ASSESSING LAND COVER CHANGE IN LANGTANG – SOUTH LOCAL GOVERNMENT AREA, PLATEAU STATE, NIGERIA USING GIS AND REMOTE SENSING TOOLS: A CASE STUDY APPROACH.

Main Article Content

Vincent D. Choji
Nanchang D.

Abstract

This study investigates land use and land cover changes LULC in Langtang South Local Government Area, Plateau State, Nigeria, over a 15-year period (2007–2022). Landsat 7 satellite imagery was sourced from the USGS EROS database for December 7, 2007 (Image ID: 200701207_02), and December 9, 2022 (Image ID: 20221209_02). The imagery was processed and analyzed using ArcGIS 10.3.1 software to examine spatial and temporal land cover change variations within the study area. Findings revealed notable LULC dynamics. Bare land increased significantly from 1.76% in 2007 to 7.05% in 2022, suggesting rising land degradation. Farmland, which dominated in 2007, declined substantially from 75.59% to 66.75%, indicating potential shifts in agricultural practices or land conversion. Settlement areas saw a modest rise, increasing from 0.16% to 0.57%, reflective of urban expansion. Vegetation cover experienced a positive change, growing from 22.27% to 25.13%, likely due to reforestation or conservation efforts. Water bodies also increased slightly from 0.21% to 0.51%, potentially influenced by improved rainfall or water management systems. The confusion matrix analysis revealed classification challenges, particularly between bare land and farmland, settlement and vegetation, and vegetation and water bodies, highlighting the need for advanced classification techniques to improve accuracy. To address the observed changes, the study suggests the implementation of sustainable land use policies, reforestation programs, and urban planning strategies to mitigate land degradation and promote environmental stability. Additionally, integrating local communities in land management decisions can foster long-term conservation goals and reduce the impacts of urbanization and agriculture on land cover. 

Downloads

Download data is not yet available.

Article Details

Section

Articles

References

Abuelaish, B. (2018). Urban land use change analysis and modeling: a case study of the Gaza Strip. Geomatic Approaches for Modeling Land Change Scenarios, 271-291.

Admasu, S., Yeshitela, K., & Argaw, M. (2023). Impact of land use land cover changes on ecosystem service values in the Dire and Legedadi watersheds, central highlands of Ethiopia: Implication for landscape management decision making. Heliyon, 9(4), 1-14.

Adam, A. H. M., Elhag, A. M. H., & Salih, A. M. (2013). Accuracy assessment of land use & land cover classification (LU/LC), case study of Shomadi area, Renk County, Upper Nile State, South Sudan. International Journal of Scientific and Research Publications, 3(5), 1712-1717.

Afuye, G. A., Kalumba, A. M., Ishola, K. A., & Orimoloye, I. R. (2022). Long-term dynamics and response to climate change of different vegetation types using GIMMS NDVI3g data over amathole district in South Africa. Atmosphere, 13(4), 620-671.

Akinyemi, F. O., & Ifejika Speranza, C. (2024). Land transformation across agroecological zones reveals expanding cropland and settlement at the expense of tree-cover and wetland areas in Nigeria. Geo-spatial Information Science, 1-21.

Akumu, C. E., Dennis, S., & Reddy, C. (2018). Land cover land use mapping and change

detection analysis using geographic information systems and remote sensing. International Journal of Human Capital in Urban Management, 3: 167-178.

Aldea, A., Aldea, M., & Perju, S. (2019). GIS use of Land Use/Land Cover layers and historical data for water losses indices. In E3S Web of Conferences, 85, 1-5.

Aliero, M. M., Ismail, M. H., Alias, M. A., Ambursa, A. S., Muhammed, A., Umar, I., & Bunza, R. M. (2019, November). Spatiotemporal Assessment of Land Cover Change and Vegetation Degradation Using Remote Sensing in Kebbi State, Nigeria. In Conference of the Arabian Journal of Geosciences (pp. 347-350). Cham: Springer International Publishing.

Aljanabi, F., Dedeoğlu, M., & Şeker, C. (2024). Environmental monitoring of Land Use/Land Cover by integrating remote sensing and machine learning algorithms. Journal of Engineering and Sustainable Development, 28(4), 455-466.

Ambarwulan, W., Yulianto, F., Widiatmaka, W., Rahadiati, A., Tarigan, S. D., Firmansyah, I., & Hasibuan, M. A. S. (2023). Modelling land use/land cover projection using different scenarios in the Cisadane Watershed, Indonesia: Implication on deforestation and food security. The Egyptian Journal of Remote Sensing and Space Science, 26(2), 273-283.

Anne, Mook., Emily, Swanson. (2024). Environmental Change. The Blackwell Encyclopedia

of Sociology.

Awuah, T. K. (2018). Effects of spatial resolution, land-cover heterogeneity and different

classification methods on accuracy of land-cover mapping.Msc thesis, Swedish University of Agricultural Sciences.

Buthelezi, M. N. M., Lottering, R., Peerbhay, K., & Mutanga, O. (2024). Assessing the extent of land degradation in the eThekwini municipality using land cover change and soil organic carbon. International Journal of Remote Sensing, 45(4), 1339-1367.

Cabral, P., Feger, C., Levrel, H., Chambolle, M., & Basque, D. (2016). Assessing the impact of land-cover changes on ecosystem services: A first step toward integrative planning in Bordeaux, France. Ecosystem Services, 22, 318-327.

Chatufale, A. P., Rege, P. P., & Bhatt, A. (2022). Extraction of Waterbody Using Object-Based Image Analysis and XGBoost. In Advanced Machine Intelligence and Signal Processing (341-350). Singapore: Springer Nature Singapore.

Chen, H., Yang, L., & Wu, Q. (2023). Enhancing land cover mapping and monitoring: An interactive and explainable machine learning approach using Google Earth Engine. Remote Sensing, 15(18), 4585-4598.

Christman, Z., Rogan, J., Eastman, J. R., & Turner, B. L. (2015). Quantifying uncertainty and confusion in land change analyses: A case study from central Mexico using MODIS data. GIScience & Remote Sensing, 52(5), 543-570.

Capolupo, A., Monterisi, C., Saponaro, M., & Tarantino, E. (2020, August). Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform. In Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020) (11524, 447-458).

Demattê, J. A., Safanelli, J. L., Poppiel, R. R., Rizzo, R., Silvero, N. E. Q., Mendes, W. D. S., ... & Lisboa, C. J. D. S. (2020). Bare earth’s surface spectra as a proxy for soil resource monitoring. Scientific Reports, 10(1), 4461-4481.

Dhillon, M. S., Kübert-Flock, C., Dahms, T., Rummler, T., Arnault, J., Steffan-Dewenter, I., & Ullmann, T. (2023). Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: A case study using STARFM NDVI in Bavaria, Germany. Remote Sensing, 15(7), 1830-1840.

Ding, K., Huang, Y., Wang, C., Li, Q., Yang, C., Fang, X., ... & Dai, M. (2022). Time series analysis of land cover change using remotely sensed and multisource urban data based on machine learning: a case study of Shenzhen, China from 1979 to 2022. Remote Sensing, 14(22), 5706-5718.

Ding, Q., Pan, T., Lin, T., & Zhang, C. (2022). Urban land-cover changes in major cities in China from 1990 to 2015. International Journal of Environmental Research and Public Health, 19(23), 16079-16089.

Edosa, B. T., & Nagasa, M. D. (2024). Spatiotemporal assessment of land use, land cover change, driving forces, and consequences using geospatial techniques: The case of Naqamte city and hinterland, western Ethiopia. Environmental Challenges, 14, 100830-100931.

Ekka, P., Patra, S., Upreti, M., Kumar, G., Kumar, A. & Saikia, P. (2023). Land Degradation and its impacts on biodiversity and Ecosystem services. Land and Environmental Management Through Forestry, 77–101.

Ekle A. E., Ibrahim H.B., John K.J., Emmanuel Y. Z., Ismaila M. A.,2 Pilika T.S.,Usman

A.,Birma P.,Iorlaha V. A. (2022). Fertility evaluation of some soils in Mabudi Langtang South Local Government Area of Plateau State, Nigeria. Adamawa State University Journal of Scientific Research, 10(2), 22-27.

Espinoza, V., Booth, L. A., & Viers, J. H. (2023). Land Use Misclassification Results in Water

Use, Economic Value, and GHG Emission Discrepancies in California’s High-Intensity Agriculture Region. Sustainability, 15, 6829-6830.

Ettehadi Osgouei, P., & Kaya, S. (2017). Analysis of land cover/use changes using Landsat 5 TM data and indices. Environmental monitoring and assessment, 189, 1-11.

Ewane, E. B., Deh-Nji, A., Mfonkwet, N. Y., & Nkembi, L. (2023). Agricultural expansion and land use land cover changes in the Mount Bamboutos landscape, Western Cameroon: implications for local land use planning and sustainable development. International Journal of Environmental Studies, 80(1), 186-206.

Faye, B., Du, G., & Zhang, R. (2022). Efficiency analysis of land use and the degree of coupling link between population growth and global built-up area in the subregion of west Africa. Land, 11(6), 847-861.

Federal Republic of Nigeria Official Gazette No. 2 Abuja — 2nd February, 2009 Vol. 96 (2009). Available online: https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://gazettes.africa/archive/ng/2009/ng-government-gazette-dated-2009-02-02-no-2.pdf&ved=2ahUKEwj5o625wY-KAxU-XEEAHeaaEH4QFnoECBgQAQ&usg=AOvVaw0q9U2ddLTdDt72AHqSLIcT. Access date: 5 December 2024.

Gebrehiwot, G. H., Bekitie, K. T., Yohannes, H., Anose, F. A., & Gebremichael, H. B. (2024). Time series land use/land cover mapping and change detection to support policies on sustainable environmental and economic management. Environmental Systems Research, 13(1),1-13.

Gobry, J.J., Twisa, S.S., Ngassapa, F. & Kilulya, K.F. (2023). Impact of land-use/cover change on water quality in the Mindu Dam drainage, Tanzania. Water Practice and Technology, 18, 1086–1098.

Guangyin, H., Huijun, J., & Zhibao, D. (2014). Research of land-use and land-cover change (LUCC) in the source regions of the Yellow River. Journal of Glaciology and Geocryology, 36(3), 573-581.

Hao, M., Dong, X., Jiang, D., Yu, X., Ding, F., & Zhuo, J. (2024). Land-use classification based on high-resolution remote sensing imagery and deep learning models. Plos one, 19(4), e0300473.

Hasan, S., Shi, W., Zhu, X., & Abbas, S. (2020). Landscape urbanization and farmland reduction from 2010 to 2017 in South China. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 699-704.

Hussain, K., Mehmood, K., Anees, S. A., Ding, Z., Muhammad, S., Badshah, T., & Khan, W. R. (2024). Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data. Heliyon, 10(14), 1-19.

Imaitor-Uku, E. E., Owei, O. B., Hart, L., & Ayotamuno, A. (2021). Impact of settlement growth on Yenagoa’s urban environment. European Journal of Environment and Earth Sciences, 2(1), 24-29.

Iwuagwu, T. E., Ibhafidon, A., Ononuju, A. H., Yohanna, W., Umar, I., Bongkur, P., & Ugwu, D. I. (2023). Knowledge of Risk Factors for Type 2 Diabetes Mellitus among Civil Servants in Langtang South Local Government Area, Plateau State, Nigeria. Nigerian Journal of Health Promotion, 16(1), 63-74.

Jabbour, J., & Hunsberger, C. (2014). Visualizing relationships between drivers of environmental change and pressures on land-based ecosystems. Natural Resources, 5(4), 147-160

Jiang, H., Ji, L., Yu, K., & Zhao, Y. (2024). Analysis of the Substantial Growth of Water Bodies during the Urbanization Process Using Landsat Imagery—A Case Study of the Lixiahe Region, China. Remote Sensing, 16(4), 711.

Kahlon, S. (2015). Land use, land cover change and human–Environment interaction: the case of Lahaul valley. International Journal of Geomatics and Geosciences, 6(2), 1568-1577.

Karandikar, A., & Agrawal, A. (2023). Performance analysis of change detection techniques for land use land cover. International Journal of Electrical and Computer Engineering (IJECE), 13(4), 4339-4346.

Khurelbaatar, T. & Ariya, B. (2022). Assessing human activities influences on the vegetation cover using trends of normalized difference vegetation index time series: Case study in Dornod Province. International Trends. Interface Science and Technology, 24–29.

Krištín, A., Hoi, H., & Kaňuch, P. (2024). Local population decline of the threatened Lesser Grey Shrike Lanius minor is linked to the modernisation of the rural landscape. Bird Conservation International, 34, e18.

Kumar, S. (2023). Change Detection Analysis of Land Cover Features using Support Vector Machine Classifier. International Journal of Next-Generation Computing, 14(2), 384.

Kushwaha, P., Nazir, A., Bansal, R., & Lone, F. R. (2025). Time series analysis of vegetation change using remote sensing, GIS and FB prophet. In Next Generation Computing and Information Systems (pp. 41-48). CRC Press.

Kyle, S., Devika, B. M., Priyadarshini, T. I., & Paul, S. (2024, June). Assessment of Land use Land Cover Changes in West Godavari District Using Gis. In Journal of Physics: Conference Series (2779, 1, 012097). IOP Publishing.

Lawal, J. O., Buba, F. N., & Awe-Peter, H. (2024). Assessment of the Impact of Land Use and Land Cover Change on the Surface Runoff of Hadejia River System, Kano, Nigeria. International Journal of Latest Technology in Engineering, Management & Applied Science, 13(5), 130-141.

Lemenkova, P. (2023). A GRASS GIS scripting framework for monitoring changes in the ephemeral salt lakes of Chotts Melrhir and Merouane, Algeria. Applied System Innovation, 6(4),1-25.

Liping, C., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PloS one, 13(7), 1-23.

Lowell, K., Reddy, S., & Farmer, E. (2014, July). Data and sampling issues associated with accuracy assessment of land cover change maps produced from multi-temporal image classification. In 2014 IEEE Geoscience and Remote Sensing Symposium ( 4235-4238). IEEE.

Mashala, M. J., Dube, T., Mudereri, B. T., Ayisi, K. K., & Ramudzuli, M. R. (2023). A systematic review on advancements in remote sensing for assessing and monitoring land use and land cover changes impacts on surface water resources in semi-arid tropical environments. Remote Sensing, 15(16),1-29.

Mpanyaro, Z., Kalumba, A. M., Zhou, L., & Afuye, G. A. (2024). Mapping and Assessing Riparian Vegetation Response to Drought along the Buffalo River Catchment in the Eastern Cape Province, South Africa. Climate, 12(1), 1-22.

National Population Commission of Nigeria (NPC, Jos office), (2024). Projected population of Local government areas and districts in Plataeau State, Nigeria.

Nezhad, M. M., Heydari, A., Fusilli, L., & Laneve, G. (2019, April). Land cover classification by using Sentinel-2 images: a case study in the City of Rome. In Proceedings of the 4th World Congress on Civil, Structural, and Environmental Engineering (CSEE’19).

Nishant, N., Anilkumar, R., Chutia, D., Babu, E. R., & Raju, P. L. N. (2022). Vegetation Trend Analysis and Change Quantification Based on Time Series Satellite Data for Northeast India. In Handbook of Himalayan Ecosystems and Sustainability,1 ( 203-223). CRC Press.

Olorunfemi, I. E., Olufayo, A. A., Fasinmirin, J. T., & Komolafe, A. A. (2022). Dynamics of land use land cover and its impact on carbon stocks in Sub-Saharan Africa: An overview. Environment, Development and Sustainability, 24(1), 40-76.

.

Pal, A., Kumar, M., Suryavanshi, S., Kumar, N., Wesley, C. J., & Lal, D. Monitoring Land Use and Land Cover Change Through Earth Observation Datasets and Metric Analysis in the Barabanki District, Uttar Pradesh, India. Current Journal of Applied Science and Technology, 43(9, 1-23.

.

Montanari, A., Palazzoli, I., & Ceola, S. (2023, May). Contribution of Anthropogenic and Climatic drivers to the Surface Water Extent Change in the Contiguous United States. In EGU General Assembly Conference Abstracts (pp. EGU-16278).

Perez-Guerra, J., Herrera-Ruiz, V., Gonzalez-Velez, J. C., Martinez-Vargas, J. D., & Torres-Madronero, M. C. (2023, August). Land Cover Classification Using Remote Sensing and Supervised Convolutional Neural Networks. In the Colombian Conference on Computing (pp. 13-24). Cham: Springer Nature Switzerland.

Puziene, R. (2024). Investigating Forest Cover Change Using Historical GIS Technologies: A Case Study with an Example of Jurbarkas District of the Republic of Lithuania. Sustainability, 16(11), 4825-4839.

Raihan, A. (2023). A comprehensive review of the recent advancement in integrating deep learning with geographic information systems. Research Briefs on Information and Communication Technology Evolution, 9, 98-115.

Ram, P. & Sheikh, M.M. (2023). Study and analysis of land use/land cover changes of jodhpur city and its impacts on economy and environment (1990–2022). Journal of Global Resources, 9, 86–95.

Rasul, A., Balzter, H., Ibrahim, G.R.F., Hameed, H.M., Wheeler, J., Adamu, B., Ibrahim, S. & Najmaddin, P.M. (2018). Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land, 7, 81-98.

Raza, A., Shahid, M. A., Safdar, M., Zaman, M., Sabir, R. M., Muzammal, H., & Ahmed, M. M. (2024). Impact of Land Use and Land Cover Change on Agricultural Production in District Bahawalnagar, Pakistan. Environmental Sciences Proceedings, 29(1), 46-59.

Renaud, F. G., Sebesvari, Z., & Gain, A. K. (2021). Assessment of land/catchment use and degradation. In Handbook of Water Resources Management: Discourses, Concepts and Examples (pp. 471-487). Cham: Springer International Publishing.

Rohith, K., Vudatha, V.P.S.H.V., Rathna, T.K. & Lakshmi, G.J. (2023). Remote Sensing- Based Vegetation Types Classification Using Landsat and Change Analysis Using Setinel-2a in Google EarthEngine.

Sameer, M.K. & Hamid, A.M. (2023). Sameer, M. K., & Hamid, A. M. (2023). Remote Sensing and GIS Techniques in Monitoring Land Use Land Cover Change. International Journal of Sustainable Construction Engineering and Technology, 14(1), 13-20.

Sapkota, L., & Dahal, R. P. (2024). Assessment of forest cover change, key drivers of change and perception of locals in Birendranagar Municipality, Surkhet District, Nepal. Archives of Agriculture and Environmental Science, 9(2), 308-316.

Schiavina, M., Melchiorri, M., Corbane, C., Freire, S., & Batista e Silva, F. (2022). Built-up areas are expanding faster than population growth: regional patterns and trajectories in Europe. Journal of land use science, 17(1), 591-608.

Sembosi, S. J. (2019). Implication of Socio-economic Factors on Land Use and Forest Cover Changes in and Around Magamba Nature Reserve in Tanzania: Perception of Local Stakeholders. Advanced Journal of Social Science, 5(1), 108-117.

Shanmugapriya, N., Bostani, A., Nabavi, A., Sasikala, D., Elangovan, T. & Adilovna, K.S. (2024). Synergizing remote sensing, geospatial intelligence, applied nonlinear analysis, and AI for sustainable environmental monitoring. Communications on Applied Nonlinear Analysis, 31, 281–292

Shaik, A. S., Shaik, N., & Priya, D. C. K. (2024). Predictive Modeling in Remote Sensing Using Machine Learning Algorithms. International Journal Current Science Research,, 7(06), 12-19.

Simon, O., Lyimo, J., & Yamungu, N. (2023). Influence of socioeconomic drivers on land use and cover changes using Remote sensing and GIS in Dar es Salaam, Tanzania. Authorea Preprints.

Singh, A. K. (2021). Landscape Fragmentation and Land Use and Land Cover Analysis of the Hinterland of Ranchi: A Case Study of Kanke Block, 2001 and 2019. Urban India, 41(1), 166-184.

Song, L., Estes, A. B., & Estes, L. D. (2023). A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes. International Journal of Applied Earth Observation and Geoinformation, 116, 103152.

Stratoulias, D., De By, R. A., Zurita-Milla, R., Bijker, W., Tolpekin, V. A., Schulthess, U., & Ahmed, Z. U. (2015, October). The potential of very high spatial remote sensing in smallholder agriculture. In 36th Asian Conference on Remote Sensing, ACRS 2015: Fostering Resilient Growth in Asia (pp. 1533-1539).

Sun, Q., & Li, J. (2024). A method for extracting small water bodies based on DEM and remote sensing images. Scientific reports, 14(1), 760-791.

Thawaba, S., Abu-Madi, M., & Özerol, G. (2017). Effect of land-use/land-cover change on the future of rainfed agriculture in the Jenin Governorate, Palestine. International Journal of Global Environmental Issues, 16(1-3), 176-189.

Tiwari, A. K., Pal, A., & Kanchan, R. (2024). Mapping and Monitoring of Land Use/Land Cover Transformation Using Geospatial Techniques in Varanasi City Development Region, India. Nature Environment & Pollution Technology, 23(1), 365-379.

Tsheko, R. (2022). Non-seasonal Landsat based bare area gain detection in Botswana during 2002 to 2020 Period using Maximum Likelihood Classifier (MLC). South African Journal of Geomatics, 11(1), 84-99.

Wanjura, D. F. (1984). Seasonal spectral characteristics of four crop canopy architectures. Transactions of the ASAE, 27(6), 1734-1738.

Winkler, K., Fuchs, R., Rounsevell, M., & Herold, M. (2022, May). Global land use transitions and their drivers during 1960-2019. In EGU General Assembly Conference Abstracts (pp. EGU22-9145).

Wojkowski, J., Wałęga, A., Radecki-Pawlik, A., Młyński, D., & Lepeška, T. (2022). The influence of land cover changes on landscape hydric potential and river flows: Upper Vistula, Western Carpathians. Catena, 210, 105878.

Xu, X., Kong, W., Wang, L., Wang, T., Luo, P., & Cui, J. (2024). A novel and dynamic land use/cover change research framework based on an improved PLUS model and a fuzzy multiobjective programming model. Ecological Informatics, 80, 1-15.

Xu, Y., Dai, Q. Y., Zou, B., Xu, M., & Feng, Y. X. (2023). Tracing climatic and human disturbance in diverse vegetation zones in China: Over 20 years of NDVI observations. Ecological Indicators, 156, 1-12.

Xue, J., Zhang, X., Chen, S., Hu, B., Wang, N., & Shi, Z. (2024). Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China. Journal of Integrative Agriculture, 23(1), 283-297.

Ying, Q., Hansen, M. C., Potapov, P. V., Tyukavina, A., Wang, L., Stehman, S. V., ... & Hancher, M. (2017). Global bare ground gain from 2000 to 2012 using Landsat imagery. Remote sensing of environment, 194, 161-176.

Wuyep, S. Z., Daloeng, H. M., Isha, A. H., & Arin, H. B. (2020). Application of Remote Sensing and GIS to Detect Land Use and Land Cover Changes in Jos East, Plateau State, Nigeria. Bokkos Journal of Science Report, 1, 1-8.

Younes, A., Ahmad, A., Hanjagi, A. D., & Nair, A. M. (2023). Understanding Dynamics of Land Use & Land Cover Change Using GIS & Change Detection Techniques in Tartous, Syria. European Journal of Geography, 14(3), 20-41.

Zhang, J., Wei, F., Sun, P., Pan, Y., Yuan, Z., & Yun, Y. (2015). A stratified temporal spectral mixture analysis model for mapping cropland distribution through MODIS time-series data. Journal of Agricultural Science, 7(8), 95.

Zhang, X., Ren, W., & Peng, H. (2022). Urban land use change simulation and spatial responses of ecosystem service value under multiple scenarios: A case study of Wuhan, China. Ecological Indicators, 144, 1-17.

Zhang, X., Zhao, T., Xu, H., Liu, W., Wang, J., Chen, X. & Liu, L. (2024). GLC_FCS30D: The first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method. Earth System Science Data, 16, 1353–1381.