Abstract
In the present study, the Geospatial Artificial Intelligence (Geo-AI) is proposed to overcome challenges and phenomena related to land degradation identification in the field by integrating multi-source geospatial data and machine learning modeling techniques. The study area is located in the Batanghari watershed, Sumatra, Indonesia, which is a tropical environment. The existence of environmental problems in the study area can be identified based on land degradation. This study’s novelty model is that it is the first to integrate the six main variables of multi-source geospatial data—topographical, biophysical, bioclimatic, geo-environmental, global human modification, and accessibility—in predicting and mapping the potential of land degradation in Indonesia’s tropical environment. Support Vector Machine (SVM), Minimum Distance (MD), Classification and Regression Trees (CART), Gradient Tree Boost (GTB), Naïve Bayes (NB), Random Forest (RF) are machine learning modeling algorithms used to predic and map land degradation in the study area. The prediction results from these modeling algorithms can be compared and evaluated to get the most optimal performance and accuracy. During the modeling phase, 70% of the reference data were divided into training and 30% into validation. The performance of the model was compared using three accuracy assessment processes (producer accuracy, user accuracy, and overall accuracy). The overall accuracy of the results of the comparison and evaluation of machine learning modeling on the SVM, MD, CART, GTB, NB, and RF algorithms in the study area are 52.8, 34.5, 81.2, 85.8, 36.3, and 86.2%, respectively. Therefore, the study concluded that the RF, CART, and GTB are machine learning modeling algorithms that were proposed to be applied and to produce a land degradation map in the study area. The results of this study can be used by policymakers in making decisions related to environmental management, ecosystem rehabilitation, and restoration. Technically, this can also be applied to other watersheds with similar characteristics. In addition, the results of this study can also be used to develop an intelligent watershed monitoring system to effectively monitor land cover change.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The authors can confirm that all relevant data used in this study is included in the article and/or information files in the manuscript, entitled: “Prediction and mapping of land degradation in the Batanghari watershed, Sumatra, Indonesia: utilizing multi-source geospatial data and machine learning modeling techniques”. The data available on request from the authors and availability of data can be accessed free of charge from the data source.
References
Abinaya V, Poonkuntran S (2019) Classification of satellite image using minimum distance classification algorithm. SSRG Int J Computer Sci Eng (SSRG-IJCSE) 15–18
Abu Hammad A, Tumeizi A (2012) Land degradation: Socioeconomic and environmental causes and consequences in the eastern mediterranean. Land Degrad Dev 23:216–226. https://doi.org/10.1002/ldr.1069
Adepoju KA, Adelabu SA (2020) Improving accuracy evaluation of Landsat-8 OLI using image composite and multisource data with Google Earth Engine. Remote Sens Lett 11:107–116. https://doi.org/10.1080/2150704X.2019.1690792
Ahmad F (2013) Land Degradation Pattern UsingGeo-Information Technology for Kot Addu,Punjab Province, Pakistan. 13:
Alexakis DD, Tapoglou E, Vozinaki AEK, Tsanis IK (2019) Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion. Remote Sens. https://doi.org/10.3390/rs11091106
Ali RR, Samra RMA, Ali RR, Gis-based RMAS (2019) GIS-based land degradation risk assessment of Damietta governorate, Egypt ScienceDirect Damietta governorate, Egypt. Egypt J Basic Appl Sci 2:183–189. https://doi.org/10.1016/j.ejbas.2015.01.001
Arjasakusuma S, Kusuma SS, Saringatin S et al (2021) Shoreline dynamics in East Java Province, Indonesia, from 2000 to 2019 using multi-sensor remote sensing data. Land 10:1–17. https://doi.org/10.3390/land10020100
Ayala Izurieta JE, Jara Santillán CA, Márquez CO et al (2022) Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression. Plant Soil. https://doi.org/10.1007/s11104-022-05506-1
Bajocco S, De Angelis A, Perini L et al (2012) The impact of land use/land cover changes on land degradation dynamics: a mediterranean case study. Environ Manage 49:980–989. https://doi.org/10.1007/s00267-012-9831-8
Bayad M, Chau HW, Trolove S et al (2020) Time series of remote sensing and water deficit to predict the occurrence of soil water repellency in New Zealand pastures. ISPRS J Photogramm Remote Sens 169:292–300. https://doi.org/10.1016/j.isprsjprs.2020.09.024
Bhanumurthy V, Rao KV, Rao SS et al (2014) Enabling heterogenous multi-scale database for emergency service functions through geoinformation technologies. Int Arch Photogram Remote Sens Spatial Inf Sci—ISPRS Arch XL. https://doi.org/10.5194/isprsarchives-XL-8-7-2014
Bittencourt HR, Clarke RT (2003) Use of Classification and regression trees (CART) to classify remotely-sensed digital images. Int Geosci Remote Sens Symp (IGARSS) 6:3751–3753. https://doi.org/10.1109/igarss.2003.1295258
Bui DH, Mucsi L (2022) Comparison of layer-stacking and dempster-shafer theory-based methods using sentinel-1 and sentinel-2 data fusion in urban land cover mapping. Geo-Spatial Inf Sci 00:1–14. https://doi.org/10.1080/10095020.2022.2035656
Bui HB, Nguyen H, Choi Y et al (2019) A novel artificial intelligence technique to estimate the gross calorific value of coal based on meta-heuristic and support vector regression algorithms. Appl Sci (Switzerland). https://doi.org/10.3390/app9224868
Bui QT, Chou TY, Van HT et al (2021) Gradient boosting machine and object-based cnn for land cover classification. Remote Sens 13:1–15. https://doi.org/10.3390/rs13142709
Chakrabortty R, Pal SC (2023) Modeling soil erosion susceptibility using GIS-based different machine learning algorithms in monsoon dominated diversified landscape in India. Model Earth Syst Env. https://doi.org/10.1007/s40808-022-01681-3
Choi Y, Lim CH, Ryu J, Jeon SW (2017) Bioclimatic classification of Northeast Asia reflecting social factors: development and characterization. Sustainability (Switzerland). https://doi.org/10.3390/su9071137
Chu D (2020) Fractional vegetation cover BT—remote sensing of land use and land cover in mountain region: a comprehensive study at the Central Tibetan Plateau. In: Chu D (ed). Springer Singapore, Singapore, pp 195–207
Chu L, Oloo F, Bergstedt H, Blaschke T (2020) Assessing the link between human modification and changes in land surface temperature in hainan, china using image archives from google earth engine. Remote Sens. https://doi.org/10.3390/rs12050888
de Medeiros JF, Cestaro LA (2020) O emprego de técnicas estatísticas para a compartimentação geoambiental da Serra de Martins-RN. Sociedade Natureza 32:404–415. https://doi.org/10.14393/sn-v32-2020-46691
Dubovyk O (2017) The role of Remote Sensing in land degradation assessments: opportunities and challenges. Eur J Remote Sens 50:601–613. https://doi.org/10.1080/22797254.2017.1378926
Duda R, Hart P, Stork D (2001) Minimum distance classifier. Pattern Classification 23–24
ESDM (2022) ESDM one map (Geo-Map). https://geoportal.esdm.go.id/geologi/
Ewunetu A, Simane B, Teferi E (2021) Mapping and Quantifying Comprehensive Land Degradation Status Using Spatial Multicriteria Evaluation Technique in the Headwaters Area of Upper Blue Nile River
Fang P, Zhang X, Wei P et al (2020) The classification performance and mechanism of machine learning algorithms in winter wheat mapping using Sentinel-2 10 m resolution imagery. Appl Sci (Switzerland). https://doi.org/10.3390/app10155075
Farr TG, Rosen PA, Caro E et al (2007) The shuttle radar topography mission. Rev Geophys. https://doi.org/10.1029/2005RG000183
Gao J, Liu Y (2010) Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. Int J Appl Earth Obs Geoinf 12:9–16. https://doi.org/10.1016/j.jag.2009.08.003
Ghorbanian A, Zaghian S, Asiyabi RM et al (2021) Mangrove ecosystem mapping using sentinel-1 and sentinel-2 satellite images and random forest algorithm in google earth engine. Remote Sens. https://doi.org/10.3390/rs13132565
Gichenje H, Pinto-Correia T, Godinho S (2019) An analysis of the drivers that affect greening and browning trends in the context of pursuing land degradation-neutrality. Remote Sens Appl: Soc Env 15:100251. https://doi.org/10.1016/j.rsase.2019.100251
Giuliani G, Chatenoux B, Benvenuti A et al (2020) Monitoring land degradation at national level using satellite earth observation time-series data to support SDG15–exploring the potential of data cube. Big Earth Data 4:3–22. https://doi.org/10.1080/20964471.2020.1711633
Gonzales-Inca C, Calle M, Croghan D et al (2022) Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling: review of current applications and trends. Water (Switzerland). https://doi.org/10.3390/w14142211
Grinand C, Vieilledent G, Razafimbelo T et al (2020) Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools. Land Degrad Dev 31:1699–1712. https://doi.org/10.1002/ldr.3526
Guo H, Nguyen H, Vu DA, Bui XN (2021) Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach. Res Policy 74:101474. https://doi.org/10.1016/j.resourpol.2019.101474
Habibi V, Ahmadi H, Moeini MJ (2021) Prediction of land degradation by machine learning methods: a case study from Sharifabad Watershed, Central Iran. Earth Sci Res J 25:353–362
Hamid B, Massinissa B, Nabila G (2022) Landslide susceptibility mapping using GIS-based statistical and machine learning modeling in the city of Sidi Abdellah, Northern Algeria. Model Earth Syst Env. https://doi.org/10.1007/s40808-022-01633-x
Hengl T, Wheeler I (2018) Soil organic carbon content in x 5g/kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (Version v02) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1475457
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Clim 25:1965–1978. https://doi.org/10.1002/joc.1276
Jati HF, Darsono SNAC, Hermawan DT et al (2019) Awareness and knowledge assessment of sustainable development goals among university students. Jurnal Ekonomi Studi Pembangunan. https://doi.org/10.18196/jesp.20.2.5022
Jiao W, Hao X, Qin C (2021) The image classification method with cnn-xgboost model based on adaptive particle swarm optimization. Information (Switzerland) 12:1–22. https://doi.org/10.3390/info12040156
Jiménez-Rodríguez DL, Gao Y, Solórzano JV et al (2022) Mapping forest degradation and contributing factors in a tropical dry forest. Front Environ Sci 10:1–16. https://doi.org/10.3389/fenvs.2022.912873
Juita E, Barlian E, Hermon D et al (2020) Morphological changes batanghari watershed due to illegal mine: case study of solok selatan regency-Indonesia. J Adv Res Dyn Control Syst 12:664–670. https://doi.org/10.5373/JARDCS/V12SP7/20202156
Kandasamy S (2014) Leaf Area Index ( LAI ) monitoring at global scale : improved definition, continuity and consistency of LAI estimates from kilometric satellite observations. https://doi.org/10.13140/2.1.1042.9768
Karakizi C, Karantzalos K, Vakalopoulou M, Antoniou G (2018) Detailed land cover mapping from multitemporal Landsat-8 data of different cloud cover. Remote Sens 10:1–25. https://doi.org/10.3390/rs10081214
Karamesouti M, Detsis V, Kounalaki A et al (2015) Land-use and land degradation processes affecting soil resources: evidence from a traditional Mediterranean cropland (Greece). CATENA. https://doi.org/10.1016/j.catena.2015.04.010
Kennedy CM, Oakleaf JR, Theobald DM et al (2019) Managing the middle: a shift in conservation priorities based on the global human modification gradient. Global Change Biol 25:811–826. https://doi.org/10.1111/gcb.14549
Kganyago M, Mhangara P, Alexandridis T et al (2020) Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape. Remote Sens Lett 11:883–892. https://doi.org/10.1080/2150704X.2020.1767823
Kioupi V, Voulvoulis N (2020) Paper 10 (contribution of HE). Sustainability (Switzerland) 12
Knijff J, Jones R, Montanarella L (2000) Soil erosion risk assessment in Europe. Soil Erosion Risk Assessment in Europe
Krishnan AG, Krishnamoorthy Lakshmi P, Chellappan S (2023) Artificial neural network modelling approach for the prediction of turbidity removal efficiency of PACl and Moringa Oleifera in water treatment plants. Model Earth Syst Environ. https://doi.org/10.1007/s40808-022-01651-9
Kumar BP, Babu KR, Anusha BN, Rajasekhar M (2022) Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI/TIRS, LST, and NDVI approach. Environ Challenges. https://doi.org/10.1016/j.envc.2022.100578
Kust G, Andreeva O, Cowie A (2017) Land degradation neutrality: concept development, practical applications and assessment. J Environ Manage 195:16–24. https://doi.org/10.1016/j.jenvman.2016.10.043
Lestariningsih ID, Widianto W, Agustina C et al (2018) Relationship between land degradation biophysical and social factors in Lekso Watershed, East Java, Indonesia. J Degraded Mining Lands Management. https://doi.org/10.15243/jdmlm20180531283
Li X, Gao J, Zhang J (2018) A topographic perspective on the distribution of degraded meadows and their changes on the Qinghai-Tibet Plateau, West China. Land Degradation Dev 29:1574–1582. https://doi.org/10.1002/ldr.2952
Liang S, Zhang CC, Liu SS et al (2015) Covariance structure analysis of health-related indices for the elderly at home, focusing on subjective feelings of health. Proc Natl Acad Sci 3:1–15
Liang S, Wang JBT-ARS (Second E (eds) (2020a) Chapter 11—Fraction of absorbed photosynthetically active radiation. Academic Press, pp 447–476
Liang S, Wang JBT-ARS (Second E (eds) (2020b) Chapter 12—Fractional vegetation cover. Academic Press, pp 477–510
Linard C, Tatem AJ, Gilbert M (2013) Modelling spatial patterns of urban growth in Africa. Appl Geogr 44:23–32. https://doi.org/10.1016/j.apgeog.2013.07.009
Lyons DS, Dobrowski SZ, Holden ZA et al (2021) Soil moisture variation drives canopy water content dynamics across the western US. Remote Sens Environ 253:112233. https://doi.org/10.1016/j.rse.2020.112233
Mainuri ZG, Owino JO (2014) Linking landforms and land use to land degradation in the Middle River Njoro Watershed. Int Soil Water Conserv Res 2:1–10. https://doi.org/10.1016/S2095-6339(15)30001-0
Martin RE, Asner GP, Francis E et al (2018) Remote measurement of canopy water content in giant sequoias (Sequoiadendron giganteum) during drought. For Ecol Manage 419–420:279–290. https://doi.org/10.1016/j.foreco.2017.12.002
Maxwell AE, Warner TA, Strager MP et al (2015) Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation. Int J Remote Sens 36:954–978. https://doi.org/10.1080/01431161.2014.1001086
McCarty DA, Kim HW, Lee HK (2020) Evaluation of light gradient boosted machine learning technique in large scale land use and land cover classification. Environ—MDPI 7:1–22. https://doi.org/10.3390/environments7100084
Mickovski SB, Alves G (2019) The role of geo-environmental factors in landscape and visual assessment for shallow-water offshore structures. Lecture Notes Civil Eng 18:81–87. https://doi.org/10.1007/978-981-13-2306-5_9
Ministry of Environmental and Forestry (2020) The state of Indonesia's forests 2020. Ministry of Environmental and Forestry, Republic of Indonesia. ISBN: 978-602-8358-91-0
Mohajane M, Costache R, Karimi F et al (2021) Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators 129:107869. https://doi.org/10.1016/j.ecolind.2021.107869
Mohammed MAA, Khleel NAA, Szabó NP, Szűcs P (2022) Modeling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State, Sudan. Model Earth Syst Environ. https://doi.org/10.1007/s40808-022-01638-6
Mokarram M, Sathyamoorthy D (2015) Modeling the relationship between elevation, aspect and spatial distribution of vegetation in the Darab Mountain, Iran using remote sensing data. Model Earth Syst Env 1:30. https://doi.org/10.1007/s40808-015-0038-x
Möller M, Zepp S, Wiesmeier M et al (2022) Scale-specific prediction of topsoil organic carbon contents using terrain attributes and SCMaP soil reflectance composites. Remote Sens. https://doi.org/10.3390/rs14102295
Mosavi A, Sajedi-Hosseini F, Choubin B et al (2020) Susceptibility mapping of soil water erosion using machine learning models. Water (Switzerland) 12:1–17. https://doi.org/10.3390/w12071995
Mujiyo HT, Widijanto H, Herawati A (2021) Effects of land use on soil degradation in Giriwoyo, Wonogiri, Indonesia. J Degraded Mining Lands Manage 9:3063–3072. https://doi.org/10.15243/JDMLM.2021.091.3063
Nascimento CM, de Sousa MW, Quiñonez Silvero NE et al (2021) Soil degradation index developed by multitemporal remote sensing images, climate variables, terrain and soil atributes. J Environ Manage. https://doi.org/10.1016/j.jenvman.2020.111316
Navale V, Mhaske S (2022) Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) model for forecasting groundwater level in the Pravara River Basin. Model Earth Syst Environ, India. https://doi.org/10.1007/s40808-022-01639-5
Nichol J, Wong MS (2005) Detection and interpretation of landslides using satellite images. Land Degrad Dev 16:243–255. https://doi.org/10.1002/ldr.648
Oktanisa I, Supianto AA (2018) Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank a Comparison of Classification Techniques in Data Mining for. Teknologi Informasi dan Ilmu Komputer 5:567–576. https://doi.org/10.25126/jtiik20185958
Oo TK, Arunrat N, Sereenonchai S et al (2022) Comparing four machine learning algorithms for land cover classification in gold mining: a case study of kyaukpahto gold mine, Northern Myanmar. Sustainability 14:10754. https://doi.org/10.3390/su141710754
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222. https://doi.org/10.1080/01431160412331269698
Pasqualotto N, Bolognesi SF, Belfiore OR et al. (2019) Canopy chlorophyll content and LAI estimation from Sentine1–2: Vegetation indices and Sentine1–2 Leve1-2A automatic products comparison. 2019 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2019—Proceedings 301–306. https://doi.org/10.1109/MetroAgriFor.2019.8909218
Phan TN, Kuch V, Lehnert LW (2020) Land cover classification using google earth engine and random forest classifier-the role of image composition. Remote Sens. https://doi.org/10.3390/RS12152411
Ramadhan S, Hermansah RB, Yasin S (2016) Pengaruh Konversi Hutan Menjadi Kebun Kelapa Sawit Terhadap Kualitas Air di Sub DAS Batanghari Hilir. Seminar Nasional Pembangunan Pertanian 2:278–284
Ramayanti LA, Yuwono BD, Awaluddin M (2015) Pemetaan tingkat lahan kritis dengan menggunakan penginderaan jauh dan Sistem Informasi Geografi (Studi Kasus: Kabupaten Blora). J Geodesi Undip 4(2):200–207 ((in Bahasa))
Rana VK, Venkata Suryanarayana TM (2020) Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sens Appl: Soc Environ 19:100351. https://doi.org/10.1016/j.rsase.2020.100351
Rawat KS, Singh SK (2018) Appraisal of Soil conservation capacity using NDVI model-based C factor of RUSLE model for a semi arid ungauged watershed: a case study. Water Conserv Sci Eng 3:47–58. https://doi.org/10.1007/s41101-018-0042-x
Reith J, Ghazaryan G, Muthoni F, Dubovyk O (2021) Assessment of land degradation in semiarid Tanzania-using multiscale remote sensing datasets to support sustainable development goal 15.3. Remote Sens. https://doi.org/10.3390/rs13091754
Rodrigues SG, Silva MM, Alencar MH (2021) A proposal for an approach to mapping susceptibility to landslides using natural language processing and machine learning. Landslides 18:2515–2529. https://doi.org/10.1007/s10346-021-01643-3
Rukhovich DI, Koroleva PV, Rukhovich DD, Kalinina NV (2021) The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution. Remote Sens 13:1–28. https://doi.org/10.3390/rs13010155
Sallata MK (2017) Pentingnya Aplikasi Teknik Konservasi Air Dengan Metode Struktur Fisik di Wilayah Hulu DAS. Info Teknis Eboni 14:47–62
Shah A, Ali K, Nizami SM (2021) Four decadal urban land degradation in Pakistan a case study of capital city islamabad during 1979–2019. Environ Sustainability Indicators 10:100108. https://doi.org/10.1016/j.indic.2021.100108
Shetty S, Gupta PK, Belgiu M, Srivastav SK (2021) Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine. Remote Sensing. 13(8):1433. https://doi.org/10.3390/rs13081433
Sunartomo AF (2011) Inventarisasi dan sebaran lahan kritis di kabupaten situbondo. J-Sep 5:12–22
Tang J, Liu G, Xie Y et al (2022) Effect of topographic variations and tillage methods on gully erosion in the black soil region: a case study from northeast China. Land Degrad Dev n/a. https://doi.org/10.1002/ldr.4423
Tarigan SD (2016) Land cover change and its impact on flooding frequency of batanghari watershed, Jambi Province, Indonesia. Procedia Environ Sci 33:386–392. https://doi.org/10.1016/j.proenv.2016.03.089
Thaler EA, Larsen IJ, Yu Q (2019) A new index for remote sensing of soil organic carbon based solely on visible wavelengths. Soil Sci Soc Am J 83:1443–1450. https://doi.org/10.2136/sssaj2018.09.0318
Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS One 10:e0143619
Tolche AD, Gurara MA, Pham QB, Anh DT (2021) Modelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach. Geocarto Int. https://doi.org/10.1080/10106049.2021.1959656
Torabi Haghighi A, Darabi H, Karimidastenaei Z et al (2021) Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms for the Pole-Doab watershed, Iran. Environ Earth Sci 80:1–21. https://doi.org/10.1007/s12665-020-09327-2
Tu Y, Jia K, Liang S et al (2020) Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model. Int J Digital Earth 13:487–503. https://doi.org/10.1080/17538947.2018.1531438
UU No. 37 (2014) Undang-Undang Nomor 37 Tahun 2014 tentang Konservasi Tanah dan Air mengatur perencanaan, penyelenggaraan serta pembinaan dan pengawasan konservasi tanah dan air. (in Bahasa)
Upreti D, Huang W, Kong W et al (2019) A comparison of hybrid machine learning algorithms for the retrieval of wheat biophysical variables from sentinel-2. Remote Sens. https://doi.org/10.3390/rs11050481
Utami N, Sapei A (2018) Analisis Perubahan Penggunaan Lahan Das Batanghari Jambi. Prosiding Seminar Nasional PERTETA 2018:224–230
Verstraete NG and MM (2009) Fraction of absorbed photosynthetically active radiation (FAPAR). Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables 1–2
Vopham T, Hart JE, Laden F, Chiang YY (2018) Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environ Health: A Global Access Sci Source. https://doi.org/10.1186/s12940-018-0386-x
Wahyunto, Dariah AI (2014) Land degradation in Indonesia: existing conditions, characteristics and uniformity in the definition of supporting the movement towards a single map. Land Res J 8(2):81–93
Waldner F, Lambert MJ, Li W et al (2015) Land cover and crop type classification along the season based on biophysical variables retrieved from multi-sensor high-resolution time series. Remote Sens 7:10400–10424. https://doi.org/10.3390/rs70810400
Waltari E, Schroeder R, Mcdonald K et al (2014) Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling. Methods Ecol Evol 5:1033–1042. https://doi.org/10.1111/2041-210X.12264
Weiss A (2001) Topographic position and landforms analysis. In Poster presentation, ESRI User Conference, San Diego, CA 200
Weiss DJ, Nelson A, Vargas-Ruiz CA et al (2020) Global maps of travel time to healthcare facilities. Nat Med 26:1835–1838. https://doi.org/10.1038/s41591-020-1059-1
Wibowo A, Ismullah IH, Dipokusumo BS, Wikantika K (2012) Land degradation model based on vegetation and erosion aspects using remote sensing data. ITB J Sci. https://doi.org/10.5614/itbj.sci.2012.44.1.3
Wijitkosum S (2021) Factor influencing land degradation sensitivity and desertification in a drought prone watershed in Thailand. Int Soil Water Conserv Res 9:217–228. https://doi.org/10.1016/j.iswcr.2020.10.005
World Agroforestry Centre (2017) Science for a food-secure future. Annual report 2016-2017. World Agroforestry Centre, United Nations
Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping: a review. J Plant Ecol 1:9–23. https://doi.org/10.1093/jpe/rtm005
Yang B, Li S (2013) Remote sense image classification based on CART algorithm. Adv Mater Res 864–867:2782–2786. https://doi.org/10.4028/www.scientific.net/AMR.864-867.2782
Yang C, Feng M, Song L et al (2021) Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments. Sci Rep 11:1–9. https://doi.org/10.1038/s41598-021-98143-0
Yaojie Y, Min L, Lin W, A-Xing Z, (2019) A data-mining-based approach for aeolian desertification susceptibility assessment: a case-study from Northern China. Land Degrad Dev 30:1968–1983. https://doi.org/10.1002/ldr.3393
Yue J, Guo W, Yang G et al (2021) Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing. Plant Methods 17:1–16. https://doi.org/10.1186/s13007-021-00752-3
Zanaga D, Van De Kerchove R, Daems D, De Keersmaecker W, Brockmann C, Kirches G, Wevers J, Cartus O, Santoro M, Fritz S, Lesiv M, Herold M, Tsendbazar NE, Xu P, Ramoino F, Arino O (2022) ESA WorldCover 10 m 2021 v200. https://doi.org/10.5281/zenodo.7254221
Zhang F, Zhou G, Nilsson C (2015) Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China. J Plant Ecol 8:429–435. https://doi.org/10.1093/jpe/rtu027
Zhang C, Qi X, Wang K et al (2017) The application of geospatial techniques in monitoring karst vegetation recovery in southwest China: a review. Prog Phys Geogr 41:450–477. https://doi.org/10.1177/0309133317714246
Zhang X, He Y, Wang C et al (2019) Estimation of corn canopy chlorophyll content using derivative spectra in the O2–A absorption band. Front Plant Sci 10:1–13. https://doi.org/10.3389/fpls.2019.01047
Zhao Q, Yu S, Zhao F et al (2019) Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. For Ecol Manage 434:224–234. https://doi.org/10.1016/j.foreco.2018.12.019
Zheng G, Moskal LM (2009) Retrieving Leaf area index (LAI) using remote sensing: theories, methods and sensors. Sensors 9:2719–2745. https://doi.org/10.3390/s90402719
Žížala D, Juřicová A, Zádorová T et al (2019) Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic. Eur J Remote Sens 52:108–122. https://doi.org/10.1080/22797254.2018.1482524
Zwirowicz-Rutkowska A, Michalik A (2016) The use of spatial data infrastructure in environmental management:an example from the spatial planning practice in Poland. Environ Manage 58:619–635. https://doi.org/10.1007/s00267-016-0732-0
Acknowledgements
This article is part of the progress of research activities entitled “Development of Geospatial Artificial Intelligence Models for Determining Critical Lands in Monitoring the Health of the Batanghari Watershed, Sumatra, Indonesia”. The authors are grateful for the funds provided by Program House of Management of Water and Lake Resources No. B-22/III/PR.03/1/2022, Research Organization for Earth Science and Maritime, National Research and Innovation Agency (BRIN) Indonesia.
Funding
This study was funded by Program House of Management of Water and Lake Resources No. B-22/III/PR.03/1/2022, Research Organization for Earth Science and Maritime, National Research and Innovation Agency (BRIN) Indonesia.
Author information
Authors and Affiliations
Contributions
FY, PDR, IBP. MAS main contributors in constructing experiments and research; FY, PDR, IBP, MAS, GAC, GN, ADS, SN, and SB wrote the paper and performed the experiments; FY, PDR, IBP, ADS, and SN analyzed the data; FY, GAC, GN, SN, and SB pre-processed the datasets; All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yulianto, F., Raharjo, P.D., Pramono, I.B. et al. Prediction and mapping of land degradation in the Batanghari watershed, Sumatra, Indonesia: utilizing multi-source geospatial data and machine learning modeling techniques. Model. Earth Syst. Environ. 9, 4383–4404 (2023). https://doi.org/10.1007/s40808-023-01761-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40808-023-01761-y