DOI: https://doi.org/10.1088/1748-9326/10/5/054019.
ALQADHI S, MALLICK J and HANG HT, 2025, Assessing drought trends and vegetation health in arid regions using advanced remote sensing techniques: a case study in Saudi Arabia. Theoretical and Applied Climatology 156(1): 33. DOI: https://doi.org/10.1007/s00704-024-05301-1.
AMBIKA AK and MISHRA V, 2019, Observational evidence of irrigation influence on vegetation health and land surface temperature in India. Geophysical Research Letters 46(22): 13441-13451. DOI: https://doi.org/10.1029/2019GL084367.
ASHRAF A, HAROON MA, AHMAD S, ABOWARDA AS, WEI C and LIU X, 2023, Use of remote sensing-based pressure-state-response framework for the spatial ecosystem health assessment in Langfang, China. Environmental Science and Pollution Research 30(38): 89395-89414. DOI: https://doi.org/10.1007/s11356-023-28674-8.
BABBAR D, AREENDRAN G, SAHANA M, SARMA K, RAJ K and SIVADAS A, 2021, Assessment and prediction of carbon sequestration using Markov chain and InVEST model in Sariska Tiger Reserve, India. Journal of Cleaner Production 278: 123333. DOI: https://doi.org/10.1016/j.jclepro.2020.123333.
BARBOSA HA, LAKSHMI KUMAR TV and SILVA LRM, 2015, Recent trends in vegetation dynamics in the South America and their relationship to rainfall. Natural Hazards 77(2): 883-899. DOI: https://doi.org/10.1007/s11069-015-1635-8.
BERA B, SAHA S and BHATTACHARJEE S, 2020, Estimation of forest canopy cover and forest fragmentation mapping using Landsat satellite data of Silabati River Basin (India). KN - Journal of Cartography and Geographic Information 70(4): 181-197. DOI: https://doi.org/10.1007/s42489-020-00060-1.
BINTE MOSTAFIZ R, NOGUCHI R and AHAMED T, 2021, Agricultural land suitability assessment using satellite remote sensing-derived soil-vegetation indices. Land 10(2): 1-26. DOI: https://doi.org/10.3390/land10020223.
BRO R and SMILDE AK, 2014, Principal component analysis. Analytical Methods 6(9): 2812-2831. DOI: https://doi.org/10.1039/c3ay41907j.
CABELLO-SOLORZANO K, ORTIGOSA DE ARAUJO I, PEÑA M, CORREIA L and J. TALLÓN-BALLESTEROS A, 2023, The impact of data normalization on the accuracy of machine learning algorithms: a comparative analysis. In: Proceedings (pp. 344-353). DOI: https://doi.org/10.1007/978-3-031-42536-3_33.
CHACKO S, KURIAN J, RAVICHANDRAN C, VAIRAVEL SM and KUMAR K, 2021, An assessment of water yield ecosystem services in Periyar Tiger Reserve, Southern Western Ghats of India. Geology, Ecology, and Landscapes 5(1): 32-39. DOI: https://doi.org/10.1080/24749508.2019.1699988.
CHAUHAN P, NGANGOM M and THAKKAR MG, 2025, Hydroclimate dynamics and their impact on vegetation health in the Luni River Basin, Western India: a multi-index assessment using remote sensing. Modeling Earth Systems and Environment 11(3): 162. DOI: https://doi.org/10.1007/s40808-025-02339-6.
CHOWDHURY M, ANAND R, DHAR T, KURMI R, SAHNI RK and KUSHWAH A, 2024, Digital insights into plant health: exploring vegetation indices through computer vision. In: Applications of Computer Vision and Drone Technology in Agriculture 4.0 (pp. 7-30). Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-99-8684-2_2.
DAGLIO G, CESARO P, TODESCHINI V, LINGUA G, LAZZARI M, BERTA G and MASSA N, 2022, Potential field detection of Flavescence dorée and Esca diseases using a ground sensing optical system. Biosystems Engineering 215: 203-214. DOI: https://doi.org/10.1016/j.biosystemseng.2022.01.009.
DAPKE PP, NAGARE SM, QUADRI SA, BANDAL SB, GAIKWAD RM and BAHETI MR, 2025, Seasonal analysis of vegetation, moisture, urbanization, and land surface temperature (LST) using NDVI, NDMI, NDWI, and NDBI indices: a case study of Sillod, Maharashtra. In: 2025 International Conference on Computational, Communication and Information Technology (ICCCIT): 753-760. DOI: https://doi.org/10.1109/ICCCIT62592.2025.10928110.
DAS S, BHUNIA GS, BERA B and SHIT PK, 2022, Evaluation of wetland ecosystem health using geospatial technology: evidence from the lower Gangetic flood plain in India. Environmental Science and Pollution Research 29(2): 1858-1874. DOI: https://doi.org/10.1007/s11356-021-15674-9.
DUBEY AK, RIZVI FF and TIWARI A, 2025, Gauging land surface temperatures with changes in land use land cover: a spatiotemporal analysis of Gaya City, Bihar. Singapore Journal of Tropical Geography 46(3): 386-409. DOI: https://doi.org/10.1111/sjtg.70019.
DUTTA S, REHMAN S, SAHANA M and SAJJAD H, 2021, Assessing forest health using Geographical Information System based analytical hierarchy process: evidences from Southern West Bengal, India (pp. 71-102). DOI: https://doi.org/10.1007/978-3-030-56542-8_3.
GIDEY E, DIKINYA O, SEBEGO R, SEGOSEBE E and ZENEBE A, 2018, Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environmental Systems Research 7(1): 13. DOI: https://doi.org/10.1186/s40068-018-0115-z.
GLENN EP, HUETE AR, NAGLER PL and NELSON SG, 2008, Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8(4): 2136-2160. DOI: https://doi.org/10.3390/s8042136.
GREEN JK, 2024, The intricacies of vegetation responses to changing moisture conditions. New Phytologist 244(6): 2156-2162. DOI: https://doi.org/10.1111/nph.20182.
GREENACRE M, GROENEN PJF, HASTIE T, D’ENZA AI, MARKOS A and TUZHILINA E, 2022, Principal component analysis. Nature Reviews Methods Primers 2(1). DOI: https://doi.org/10.1038/s43586-022-00184-w.
GREENACRE M, GROENEN PJF, HASTIE T, IODICE A, MARKOS A and TUZHILINA E, 2023, Principal component analysis (No. 1856).
HAN D, HUANG J, DING L, ZHANG G, LIU X, LI C and YANG F, 2022, Breaking the ecosystem balance over the Tibetan Plateau. Earth’s Future 10(10). DOI: https://doi.org/10.1029/2022EF002890.
HAQ SM, WAHEED M, KHOJA AA, AMJAD MS, BUSSMANN RW, ALI K and JONES DA, 2023, Measuring forest health at stand level: a multi-indicator evaluation for use in adaptive management and policy. Ecological Indicators 150: 110225. DOI: https://doi.org/10.1016/j.ecolind.2023.110225.
HUANG S, TANG L, HUPY JP, WANG Y and SHAO G, 2021, A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32(1): 1-6. DOI: https://doi.org/10.1007/s11676-020-01155-1.
HUANG X, XIAO J and MA M, 2019, Evaluating the performance of satellite-derived vegetation indices for estimating gross primary productivity using FLUXNET observations across the globe. Remote Sensing 11(15): 1823. DOI: https://doi.org/10.3390/rs11151823.
KARIMI H, AMIRI S, HUANG J and KARIMI A, 2019, Integrating GIS and multi-criteria decision analysis for landfill site selection, case study: Javanrood County in Iran. International Journal of Environmental Science and Technology 16(11): 7305-7318. DOI: https://doi.org/10.1007/s13762-018-2151-7.
KHARAT MG, KAMBLE SJ, RAUT RD and KAMBLE SS, 2016, Identification and evaluation of landfill site selection criteria using a hybrid Fuzzy Delphi, Fuzzy AHP and DEMATEL based approach. Modeling Earth Systems and Environment 2(2): 1-13. DOI: https://doi.org/10.1007/s40808-016-0171-1.
KRISBIANTORO D, WALUYO R, HASANAH U, PRATAMA I and SARMINI S, 2024, Classification of air pollutant index on data with outliers and imbalance class problem. JOIV: International Journal on Informatics Visualization 8(3): 1049. DOI: https://doi.org/10.62527/joiv.8.3.1993.
KUMAR S, GHOSH SS, MANDAL D, BHATTACHARYA A, PORWAL A and KARTHIKEYAN L, 2025, Enhancing vegetation monitoring: a proposal for a Sentinel-2 based vegetation health index. Frontiers in Remote Sensing 6. DOI: https://doi.org/10.3389/frsen.2025.1581355.
KUNDU A, DWIVEDI S and DUTTA D, 2016, Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices. Arabian Journal of Geosciences 9(2): 144. DOI: https://doi.org/10.1007/s12517-015-2185-9.
KUREEL N, SARUP J, MATIN S, GOSWAMI S and KUREEL K, 2022, Modelling vegetation health and stress using hyperspectral remote sensing data. Modeling Earth Systems and Environment 8(1): 733-748. DOI: https://doi.org/10.1007/s40808-021-01113-8.
LAUSCH A, BASTIAN O, KLOTZ S, LEITÃO PJ, JUNG A, ROCCHINI D, SCHAEPMAN ME, SKIDMORE AK, TISCHENDORF L and KNAPP S, 2018, Understanding and assessing vegetation health by in situ species and remote-sensing approaches. Methods in Ecology and Evolution 9(8): 1799-1809. DOI: https://doi.org/10.1111/2041-210X.13025.
LI L, XIN X, ZHAO J, YANG A, WU S, ZHANG H and YU S, 2023, Remote sensing monitoring and assessment of global vegetation status and changes during 2016-2020. Sensors 23(20): 8452. DOI: https://doi.org/10.3390/s23208452.
LIU L, GUDMUNDSSON L, HAUSER M, QIN D, LI S and SENEVIRATNE SI, 2020, Soil moisture dominates dryness stress on ecosystem production globally. Nature Communications 11(1): 4892. DOI: https://doi.org/10.1038/s41467-020-18631-1.
MAHAJAN U and BUNDEL BR, 2016, Drones for Normalized Difference Vegetation Index (NDVI), to estimate crop health for precision agriculture: a cheaper alternative for spatial satellite sensors. In: International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016): 38-41.
MAHATA B, SANKAR SAHU S, SARDAR A, LAXMIKANTA R and MAITY M, 2024, Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: a case study in New Town Kolkata, eastern India. Regional Sustainability 5(2). DOI: https://doi.org/10.1016/j.regsus.2024.100138.
MAHMOODY VANOLYA N and JELOKHANI-NIARAKI M, 2021, The use of subjective-objective weights in GIS-based multi-criteria decision analysis for flood hazard assessment: a case study in Mazandaran, Iran. GeoJournal 86(1): 379-398. DOI: https://doi.org/10.1007/s10708-019-10075-5.
MAIGNAN F, BRÉON F-M, BACOUR C, DEMARTY J and POIRSON A, 2008, Interannual vegetation phenology estimates from global AVHRR measurements. Remote Sensing of Environment 112(2): 496-505. DOI: https://doi.org/10.1016/j.rse.2007.05.011.
MEHMOOD K, ANEES SA, MUHAMMAD S, HUSSAIN K, SHAHZAD F, LIU Q, ANSARI MJ, ALHARBI SA and KHAN WR, 2024, Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports 14(1): 11775. DOI: https://doi.org/10.1038/s41598-024-62464-7.
MORRISON W, GRIMMOND S and KOTTHAUS S, 2023, Simulating satellite urban land surface temperatures: sensitivity to sensor view angle and assumed landscape complexity. Remote Sensing of Environment 293(April): 113579. DOI: https://doi.org/10.1016/j.rse.2023.113579.
NSE OU, OKOLIE CJ and NSE VO, 2020, Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria. Scientific African 10: e00599. DOI: https://doi.org/10.1016/j.sciaf.2020.e00599.
NZABARINDA V, BAO A, XU W, UWAMAHORO S, UDAHOGORA M, UMWALI ED, NYIRARWASA A and UMUHOZA J, 2021, A spatial and temporal assessment of vegetation greening and precipitation changes for monitoring vegetation dynamics in climate zones over Africa. ISPRS International Journal of Geo-Information 10(3): 129. DOI: https://doi.org/10.3390/ijgi10030129.
PÉREZ-AGUILAR LY, LÓPEZ-OSORIO RF, ZAMBRANO-MEDINA YG, AVILA-ACEVES E, MONJARDIN-ARMENTA SA, PLATA-ROCHA W, QUINTERO-MORALES MA, MORA-RAMÍREZ MA and BERNAL-GUADIANA R, 2026, Soil degradation: causes, impacts, and mitigation strategies in the context of climate change (pp. 3-36). DOI: https://doi.org/10.1007/978-3-032-00704-9_1.
PORPORATO A, LAIO F, RIDOLFI L and RODRIGUEZ-ITURBE I, 2001, Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress. Advances in Water Resources 24(7): 725-744. DOI: https://doi.org/10.1016/S0309-1708(01)00006-9.
QAMAR F, SHARMA MS and DOBLER G, 2022, The impacts of air quality on vegetation health in dense urban environments: a ground-based hyperspectral imaging approach. Remote Sensing 14(16): 3854. DOI: https://doi.org/10.3390/rs14163854.
RAO MCS, UDAYANA SK and SWAMI DV, 2023, Achieving land degradation neutrality to combat the impacts of climate change. In: Enhancing Resilience of Dryland Agriculture Under Changing Climate (pp. 77-96). Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-19-9159-2_5.
RATHORE S, AHMAD SR and SHIRAZI SA, 2016, Use of the suitability model to identify landfill sites in Lahore-Pakistan. Journal of Basic & Applied Sciences 12: 103-108. DOI: https://doi.org/10.6000/1927-5129.2016.12.16.
SAHAK AS, KARSLI F, GORMUS ET and AHMADI K, 2023, Seasonal monitoring of urban heat island based on the relationship between land surface temperature and land use/cover: a case study of Kabul City, Afghanistan. Earth Science Informatics 16(1): 845-861. DOI: https://doi.org/10.1007/s12145-022-00918-0.
SAMSUDDIN N, SALLEH SA, KHALID N, MOHAMED SARAF N, YAMAN R, PINTOR L, RUEDA G and SAMSUDDIN N, 2025, Assessing the Heat Vulnerability Index (HVI) in Klang Valley using Principal Component Analysis (PCA) and an equal-weighted approach. Forum Geografi 39(2): 188-208. DOI: https://doi.org/10.23917/forgeo.v39i2.8633.
SINGH R, SARITHA V and PANDE CB, 2024a, Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India. Environmental Research 241(November 2023): 117638. DOI: https://doi.org/10.1016/j.envres.2023.117638.
SINGH R, SARITHA V and PANDE CB, 2024b, Dynamics of LULC changes, LST, vegetation health and climate interactions in Wetland buffer zone: a remote sensing perspective. Physics and Chemistry of the Earth, Parts A/B/C 135: 103660. DOI: https://doi.org/10.1016/j.pce.2024.103660.
TAN Q and XU X, 2014, Comparative analysis of spatial interpolation methods: an experimental study. Sensors and Transducers 165(2): 155-163.
VAIPHASA C, PIAMDUAYTHAM S, VAIPHASA T and SKIDMORE AK, 2011, A normalized difference vegetation index (NDVI) time-series of idle agriculture lands: a preliminary study. Engineering Journal 15(1): 9-16. DOI: https://doi.org/10.4186/ej.2011.15.1.9.
VAROUCHAKIS EA, KOMNITSAS K and GALETAKIS M, 2025, Spatiotemporal analysis of vegetation health and moisture dynamics in rehabilitated mining quarries using satellite imagery. Environmental Processes 12(3): 45. DOI: https://doi.org/10.1007/s40710-025-00781-3.
WANG Z, YANG C, YANG L, ZHOU Z, RAO J, YUAN L and LI J, 2007, Relationship and its ecological significance between plant species diversity and ecosystem function of soil conservation in semi-humid evergreen forests, Yunnan Province, China. Frontiers of Forestry in China 2(1): 18-27. DOI: https://doi.org/10.1007/s11461-007-0003-6.
YANG X, LI X, WANG X, DING F, CHEN F, WANG J, ZHANG X and ZHANG Y, 2022, Meta-analysis of the correlation between vegetation and precipitation in the temperate deserts of the Northern Hemisphere over the last 40 years. Ecological Indicators 142: 109269. DOI: https://doi.org/10.1016/j.ecolind.2022.109269.
YIN F, LEWIS PE and GÓMEZ-DANS JL, 2022, Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI. Geoscientific Model Development 15(21): 7933-7976. DOI: https://doi.org/10.5194/gmd-15-7933-2022.
ZENG Y, HAO D, HUETE A, DECHANT B, BERRY J, CHEN JM, JOINER J, FRANKENBERG C, BOND-LAMBERTY B, RYU Y, XIAO J, ASRAR GR and CHEN M, 2022, Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment 3(7): 477-493. DOI: https://doi.org/10.1038/s43017-022-00298-5.
ZHOU W, LI C, WANG S, REN Z and STRINGER LC, 2023, Effects of vegetation restoration on soil properties and vegetation attributes in the arid and semi-arid regions of China. Journal of Environmental Management 343: 118186. DOI: https://doi.org/10.1016/j.jenvman.2023.118186.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Teerath Raaj, Alok Kumar Dubey, Arti Vishnoi
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Stats
Number of views and downloads: 11
Number of citations: 0
