Original Article
Remote Sensing and Its Role in Shaping Forest Crescendos under Climate Change
Year: 2025 | Month: December | Volume 12 | Issue 2
Miriam W. Githumbi
Riziki Mwadalu
Joseph Onyango-Gweyi
Jacob Omollo
Sumit Sow
Shivani Ranjan
Sagar Maitra and Harun I. Gitari
1.Abatzoglou, J.T., Williams, A.P., Boschetti, L., Zubkova, M. and Kolden, C.A. 2018. Global patterns of interannual climate–fire relationships. Global Change Biol., 24(11):5164– 5175.
View at Google Scholar2.Ainsworth, E.A. and Long, S.P. 2021. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Global Change Biol., 27(1): 27–49.
View at Google Scholar3.Anderegg, W.R., Trugman, A.T., Badgley, G., Anderson, C.M., Bartuska, A., Ciais, P. and Randerson, J.T. 2020. Climatedriven risks to the climate mitigation potential of forests. Science, 368(6497): eaaz7005.
View at Google Scholar4.Aragão, L.E., Anderson, L.O., Fonseca, M.G., Rosan, T.M., Vedovato, L.B., Wagner, F.H. and Saatchi, S. 2018. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Comm., 9(1): 536.
View at Google Scholar5.Asner, G.P., Martin, R.E., Anderson, C.B., Kryston, K., Vaughn, N., Knapp, D.E. and Field, C.B. 2017. Scale dependence of canopy trait distributions along a tropical forest elevation gradient. New Phytol., 214(3): 973–988.
View at Google Scholar6.Baccini, A., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D. and Houghton, R.A. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Chang., 2(3): 182–185.
View at Google Scholar7.Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla- Menashe, D. and Houghton, R.A. 2017. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358(6360): 230–234.
View at Google Scholar8.Boisvert-Marsh, L., Périé, C. and de Blois, S. 2019. Shifting with climate? Evidence for recent changes in tree species distribution at high latitudes. Ecosphere, 10(3): e02672.
View at Google Scholar9.Bonan, G.B. and Doney, S.C. 2018. Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models. Science, 359(6375): eaam8328.
View at Google Scholar10.Bouvet, A., Mermoz, S., Ballère, M., Koleck, T. and Le Toan, T. 2018. Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. Remote Sens., 10(8): 1250.
View at Google Scholar11.Brandt, M., Rasmussen, K., Peñuelas, J., Tian, F., Schurgers, G., Verger, A. and Fensholt, R. 2017. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol., 1(4): 0081.
View at Google Scholar12.Brofeldt, S., Theilade, I., Burgess, N.D., Danielsen, F., Poulsen, M.K., Adrian, T. and Widayati, A. 2014. Community monitoring of carbon stocks for REDD+: Does accuracy and cost change over time? Forests, 5(8): 1834–1854.
View at Google Scholar13.Chappa, L.R., Nungula, E.Z., Makwinja, Y.H., Ranjan, S., Sow, S., Alnemari, A.M., Maitra, S., Seleiman, M.F., Mwadalu, R. and Gitari, H.I. 2024. Outlooks on Major Agroforestry Systems. In: Raj A, Jhariya MK, Banerjee A, Jha RK, Singh KP (Eds). Agroforestry. Wiley-Scrivener, USA., pp. 21–48.
View at Google Scholar14.Cheptoek, R.P., Mochoge, B., Kisaka, O.M., Otieno, E., Maitra, S., Nasar, J. and Seleiman, M.F. 2021. Maize productivity, economic returns, and phosphorus use efficiency as influenced by lime, Minjingu Rock Phosphate, and NPK inorganic fertilizer. Int. J. Biores. Sci., 8: 47–60.
View at Google Scholar15.Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F. and Bargellini, P. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ., 120: 25–36.
View at Google Scholar16.Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., Hurtt, G.C. and Chazdon, R.L. 2010. Estimation of tropical forest height and biomass dynamics using LiDAR remote sensing at La Selva, Costa Rica. J. Geophys. Res. Biogeosci., 115(G2).
View at Google Scholar17.Emmanuely, N.Z., Chappa, L.R., Ngaiza, V.V., Saeed, A., Ranjan, S., Sow, S., Mwadalu, R., Raza, M.A., Soratto, R.P., Rezaei-Chiyaneh, E. and Nasar, J. 2025. Agroforestry for Monetising Carbon Credits. In: Raj A, Jhariya MK, Meena RS (Eds). Carbon trading in Agroforestry under changing climate. Springer, Cham, pp. 89–111.
View at Google Scholar18.Emmanuely, N.Z., Chappa, L.R., Ranjan, S., Sow, S., Alnemari, A.M., Seleiman, M.F., Mwadalu, R. and Maitra S. 2024. Ecosystem Services through Agroforestry Systems and its Sustainability. In: Raj A, Jhariya MK, Banerjee A, Jha RK, Singh KP (Eds). Agroforestry. Wiley-Scrivener, USA. pp. 223–254.
View at Google Scholar19.Fisher, J.B., Lee, B., Purdy, A.J., Halverson, G.H., Dohlen, M.B., Cawse-Nicholson, K. and Hook, S. 2020. ECOSTRESS: NASA’s next-generation mission to measure evapotranspiration from the International Space Station. Water Res. Res., 56(4): e2019WR026058.
View at Google Scholar20.Foody, G.M. 2015. Valuing map validation: The need for rigorous land cover map accuracy assessment in economic valuations of ecosystem services. Ecol. Econ., 111: 23–28.
View at Google Scholar21.Gitari, H.I., Nungula, E.Z., Chappa, L.R., Raza, M.A., Ranjan, S., Sow, S., Alnemari, A.M., Seleiman, M.F., Mwadalu, R. and Maitra, S. 2024. Agroforestry for Climate Security. In: Raj, A., Jhariya, M.K., Banerjee, A., Jha, R.K., Singh, K.P. (Eds). Agroforestry. Wiley-Scrivener, USA., pp. 319–344.
View at Google Scholar22.Goetz, S., Hansen, M., Houghton, R., Walker, W., Laporte, N.T. and Busch, J. 2015. Measurement and monitoring for REDD+: the needs, current technological capabilities and future potential. Center for Global Development Working Paper, (392), available online: Measurement and Monitoring for REDD+: The Needs, Current Technological Capabilities and Future Potential
View at Google Scholar23.Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A. and Townshend, J.R. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850–853.
View at Google Scholar24.Herold, M. and Johns, T. 2007. Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process. Environ. Res. Lett., 2(4): 045025.
View at Google Scholar25.Herrmann, S.M. and Tappan, G.G. 2013. Vegetation impoverishment despite greening: A case study from central Senegal. J. Arid Environ., 90: 55–66.
View at Google Scholar26.Houborg, R. and McCabe, M.F. 2018. Daily retrieval of NDVI and LAI at 3 m resolution via the fusion of CubeSat, Landsat and MODIS data. Remote Sens., 10(6): 890.
View at Google Scholar27.Hubau, W., Lewis, S.L., Phillips, O.L., Affum-Baffoe, K., Beeckman, H., Cuní-Sanchez, A. and Zemagho, L. 2020. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature, 579(7797): 80–87.
View at Google Scholar28.Jeong, S.J., Ho, C.H., Choi, S.D., Kim, J., Lee, E.J. and Gim, H.J. 2013. Satellite data-based phenological evaluation of the nationwide reforestation of South Korea. PLoS One, 8(3): e58900.
View at Google Scholar29.Johnstone, J.F., Allen, C.D., Franklin, J.F., Frelich, L.E., Harvey, B.J., Higuera, P.E. and Turner, M.G. 2016. Changing disturbance regimes, ecological memory and forest resilience. Front. Ecol. Environ., 14(7): 369–378.
View at Google Scholar30.Jones, B.M., Grosse, G.D.A.C., Arp, C.D., Jones, M.C., Walter Anthony, K.M. and Romanovsky, V.E. 2011. Modern thermokarst lake dynamics in the continuous permafrost zone, northern Seward Peninsula, Alaska. J. Geophys. Res.: Biogeosci., 116(G2).
View at Google Scholar31.Ju, J. and Masek, J.G. 2016. The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data. Remote Sens. Environ., 176: 1–16.
View at Google Scholar32.Kasischke, E.S. and Turetsky, M.R. 2006. Recent changes in the fire regime across the North American boreal region— Spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett., 33: L09703.
View at Google Scholar33.Kellndorfer, J.M., Walker, W.S., LaPoint, E., Kirsch, K., Bishop, J. and Fiske, G. 2010. Statistical fusion of Lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets. J Geophysic. Res.: Biogeosc., 115(G2).
View at Google Scholar34.Le Toan, T., Huu, N., Simioni, M., Phan, H., Arai, H., Mermoz, S. and Espagne, E. 2021. Agriculture in Viet Nam under the impact of climate change. Climate change in Viet Nam. Impacts and adaptation. A COP26 assessment report of the GEMMES Viet Nam project.
View at Google Scholar35.Luciana, C.R., Nungula, E.Z., Ngaiza, V.V., Mwadalu, R., Makwinja, Y.H., Dlamini, J.C., Ranjan, S., Sow, S., Raza, M.A., Soratto, R.P. and Raj, A. 2025. The challenges and solutions for carbon credits. In: Raj, A., Jhariya, M.K., Meena, R.S. (Eds). Carbon trading in Agroforestry under changing climate. Springer, Cham., pp. 525–549.
View at Google Scholar36.Maitra, S., Bhattacharya, U,, Pramanick, B., Sagar, L,, Gaikward, D.J., Pramanick, B., Sairam, M., Sahoo, U., Jatav, H.S., Shankar, T., Kandileri, A.M., Rezaei-Chiyaneh, E. and Hossain, A. 2024a. Agroforestry: A Resource Conserving Technology for Efficient Utilization of Agricultural Inputs, Leads to Food and Environmental Security. In: Jatav, H.S., Rajput, V.D., Minkina, T., Van Hullebusch, E.D., Dutta, A. (Eds) Agroforestry to Combat Global Challenges. Sustainable Development and Biodiversity, pp. 15–52. Springer, Singapore.
View at Google Scholar37.Maitra, S., Ray, S., Sagar, L., Sairam, M., Pramanick, B., Gitari, H., Santosh, D.T., Jagannath, G.D., Atapattu, A. and Chappa, L.R. 2025a. Cropping System Approach in Climate-Resilient Food Crop Production. In: Climate- Smart Agricultural Technologies Springer, Singapore, pp. 327-356.
View at Google Scholar38.Maitra, S., Panda, P., Panda, S.K., Behera, D., Shankar, T. and Nanda, S.P. 2020. Relevance of barnyard millet (Echinochloa frumentacea L.) cultivation and agronomic management for production sustainability. Int. J. Bioinform. Biol. Sci., 8: 27-32.
View at Google Scholar39.Maitra, S., Sairam, M., Ray, S., Santosh, D. T., Gaikwad, D. J., Gitari, H., Mukesh, G. and Maheswari, N. 2024b. Applauding soil quality improvement through legumebased intercropping system: A review. Int. J. Biores. Sci., 11: 59-69.
View at Google Scholar40.Maitra, S., Sairam, M., Sagar, L., Ray, S., Santosh, D.T., Gaikwad, D.J., Atapattu, A.J. and Gitari, H.I. (2025b). Alley Cropping: A Sustainable Agroforestry System for Enhancing Productivity, Soil Health and Biodiversity. In Plant Biotechnology and Sustainable Agriculture: Bridging the Gap for Global Food Security, CABI, pp. 235-248.
View at Google Scholar41.Maitra, S., Santosh, D.T., Jena, J., Berera, S.D., Ray, S., Sairam, M., Palai, J.B., Gaikwad, D.J., Rishitha, S., Atapattu, A.J. and Hossain, A. 2026. Understanding Plant Responses to Abiotic Stress through Systems Biology. In: Aftab, T. (Ed). Systems Biology in Crop Improvement: Emerging Tools and Technologies. Elsevier, Chapter 3, pp. 1–33.
View at Google Scholar42.Manasa, P., Sairam, M. and Maitra, S. 2021. Influence of maizelegume intercropping system on growth and productivity of crops. Int. J. Biores. Sci., 08(01): 21-28.
View at Google Scholar43.Masina, S., Maitra, S., Sahoo, U., Sagar, L. and Krishna, T.G. 2023. Evaluation of precision nutrient tools and nutrient optimization in maize (Zea mays L.) for enhancement of growth, productivity and nutrient use efficiency. Res. Crop., 24(4): 666-677.
View at Google Scholar44.Miettinen, J., Shi, C. and Liew, S.C. 2016. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Global Ecol. Conserv., 6: 67–78.
View at Google Scholar45.Mukesh, G., Sairam, M., Maitra, S., Gaikwad, D.J., Sagar, L. and Ray, S. 2024. Agrometeorological indices, physiological growth parameters and performance of finger millet as influenced by different cultivars under hot and sub-humid region of Odisha. Int. J. Exp. Res. Rev., 42: 148-161.
View at Google Scholar46.Mwadalu, R., Abdi, A.M., Rutto, M., Chappa, L.R., Nungula, E.Z., Rezaei-Chiyaneh, E., Nasar, J., Heydarzadeh, S., Ranjan, S., Sow, S., Dlamini, J.C. and Raj, A. 2025. Potential of agroforestry for enhancing Africa’s access to carbon markets: A review. In: Raj, A., Jhariya, M.K., Meena, (R.S. Eds). Carbon trading in Agroforestry under changing climate. Springer, Cham, pp. 325–367.
View at Google Scholar47.Neuenschwander, A. and Pitts, K. 2019. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sens. Environ., 221: 247–259.
View at Google Scholar48.Nungula, E.Z., Chappa, L.R., Ngaiza, V.V., Msangi, G.S., Dlamini, J.C., Saeed, A., Mwadalu, R., Soratto, R.P., Rezaei-Chiyaneh. E. and Nasar, J. 2025. Climate Change and Carbon Emission Trading. In: Raj A, Jhariya MK, Meena RS (Eds). Carbon trading in Agroforestry under changing climate. Springer, Cham., pp. 23–57.
View at Google Scholar49.Nungula, E.Z., Mugwe, J., Nasar, J., Massawe, H.J., Karuma, A.N., Maitra, S., Seleiman, M.F., Dindaroglu, T. and Khan, N. 2023. Land degradation unmasked as the key constraint in sunflower (Helianthus annus) production: Role of GIS in Revitalizing this vital sector. Cogent Food Agric., 9(2): 2267863.
View at Google Scholar50.Nyawade, S., Karanja, N.N., Gachene, C.K.K., Schulte- Geldermann, E. and Parker, M. 2021. Yield and evapotranspiration characteristics of potato-legume intercropping simulated using a dual coefficient approach in a tropical highland. Field Crops Res., 274: 108327.
View at Google Scholar51.Paneque-Gálvez, J., McCall, M.K., Napoletano, B.M., Wich, S.A. and Koh, L.P. 2014. Small drones for communitybased forest monitoring: An assessment of their feasibility and potential in tropical areas. Forests, 5(6): 1481–1507.
View at Google Scholar52.Pugh, T.A., Lindeskog, M., Smith, B., Poulter, B., Arneth, A., Haverd, V. and Calle, L. 2019. Role of forest regrowth in global carbon sink dynamics. Proc. Nat. Acad. Sci., 116(10): 4382–4387.
View at Google Scholar53.Raj, A., Jhariya, M.K., Banerjee, A., Mina, H.K. and Mina, K.K. 2025. Carbon Credits in Agroforestry for Net Zero Emissions: A Global Synthesis. In: Raj A, Jhariya MK, Meena RS (Eds). Carbon trading in Agroforestry under changing climate. Springer, Cham., pp. 113–127.
View at Google Scholar54.Ray, S., Maitra, S., Sairam, M., Sravya, M., Priyadarshini, A., Shubhadarshi, S. and Padhi, D.P. 2024. An unravelled potential of foliar application of micro and beneficial nutrients in cereals for ensuring food and nutritional security. Int. J. Exp. Res. Rev., 41 (Spl. Vol.): 19–42.
View at Google Scholar55.Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Odongo-Braun, C. and Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environ. Res. Lett., 16(2): 024005.
View at Google Scholar56.Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N. and Prabhat, F. 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743): 195–204.
View at Google Scholar57.Rocchini, D,, Salvatori, N,, Beierkuhnlein, C., Chiarucci, A., De Boissieu, F., Förster, M. and Féret, J.B. 2021. From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing. Ecological Informatics, 61: 101195.
View at Google Scholar58.Sairam, M., Maitra, S., Praharaj, S., Nath, S., Shankar, T., Sahoo, U., Santosh, D.T., Sagar, L., Panda, M., Shanthi Priya, Ashwini, T.R., Gaikwad, D.J., Hossain, A. Pramanick, B., Jatav, H.S., Gitari, H.I. and Aftab, T. 2023. An insight into the consequences of emerging contaminants in soil and water and plant responses. In: Emerging Contaminants and Plants Interactions, Adaptations and Remediation Technologies, Aftab, T. (Ed), pp.1 - 27.
View at Google Scholar59.Sairam, M., Maitra, S., Sagar, L., Biswas, T., Bárek, V., Brestic, M. and Hossain, A. 2025. Application of precision nutrient tools for the optimization of fertilizer requirements and assessment of the growth and productivity of maize (Zea mays L.) in the northeastern Ghat of India. J Agric. Food Res., 21: 101958.
View at Google Scholar60.Santoro, M. and Cartus, O. 2023. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4.
View at Google Scholar61.Santosh, D.T., Debnath, S., Maitra, S., Sairam, M., Sagar, L.L., Hossain, A. and Moulick, D. 2024. Alleviation of climate catastrophe in agriculture through adoption of climate-smart technologies. In: Climate Crisis: Adaptive Approaches and Sustainability. (Eds; Chatterjee, U., Shaw, R., Kumar, S., Raj, A.D. and Das, S.) Springer Nature Switzerland, pp. 307-32.
View at Google Scholar62.Schimel, D., Pavlick, R., Fisher, J.B., Asner, G.P., Saatchi, S., Townsend, P. and Cox, P. 2015. Observing terrestrial ecosystems and the carbon cycle from space. Global Chang. Biol., 21(5): 1762–1776.
View at Google Scholar63.Schulze, E.D., Bouriaud, O., Irslinger, R. and Valentini, R. 2022. The role of wood harvest from sustainably managed forests in the carbon cycle. Ann. Forest Sci., 79(1): 17.
View at Google Scholar64.Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G. and Reyer, C.P. 2017. Forest disturbances under climate change. Nat. Clim. Chang., 7(6): 395–402.
View at Google Scholar65.Solberg, S., Astrup, R., Breidenbach, J., Nilsen, B. and Weydahl, D. 2013. Monitoring spruce volume and biomass with InSAR data from TanDEM-X. Remote Sens. Environ., 139: 60–67.
View at Google Scholar66.Soratto, R.P., Perdoná, M.J., Parecido, R.J. and Pinotti, R.N. 2022. Turning biennial into biannual harvest: Long-term assessment of Arabica coffee–macadamia intercropping and irrigation synergism by biological and economic indices. Food Energy Sec., 11(2): e365.
View at Google Scholar67.Sow, S., Ranjan, S, Seleiman, M.F., Alkharabsheh, H.M., Kumar, M., Kumar, N., Padhan, S.R., Roy, D.K., Nath, D. and Wasonga, D.O. 2024. Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies. Phyton-Int. J. Exper. Bot., 93(7): 1569–1598.
View at Google Scholar68.Thurner, M., Beer, C., Crowther, T., Falster, D., Manzoni, S., Prokushkin, A. and Schulze, E.D. (2019). Sapwood biomass carbon in northern boreal and temperate forests. Global Ecol. Biogeog., 28(5): 640–660.
View at Google Scholar69.Vennela, M., Varma, D.D., Sadhu, S., Padhan, S.R and Roy, D.K. (2025). Accelerating Role of Artificial Intelligence and Robotics in Crop Improvement. In: Kumar S, Ranjan S, Sow S, Padhan SR, Roy DK (Eds). Climate-Resilient Agriculture for Sustainable Crop Production. CRC Press, Taylor and Francis Group, pp. 187–208.
View at Google Scholar70.Vogt, P., Riitters, K.H., Caudullo, G. and Eckhardt, B. 2019. FAO-State of the World’s forests: forest fragmentation. Publications Office of the European Union: Luxembourg.
View at Google Scholar71.Wagner, F.H., Dalagnol, R., Silva-Junior, C.H., Carter, G., Ritz, A.L., Hirye, M.C. and Saatchi, S. 2023. Mapping tropical forest cover and deforestation with Planet NICFI satellite images and deep learning in Mato Grosso State (Brazil) from 2015 to 2021. Remote Sens., 15(2): 521.
View at Google Scholar72.Wang, M., Fan, L., Frappart, F., Ciais, P., Sun, R., Liu, Y. and Wigneron, J.P. 2021. An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales. Remote Sens. Environ., 263: 112556.
View at Google Scholar73.Wen, J., Köhler, P., Duveiller, G., Parazoo, N.C., Magney, T.S., Hooker, G. and Sun, Y. 2020. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF). Remote Sens. Environ., 239: 111644.
View at Google Scholar74.Wu, Q., Lane, C.R., Li, X., Zhao, K., Zhou, Y., Clinton, N. and Lang, M.W. 2019. Integrating LiDAR data and multitemporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sens. Environ., 228: 1–13.
View at Google Scholar75.Wulder, M.A., Hermosilla, T., Stinson, G., Gougeon, F.A., White, J.C., Hill, D.A. and Smiley, B.P. 2020. Satellitebased time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: Int. J. Forest Res., 93(3): 331–343.
View at Google Scholar76.Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C., Gao, F. and Huete, A. 2003. Monitoring vegetation phenology using MODIS. Remote Sens. Environ., 84(3): 471–475.
View at Google Scholar77.Zhu, Z. and Woodcock, C.E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ, 144: 152–171.
View at Google Scholar




