Akram, M., Hayat, U., Shi, J. & Anees, S. A. Association of the female flight ability of Asian spongy moths (Lymantria dispar asiatica) with locality, age and mating: A case study from China. Forests 13 (8), 1158. https://doi.org/10.3390/f13081158 (2022).
Andreevich, U. V. et al. Are there differences in the response of natural stand and plantation biomass to changes in temperature and precipitation? A case for two-needled Pines in Eurasia. J. Resour. Ecol. 11 (4), 331. https://doi.org/10.5814/j.issn.1674-764x.2020.04.001 (2020).
Martinez, M. et al. Integrating remote sensing with Ground-based observations to quantify the effects of an extreme freeze event on black mangroves (Avicennia germinans) at the landscape scale. Ecosystems 27. (2024). https://doi.org/10.1007/s10021-023-00871-z
Datta, A. et al. Big events, little change: extreme Climatic events have no region-wide effect on great barrier reef governance. J. Environ. Manage. 320. https://doi.org/10.1016/j.jenvman.2022.115809 (2022).
França, F. M. et al. Climatic and local stressor interactions threaten tropical forests and coral reefs. Philosophical Trans. Royal Soc. B: Biol. Sci. https://doi.org/10.1098/rstb.2019.0116 (2020).
Anees, S. A. et al. Estimation of fractional vegetation cover dynamics based on satellite remote sensing in pakistan: a comprehensive study on the FVC and its drivers. J. King Saud University-Science. 34 (3), 101848. https://doi.org/10.1016/j.jksus.2022.101848 (2022b).
Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. Nat. Clim. Chang. 12 https://doi.org/10.1038/s41558-022-01352-2 (2022).
Zhang, Y. et al. Spatial heterogeneity of vegetation resilience changes to different drought types. Earths Future. 11 https://doi.org/10.1029/2022EF003108 (2023).
Anees, S. A. et al. Estimation of fractional vegetation cover dynamics and its drivers based on multi-sensor data in dera Ismail khan, Pakistan. J. King Saud University-Science. 34 (6), 102217. https://doi.org/10.1016/j.jksus.2022.102217 (2022a).
Hussain, T., Hussain, N., Ahmed, M., Nualsri, C. & Duangpan, S. Responses of lowland rice genotypes under terminal water stress and identification of drought tolerance to stabilize rice productivity in Southern Thailand. Plants 10 https://doi.org/10.3390/plants10122565 (2021).
Power, K., Barnett, J., Dickinson, T. & Axelsson, J. The role of El Niño in driving drought conditions over the last 2000 years in Thailand. Quaternary 3 https://doi.org/10.3390/quat3020018 (2020).
Tsai, C. L., Behera, S. K. & Waseda, T. Indo-China monsoon indices. Sci. Rep. 5 https://doi.org/10.1038/srep08107 (2015).
Mehmood, K. et al. Assessing Chilgoza Pine (Pinus gerardiana) Forest Fire Severity: Remote Sensing Analysis, Correlations, and Predictive Modeling for Enhanced Management Strategies. Trees, Forests and People, p.100521. (2024). https://doi.org/10.1016/j.tfp.2024.100521
Aslam, M. S. et al. Assessment of Major Food Crops production-based Environmental Efficiency in China, India, and Pakistanpp.1–10 (Environmental Science and Pollution Research, 2022). https://doi.org/10.1007/s11356-021-16161-x
Hou, X. et al. Response of vegetation productivity to greening and drought in the loess plateau based on vis and SIF. Forests 15 https://doi.org/10.3390/f15020339 (2024).
Mehmood, K. et al. Analyzing vegetation health dynamics across seasons and regions through NDVI and Climatic variables. Sci. Rep. 14 (1), 11775. https://doi.org/10.1038/s41598-024-62464-7 (2024b).
Tschumi, E., Lienert, S., Van Der Wiel, K., Joos, F. & Zscheischler, J. The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition. Biogeosciences 19 https://doi.org/10.5194/bg-19-1979-2022 (2022).
Xiao, C., Zaehle, S., Yang, H., Wigneron, J. P. & Bastos, A. Land-cover and management modulation of ecosystem resistance to drought stress. EGUsphere 2023. (2023).
Anees, S. A. et al. Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors. Earth Sci. Inf. 18, 152. https://doi.org/10.1007/s12145-024-01673-0 (2025).
Anees, S. A. et al. Integration of machine learning and remote sensing for above ground biomass Estimation through Landsat-9 and field data in temperate forests of the Himalayan region. Ecol. Inf. 102732. https://doi.org/10.1016/j.ecoinf.2024.102732 (2024).
Mehmood, K. et al. Exploring Spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: insights for sustainable management and climate resilience. Ecol. Inf. 102532. https://doi.org/10.1016/j.ecoinf.2024.102532 (2024).
Anees, S. A. et al. Unveiling Fractional Vegetation Cover Dynamics: A Spatiotemporal Analysis Using MODIS NDVI and Machine Learning. Environmental and Sustainability Indicators, p.100485. (2024). https://doi.org/10.1016/j.indic.2024.100485
Mehmood, K. et al. Assessment of Climatic Influences on Net Primary Productivity along Elevation Gradients in Temperate Ecoregions. Trees, Forests and People, p.100657. (2024)c.
Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).
Dias, T. C., Silveira, L. F., Pironkova, Z. I. & Francisco, M. R. Greening and Browning trends in a tropical forest hotspot: accounting for fragment size and vegetation indices. Remote Sens. Appl. 26 https://doi.org/10.1016/j.rsase.2022.100751 (2022).
KARTAL, S., IBAN, M. C. & SEKERTEKIN, A. Next-level vegetation health index forecasting: A ConvLSTM study using MODIS time series. Environ. Sci. Pollut. Res. 31 https://doi.org/10.1007/s11356-024-32430-x (2024).
Thavorntam, W. & Shahnawaz Evaluation of drought in the North of Thailand using meteorological and Satellite-Based drought indices. Int. J. Geoinformatics. 18 https://doi.org/10.52939/ijg.v18i5.2367 (2022).
Almouctar, M. A. S., Wu, Y., Zhao, F. & Qin, C. Drought analysis using normalized difference vegetation index and land surface temperature over Niamey region, the Southwestern of the Niger between 2013 and 2019. J. Hydrol. Reg. Stud. https://doi.org/10.1016/j.ejrh.2024.101689 (2024).
Zhang, S., Li, J., Zhang, T., Feng, P. & Liu, W. Response of vegetation to SPI and driving factors in Chinese Mainland. Agric. Water Manag. 291 https://doi.org/10.1016/j.agwat.2023.108625 (2024).
Alamdarloo, E. H., Manesh, M. B. & Khosravi, H. Probability assessment of vegetation vulnerability to drought based on remote sensing data. Environ. Monit. Assess. 190 https://doi.org/10.1007/s10661-018-7089-1 (2018).
Shahfahad, Talukdar, S. et al. Monitoring drought pattern for pre- and post-monsoon seasons in a semi-arid region of Western part of India. Environ. Monit. Assess. 194. https://doi.org/10.1007/s10661-022-10028-5 (2022).
Ekundayo, O. Y., Okogbue, E. C., Akinluyi, F. O., Kalumba, A. M. & Orimoloye, I. R. Spatiotemporal drought assessment using vegetation health index and standardized precipitation index over Sudano-Sahelian region of Nigeria. Afr. Geographical Rev. 40 https://doi.org/10.1080/19376812.2020.1841658 (2021).
Ghaleb, F., Mario, M. & Sandra, A. N. Regional landsat-based drought monitoring from 1982 to 2014. Climate 3. (2015). https://doi.org/10.3390/cli3030563
Gupta, S. K. et al. Impact of Topographical and Hydrological Parameters on the Urban Health of Jaipur City (Current Opinion in Environment and Health Science, 2024).
Roy, P. R. et al. Spatial Data Modelling of Atmospheric Water Availability and Stress in Jharkhand, India (Discover Civil Engineering, 2024).
Gupta, S. K. et al. Optimizing land use for climate mitigation using nature-based solution (NBS) strategy: a study on afforestation potential and carbon sequestration in rajasthan, India. Discover Geosci. 2 (36), SCI. https://doi.org/10.1007/s44288-024-00046-w (2024).
Gupta, S. K. et al. Unearthing india’s soil moisture anomalies: impact on agriculture and water resource strategies. Theoret. Appl. Climatol. 155 (7575–7590). https://doi.org/10.1007/s00704-024-05088-1 (2024). SCOPUS, Impact Factor: 5.8.
Gupta, S. K., Kanga, S. & Singh, S. K. Assessment of aquatic ecosystem quality in Dharoi reservoir using Sentinel-2 satellite imagery. Nat. Resour. Conserv. Res. 7 (1), SCI. https://doi.org/10.24294/nrcr.v7i1.4477 (2024).
Anees, S. A. et al. Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities. Ecological Informatics, p.102986. (2024). https://doi.org/10.1016/j.ecoinf.2024.102986
Mehmood, K. et al. Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management. J. Forestry Res. 35 (1), 82. https://doi.org/10.1007/s11676-024-01734-6 (2024d).
Shahzad, F. et al. Advancing forest fire prediction: A multi-layer stacking ensemble model approach. Earth Sci. Inf. 18 (3), 270. https://doi.org/10.1007/s12145-025-01782-4 (2025).
Mehmood, K. et al. Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project. Ecology and Evolution, 15(2), p.e70736. (2025). https://doi.org/10.1002/ece3.70736
Luo, M. et al. Improving Forest Above-Ground Biomass Estimation by Integrating Individual Mach. Learn. Models Forests, 15(6), 975. https://doi.org/10.3390/f15060975 (2024).
Pan, S. A. et al. Spatial and Temporal patterns of Non-Structural carbohydrates in Faxon Fir (Abies fargesii var. faxoniana), Subalpine Mountains of Southwest China. Forests 14 (7), 1438. https://doi.org/10.3390/f14071438 (2023).
Pan, S. A., Anees, S. A., Yang, X. & Mehmood, K. The stoichiometric characteristics and the relationship with hydraulic and morphological traits of the Faxon Fir in the subalpine coniferous forest of Southwest China. Ecol. Ind. 159, 111636. https://doi.org/10.1016/j.ecolind.2024.111636 (2024).
Chaiyana, A. et al. Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thail. Sustain. (Switzerland) 16. https://doi.org/10.3390/su16062260 (2024).
Khan, R. & Gilani, H. Global drought monitoring with big Geospatial datasets using Google Earth engine. Environ. Sci. Pollut. Res. 28 https://doi.org/10.1007/s11356-020-12023-0 (2021).
Abera, T. A., Heiskanen, J., Pellikka, P. & Maeda, E. E. Rainfall–vegetation interaction regulates temperature anomalies during extreme dry events in the Horn of Africa. Glob Planet. Change. 167. https://doi.org/10.1016/j.gloplacha.2018.05.002 (2018).
Hua, L. et al. Mapping the spatial-temporal dynamics of vegetation response lag to drought in a semi-arid region. Remote Sens. (Basel). 11. https://doi.org/10.3390/rs11161873 (2019).
Olthof, I. & Latifovic, R. Short-term response of Arctic vegetation NDVI to temperature anomalies. Int. J. Remote Sens. 28 https://doi.org/10.1080/01431160701268996 (2007).
Zeri, M. et al. Importance of including soil moisture in drought monitoring over the Brazilian semiarid region: an evaluation using the JULES model, in situ observations, and remote sensing. Clim. Resil. Sustain. 1 https://doi.org/10.1002/cli2.7 (2022).
Zhang, P., Xiao, P., Yao, W., Liu, G. & Sun, W. Profile distribution of soil moisture response to precipitation on the Pisha sandstone hillslopes of China. Sci. Rep. 10 https://doi.org/10.1038/s41598-020-65829-w (2020).
Bourbia, I., Lucani, C., Carins-Murphy, M. R., Gracie, A. & Brodribb, T. J. In situ characterisation of whole-plant stomatal responses to VPD using leaf optical dendrometry. Plant. Cell. Environ. 46. https://doi.org/10.1111/pce.14658 (2023).
Liu, Y., Kumar, M., Katul, G. G., Feng, X. & Konings, A. G. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat. Clim. Chang. 10 https://doi.org/10.1038/s41558-020-0781-5 (2020).
Arthur Endsley, K., Kimball, J. S., Reichle, R. H. & Watts, J. D. Satellite monitoring of global surface soil organic carbon dynamics using the SMAP level 4 carbon product. J. Geophys. Res. Biogeosci. 125. https://doi.org/10.1029/2020JG006100 (2020).
Beall, K., Loisel, J. & Medina-Cetina, Z. PermaBN: A bayesian network framework to help predict permafrost thaw in the Arctic. Ecol. Inf. 69 https://doi.org/10.1016/j.ecoinf.2022.101601 (2022).
Yegizbayeva, A. et al. Satellite-based drought assessment in the endorheic basin of lake Balkhash. Front. Environ. Sci. 11. https://doi.org/10.3389/fenvs.2023.1291993 (2023).
Trisurat, Y., Shirakawa, H. & Johnston, J. M. Land-use/land-cover change from socio-economic drivers and their impact on biodiversity in Nan province, Thailand. Sustain. (Switzerland). 11. https://doi.org/10.3390/su11030649 (2019).
Jiang, L., Liu, B. & Yuan, Y. Quantifying vegetation vulnerability to climate variability in China. Remote Sens. (Basel). 14. https://doi.org/10.3390/rs14143491 (2022).
Singh, B. et al. Resilience of the central Indian forest ecosystem to rainfall variability in the context of a changing climate. Remote Sens. (Basel). 13. https://doi.org/10.3390/rs13214474 (2021).
Arnaud, M., Baird, A. J., Morris, P. J., Dang, T. H. & Nguyen, T. T. Sensitivity of Mangrove soil organic matter decay to warming and sea level change. Glob Chang. Biol. 26. https://doi.org/10.1111/gcb.14931 (2020).
Etemadi, H., Smoak, J. M. & Abbasi, E. Spatiotemporal pattern of degradation in arid Mangrove forests of the Northern Persian Gulf. Oceanologia 63. (2021). https://doi.org/10.1016/j.oceano.2020.10.003
Perri, S., Detto, M., Porporato, A. & Molini, A. Salinity-induced limits to Mangrove canopy height. Glob. Ecol. Biogeogr. 32 https://doi.org/10.1111/geb.13720 (2023).
Khan, W.R., Nazre, M., Akram, S., Anees, S.A., Mehmood, K., Ibrahim, F.H., … Zhu,X. (2024). Assessing the Productivity of the Matang Mangrove Forest Reserve: Review of One of the Best-Managed Mangrove Forests. Forests, 15 (5), 747. https://doi.org/10.3390/f15050747.
Khan, W. R. et al. Phytoextraction Potential of Rhizophora Apiculata: A Case Study in Matang Mangrove Forest Reserve, Malaysia13p.1940082920947344 (Tropical Conservation Science, 2020).
Amnuaylojaroen, T. Projection of the precipitation extremes in Thailand under climate change scenario RCP8.5. Front. Environ. Sci. 9 https://doi.org/10.3389/fenvs.2021.657810 (2021).
Promping, T. & Tingsanchali, T. Meteorological Drought Hazard Assessment under Future Climate Change Projection for Agriculture Area in Songkhram River Basin, Thailand, in: Proceedings of the 2020 International Conference and Utility Exhibition on Energy, Environment and Climate Change, ICUE 2020. (2020). https://doi.org/10.1109/ICUE49301.2020.9307085
Arunrat, N., Sereenonchai, S., Chaowiwat, W. & Wang, C. Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand. Sci. Total Environ. 807 https://doi.org/10.1016/j.scitotenv.2021.150741 (2022).
Marks, D. Climate change and thailand: impact and response. Contemp. Southeast. Asia. 33 https://doi.org/10.1355/cs33-2d (2011).
Pipitpukdee, S., Attavanich, W. & Bejranonda, S. Climate change impacts on sugarcane production in Thailand. Atmos. (Basel). 11. https://doi.org/10.3390/ATMOS11040408 (2020).
Zhai, F. & Zhuang, J. Agricultural impact of climate change: A general equilibrium analysis with special reference to Southeast Asia, in: Climate Change in Asia and the Pacific: How Can Countries Adapt? (2012). https://doi.org/10.4135/9788132114000.n3
Lestari, S. D., Leon, F. M., Riyadi, S. & Qodariah, Putra, A. H. P. K. Comparison and implementation of environmental law policies in handling climate change in ASEAN countries: A comparative study of indonesia, malaysia, and Thailand. Int. J. Energy Econ. Policy. 14 https://doi.org/10.32479/ijeep.14998 (2024).
Limsakul, A., Kammuang, A., Paengkaew, W., Sooktawee, S. & Aroonchan, N. Changes in slow-onset climate events in Thailand. Environ. Eng. Res. 29. https://doi.org/10.4491/eer.2022.784 (2024).
Wongpanarak, N. & Langkulsen, U. Climate change and mental health in Northeast of Thailand. Int. J. Environ. Health Res. https://doi.org/10.1080/09603123.2024.2328741 (2024).
Ermida, S. L., Soares, P., Mantas, V., Göttsche, F. M. & Trigo, I. F. Google Earth engine open-source code for land surface temperature Estimation from the Landsat series. Remote Sens. (Basel). 12, 1471 (2020).
He, Y., Wang, L., Niu, Z. & Nath, B. Vegetation recovery and recent degradation in different karst landforms of Southwest China over the past two decades using GEE satellite archives. Ecol. Inf. 68 https://doi.org/10.1016/j.ecoinf.2022.101555 (2022).
Du, H. et al. Evaluating the effectiveness of CHIRPS data for hydroclimatic studies. Theor. Appl. Climatol. https://doi.org/10.1007/s00704-023-04721-9 (2024b).
Shen, Z. et al. Recent global performance of the climate hazards group infrared precipitation (CHIRP) with stations (CHIRPS). J. Hydrol. (Amst). 591. https://doi.org/10.1016/j.jhydrol.2020.125284 (2020).
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191. https://doi.org/10.1038/sdata.2017.191 (2018).
TerraClimate TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces [WWW Document] (Earth Engine Data Catalog, 2022).
Mateo-García, G., Gómez-Chova, L., Amorós-López, J., Muñoz-Marí, J. & Camps-Valls, G. Multitemporal cloud masking in the Google Earth engine. Remote Sens. (Basel). 10. https://doi.org/10.3390/rs10071079 (2018).
Feng, X. et al. Spatio-Temporal variation and Climatic driving factors of vegetation coverage in the yellow river basin from 2001 to 2020 based on kNDVI. Forests 14 https://doi.org/10.3390/f14030620 (2023).
Lakra, D., Singh, S. K., Gupta, S. K. & Kanga, S. Enhancing integrated resource management through remote sensing and GIS. J. Geogr. Cartography. 7 (1), SCI. https://doi.org/10.24294/jgc.v7i1.4265 (2024).
Gu, Z. et al. Quantifying the direct and indirect effects of terrain, climate and human activity on the Spatial pattern of kNDVI-based vegetation growth: A case study from the Minjiang river basin, Southeast China. Ecol. Inf. 80 https://doi.org/10.1016/j.ecoinf.2024.102493 (2024).
Fauzi, A. B. A., Ibrahim, A. M., Khan, M. R. & Mardzuki, M. I. August. Modelling Potential Vegetation Index for Paddy Field Mapping in Malaysia Using Satellite Imagery. In 2024 9th International Conference on Mechatronics Engineering (ICOM) (pp. 485–494). IEEE. (2024).
Niu, K., Liu, G., Zhan, C. & Kang, A. The Role of Climate Change and Human Intervention in Shaping Vegetation Patterns in the Fen River Basin of China: Implications of the Grain for Green Program. Forests, 15(10), p.1733. (2024).
Sohail, M. et al. Tourism, Threat, and Opportunities for the Forest Resources: A Case Study of Gabin Jabaa, District Swat (International Journal of Forest Sciences, 2023).
Arshad, S., Kazmi, J. H., Prodhan, F. A. & Mohammed, S. Exploring the dynamic response of Agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan. Field Crops Res. 302. https://doi.org/10.1016/j.fcr.2023.109057 (2023).
Cao, S. et al. Effects and contributions of meteorological drought on agricultural drought under different Climatic zones and vegetation types in Northwest China. Sci. Total Environ. 821 https://doi.org/10.1016/j.scitotenv.2022.153270 (2022).
Dixit, J. et al. Potential of lightweight drones and Object-Oriented image segmentation in forest plantation assessment. Remote Sens. 16 (9). https://doi.org/10.3390/rs16091554ab (2024). SCOPUS, Impact Factor: 8.3.
Ming, W. et al. Quantitative assessment of cropland exposure to agricultural drought in the greater Mekong subregion. Remote Sens. (Basel). 15. https://doi.org/10.3390/rs15112737 (2023).
Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A. & Qin, S. Assessment of Spatiotemporal characteristic of droughts using in situ and remote Sensing-Based drought indices. IEEE J. Sel. Top. Appl. Earth Obs Remote Sens. 16 https://doi.org/10.1109/JSTARS.2023.3237380 (2023).
Zhang, Y. et al. Spatiotemporal Data Fusion of Index-Based VTCI Using Sentinel-2 and – 3 Satellite Data for Field-Scale Drought Monitoring. IEEE Trans. Geosci. Remote Sens. 62. https://doi.org/10.1109/TGRS.2023.3338623 (2024).
Senhorelo, A. P. et al. Application of the vegetation condition index in the diagnosis of Spatiotemporal distribution of agricultural droughts: A case study concerning the state of Espírito santo, southeastern Brazil. Divers. (Basel). 15. https://doi.org/10.3390/d15030460 (2023).
de Lima, S. C., Neto, J. M., de Lima, M., de Lima, J. P., Saboya, F. C. & L.M.F Response of semi-arid vegetation to agricultural drought determined by indices derived from MODIS satellite. Revista Brasileira De Engenharia Agricola E Ambiental. 27 https://doi.org/10.1590/1807-1929/agriambi.v27n8p632-642 (2023).
Fan, J. et al. Monitoring Global Agricultural Drought with Chinese Meteorological Satellite Data, in: 2023 11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023. (2023). https://doi.org/10.1109/Agro-Geoinformatics59224.2023.10233453
Ejaz, N., Bahrawi, J., Alghamdi, K. M., Rahman, K. U. & Shang, S. Drought monitoring using Landsat derived indices and Google Earth engine platform: A case study from Al-Lith watershed, Kingdom of Saudi Arabia. Remote Sens. (Basel). 15. https://doi.org/10.3390/rs15040984 (2023).
Tran, T. V. et al. Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data. GIsci Remote Sens. 60. https://doi.org/10.1080/15481603.2022.2163070 (2023).
Alahacoon, N., Edirisinghe, M. & Ranagalage, M. Satellite-based meteorological and agricultural drought monitoring for agricultural sustainability in Sri Lanka. Sustain. (Switzerland). 13. https://doi.org/10.3390/su13063427 (2021).
Jiang, R. et al. Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China. Sci. Rep. 11 https://doi.org/10.1038/s41598-021-93328-z (2021).
Wassie, S. B., Mengistu, D. A. & Birlie, A. B. Agricultural drought assessment and monitoring using MODIS-based multiple indices: the case of North wollo, Ethiopia. Environ. Monit. Assess. 194 https://doi.org/10.1007/s10661-022-10455-4 (2022).
Fan, J., Zhang, M., Cao, G., Zhang, X. & Wu, J. Integration of drought monitoring with remote sensing into the global drought information system, in: Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV. (2012). https://doi.org/10.1117/12.971441
Klinsuwan, T., Ratiphaphongthon, W. & Wangkeeree, Rabian, Wangkeeree, Rattanaporn, Sirisamphanwong, C. Evaluation of machine learning algorithms for supervised anomaly detection and comparison between static and dynamic thresholds in photovoltaic systems. Energies (Basel). 16. https://doi.org/10.3390/en16041947 (2023).
Javed, T. et al. Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices. J. Clean. Prod. 313. https://doi.org/10.1016/j.jclepro.2021.127866 (2021).
Kong, Y. & Luo, Z. Improving the accuracy of Coal-Rock dynamic hazard anomaly detection using a dynamic threshold and a depth Auto-Coding Gaussian hybrid model. Sustain. (Switzerland). 15. https://doi.org/10.3390/su15129655 (2023).
Li, K. et al. Dynamic evaluation of agricultural drought hazard in Northeast China based on coupled Multi-Source data. Remote Sens. (Basel). 15. https://doi.org/10.3390/rs15010057 (2023).
McLeod, A. I. Kendall rank correlation and Mann-Kendall trend test. R Package Kendall. 602, 1–10 (2005a).
McLeod, A. I. Kendall rank correlation and Mann-Kendall trend test. R Package Kendall. 602, 1–10 (2005b).
Sam, M. G., Nwaogazie, I. L. & Ikebude, C. Climate change and trend analysis of 24-Hourly annual maximum series using Mann-Kendall and Sen slope methods for rainfall IDF modeling. Int. J. Environ. Clim. Change. https://doi.org/10.9734/ijecc/2022/v12i230628 (2022).
Nyikadzino, B., Chitakira, M. & Muchuru, S. Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic. Phys. Chem. Earth. 117 https://doi.org/10.1016/j.pce.2020.102870 (2020).
Sivakumar, M. V. K. & Stefanski, R. Climate and Land degradation—an Overview (Springer, 2007).
Liu, Q., Yan, C., Ju, H. & Garré, S. Impact of climate change on potential evapotranspiration under a historical and future climate scenario in the Huang-Huai-Hai plain, China. Theor. Appl. Climatol. 132 https://doi.org/10.1007/s00704-017-2060-6 (2018).
Wu, Z., Mei, Y., Chen, J., Hu, T. & Xiao, W. Attribution analysis of dry season runoff in the Lhasa river using an extended hydrological sensitivity method and a hydrological model. Water (Switzerland). 11. https://doi.org/10.3390/w11061187 (2019).
Xu, M., Zhang, Z., Wang, Y. & Liu, B. Quantifying the contributions of Climatic and human factors to vegetation net primary productivity dynamics in East Africa. Front. Forests Global Change. 6 https://doi.org/10.3389/ffgc.2023.1332631 (2023).
Zhang, Z. et al. Lag time and cumulative effects of climate factors on drought in North China plain. Water (Switzerland). 15. https://doi.org/10.3390/w15193428 (2023).
Jarque-bascuñana, L. et al. Near infrared reflectance spectroscopy analysis to predict diet composition of a mountain ungulate species. Animals 11 https://doi.org/10.3390/ani11051449 (2021).
Gruszczyński, S. Prediction of soil properties with machine learning models based on the spectral response of soil samples in the near infrared range. Soil. Sci. Annual. 70 https://doi.org/10.2478/ssa-2019-0027 (2019).
Fu, Y., Yang, G., Wang, J., Song, X. & Feng, H. Winter wheat biomass Estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Comput. Electron. Agric. 100 https://doi.org/10.1016/j.compag.2013.10.010 (2014).
Ribeiro, A. F. S., Russo, A., Gouveia, C. M. & Páscoa, P. Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices. Theor. Appl. Climatol. 136 https://doi.org/10.1007/s00704-018-2478-5 (2019).
Mohanasundaram, S., Baghel, T., Thakur, V., Udmale, P. & Shrestha, S. Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand. Environ. Monit. Assess. 195. https://doi.org/10.1007/s10661-022-10802-5 (2023).
Mokhtari, M. H., Adnan, R. & Busu, I. A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method. Nat. Hazards. 65 https://doi.org/10.1007/s11069-012-0408-x (2013).
Badshah, M. T., Hussain, K., Rehman, A. U., Mehmood, K., Muhammad, B., Wiarta, R.,… Meng, J. (2024). The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory. Frontiers in Forests and Global Change, 7, 1345047.
Muhammad, S., Hamza, A., Mehmood, K., Adnan, M. & Tayyab, M. Analyzing the impact of forest harvesting ban in Northern temperate forest. A case study of Anakar valley, Kalam swat region, Khyber-Pakhtunkhwa, Pakistan. Pure Appl. Biology. 12 (2), 1434–1439 (2023).
Christidis, N., Manomaiphiboon, K., Ciavarella, A. & Stott, P. A. The hot and dry April of 2016 in Thailand. Bull. Am. Meteorol. Soc. 99 (1), S135–S139 (2018).
Emmerichs, T., Lu, Y. S. & Taraborrelli, D. The influence of plant water stress on vegetation–atmosphere exchanges: implications for Ozone modelling. Biogeosciences 21, 3251–3269 (2024).
Gupta, S. K. & Pandey, A. C. Spectral aspects for monitoring forest health in extreme seasons using multispectral imagery. Egypt. J. Remote Sens. Space Sci. 24 (3), 579–586. https://doi.org/10.1016/j.ejrs.2021.07.001 (2021). SCOPUS.
Bhat, A. A. et al. Simulating Climatic Patterns and Their Impacts on the Food Security Stability System in Jammu, Kashmir and Adjoining Regions, India. Climate, 12(99). SCOPUS, Impact Factor: 5.5. (2024). https://doi.org/10.3390/cli12070099
Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6 (1), 5989 (2020).
Johnston, N. K., Burns, A. S. & Hay, M. E. Response of a temperate coral to temperature stress: A comparison of populations across sites. J. Exp. Mar. Biol. Ecol. 560 https://doi.org/10.1016/j.jembe.2022.151863 (2023).
Porporato, A., D’Odorico, P., Laio, F. & Rodriguez-Iturbe, I. Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress III. Vegetation water stress. Adv. Water Resour. 24 (7), 725–744 (2001).
Vashi, V., Samaddar, A. & Ghosh, P. Tropical vegetable production under stress: challenges to food security. Global Food Secur. 25, 100385 (2020).
Santos, F., Silva, M. S. & Albuquerque, F. Drought-tolerant crop varieties for food security under climate change: A global analysis. Food Policy. 102, 102067 (2022).
Nhamo, L., Muchuru, S. & Nhemachena, C. Recurrent droughts and ENSO in Southern africa: socioeconomic impacts and policy implications. Clim. Risk Manage. 23, 50–59 (2019).
Giordano, M., Turral, H. & Scheierling, S. M. Deficit irrigation and water productivity in agriculture: how to adapt to climate stress. Agric. Water Manage. 243, 106502 (2021).
Berg, A. & Sheffield, J. Soil–vegetation–atmosphere coupling and its impact on predictability of vegetation stress under extreme weather conditions. Nat. Clim. Change. 8 (6), 485–492 (2018).
Bento, V. A., Gouveia, C. M., DaCamara, C. C., Libonati, R. & Trigo, I. F. The roles of NDVI and land surface temperature when using the vegetation health index over dry regions. Glob Planet. Change. 190. https://doi.org/10.1016/j.gloplacha.2020.103198 (2020).
Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A Pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7 https://doi.org/10.1002/ece3.3262 (2017).
Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12 https://doi.org/10.1186/s13021-017-0072-2 (2017).
Inglett, K. S., Inglett, P. W., Reddy, K. R. & Osborne, T. Z. Temperature sensitivity of greenhouse gas production in wetland soils of different vegetation. Biogeochemistry 108 https://doi.org/10.1007/s10533-011-9573-3 (2012).
Zona, D. et al. Pan-Arctic soil moisture control on tundra carbon sequestration and plant productivity. Glob Chang. Biol. 29 https://doi.org/10.1111/gcb.16487 (2023).
Dwomoh, F. K., Wimberly, M. C., Cochrane, M. A. & Numata, I. Forest degradation promotes fire during drought in moist tropical forests of Ghana. Ecol. Manage. 440 https://doi.org/10.1016/j.foreco.2019.03.014 (2019).
Shahzad, F. et al. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecol. 20 (1), 1–20. https://doi.org/10.1186/s42408-024-00289-5 (2024).
Flores, B. M. et al. Soil erosion as a resilience drain in disturbed tropical forests. Plant. Soil. https://doi.org/10.1007/s11104-019-04097-8 (2020).
Mehmood, K. et al. Spatial and Temporal Vegetation Dynamics from 2000 To 2023 in the Western Himalayan Regionspp.1–22 (Stochastic Environmental Research and Risk Assessment, 2025a). https://doi.org/10.1007/s00477-025-02971-9
Hussain, K. et al. Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach. Ann. GIS. 1–27. https://doi.org/10.1080/19475683.2024.2343399 (2024).
Losapio, G. et al. Habitat protection and removal of encroaching shrubs support the recovery of biodiversity and ecosystem functioning. Conserv. Sci. Pract. 6 https://doi.org/10.1111/csp2.13111 (2024).
Shobairi, S. O. R. et al. A comparative pattern for Populus spp. And betula spp. Stand biomass in Eurasian climate gradients. Croatian J. For. Engineering: J. Theory Application Forestry Eng. 43 (2), 457–467. https://doi.org/10.5552/crojfe.2022.1340 (2022).
Chowdhury, R. et al. Effects of nutrient limitation, salinity increase, and associated stressors on Mangrove forest cover, structure, and zonation across Indian sundarbans. Hydrobiologia 842 https://doi.org/10.1007/s10750-019-04036-9 (2019).
Ferreira, A. C. & Lacerda, L. D. Degradation and conservation of Brazilian mangroves, status and perspectives. Ocean. Coast Manag. 125 https://doi.org/10.1016/j.ocecoaman.2016.03.011 (2016).
Liza Goldberg, Lagomasino, D., Thomas, N. & Fatoyinbo, T. Global Change Biology – 2020 – Goldberg – Global Declines in human-driven Mangrove Loss (Glob Chang Biol, 2020).
Cure, M. B., Flores, B. M., Mattos, C. R. C., Oliveira, R. S. & Hirota, M. Vegetation-rainfall coupling as an indicator of ecosystem state in a heterogeneous landscape. Ecol. Indic. 157 https://doi.org/10.1016/j.ecolind.2023.111268 (2023).
ALI, S. et al. Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia. J. Integr. Agric. 22. https://doi.org/10.1016/j.jia.2023.04.028 (2023).
Bento, V. A., Trigo, I. F., Gouveia, C. M. & DaCamara, C. C. Contribution of land surface temperature (TCI) to vegetation health index: A comparative study using clear Sky and all-weather climate data records. Remote Sens. (Basel). 10. https://doi.org/10.3390/rs10091324 (2018).
Usoltsev, V. A. et al. The principle of space-for-time substitution in predicting betula spp. Biomass change related to climate shifts. Appl. Ecol. Environ. Res. 20 (4), 3683–3698. https://doi.org/10.15666/aeer/2004_36833698 (2022).
Haider, K., Khokhar, M. F., Chishtie, F., RazzaqKhan, W. & Hakeem, K. R. Identification and Future Description of Warming Signatures Over Pakistan with Special Emphasis on Evolution of CO 2 Levels and Temperature during the First Decade of the twenty-first Century24pp.7617–7629 (Environmental Science and Pollution Research, 2017).
Jallat, H. et al. Monitoring carbon stock and land-use change in 5000-year-old juniper forest stand of Ziarat, Balochistan, through a synergistic approach. Forests, 12(1), p.51. (2021).
Bustamante, M., Roitman, I., Aide, T., Alencar, A., Anderson, L., Aragão, L., … Vieira,I. (2016). Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Global Change Biology,22, 93–107. https://doi.org/10.1111/gcb.13087.
Benayas, J., Newton, A., Diaz, A. & Bullock, J. Enhancement of biodiversity and ecosystem services by ecological restoration: A meta-analysis. Science 325 (5944), 1121–1124. https://doi.org/10.1126/science.1172460 (2009).
Crouzeilles, R., Ferreira, M. S., Chazdon, R., Lindenmayer, D., Sansevero, J. B.,Monteiro, L., … Strassburg, B. (2017). Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Science Advances,3(6), e1701345. https://doi.org/10.1126/sciadv.1701345.
Barral, M., Benayas, J., Meli, P. & Maceira, N. Quantifying the impacts of ecological restoration on biodiversity and ecosystem services in agroecosystems: A global meta-analysis. Agric. Ecosyst. Environ. 202, 223–231. https://doi.org/10.1016/J.AGEE.2015.01.009 (2015).
Shoo, L. & Catterall, C. P. Stimulating natural regeneration of tropical forest on degraded land: approaches, outcomes, and information gaps. Restor. Ecol. 21 (6), 593–601. https://doi.org/10.1111/rec.12048 (2013).
Kettle, C. Seeding ecological restoration of tropical forests: priority setting under REDD+. Biol. Conserv. 154, 34–41. https://doi.org/10.1016/J.BIOCON.2012.03.016 (2012).
Allek, A. et al. How does forest restoration affect the recovery of soil quality? A global meta-analysis for tropical and temperate regions. Restor. Ecol. 31, e13747. https://doi.org/10.1111/rec.13747 (2022).
Muhammad, S. et al. Temporal variations in burn severity among various vegetation layers in subtropical Pinus Roxburghii (Chir Pine) forest of Hindu Kush mountain range. Trees Forests People. 18, 100664. https://doi.org/10.1016/j.tfp.2024.100664 (2024).
Baicha, W. Land use dynamics and land cover structure change in Thailand (as exemplified by mountainous Nan Province. Geogr. Nat. Resour. 37, 87–92 (2016).
Akbar, S. & Gupta, S. K. Transforming Agricultural Management for a Sustainable Future. In Springer (ISBN: 978-3-031-63430-7). (2024).