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2022 | OriginalPaper | Buchkapitel

3. Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

verfasst von : Rubaiya Binte Mustafiz, Ryozo Noguchi, Tofael Ahamed

Erschienen in: Remote Sensing Application

Verlag: Springer Nature Singapore

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Abstract

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, LST, and vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truth yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km²) was highly suitable, 41% of the land (1747 km²) was moderately suitable, and 10% of the land (426 km²) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km²) was identified as being highly suitable; 39% of the land (2045 km²) was identified as being moderately suitable, and 7% of the land (298 km²) was identified as being marginally suitable. Additionally, 6% (256 km²) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R² = 0.773), ARVI (R² = 0.689), SARVI (R² = 0.711), MSAVI (R² = 0.745), and OSAVI (R² = 0.812) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R² = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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Acharjee TK, Van Halsema G, Ludwig F, Hellegers P (2017) Declining trends of water requirements of dry season Boro rice in the north-west Bangladesh. Agric Water Manag 180:148–159. https://doi.org/10.1016/j.agwat.2016.11.014CrossRef

Alamgir S, Furuya J, Kobayashi S, Mostafiz RB, Ahmed R (2020) Farm income, inequality, and poverty among farm families of a flood-prone area in Bangladesh: climate change vulnerability assessment. GeoJournal 86:2861–2885. https://doi.org/10.1007/s10708-020-10231-2CrossRef

Amin SR, Zhang J, Yang M (2015) Effects of climate change on the yield and cropping area of major food crops: a case of Bangladesh. Sustain For 7:898–915. https://doi.org/10.3390/su7010898CrossRef

Ashford SA, Sitar N, Lysmer J, Deng N (1997) Topographic effects on the seismic response of steep slopes. Bull Seismol Soc Am 87:701–709CrossRef

Ayehu GT, Besufekad SA (2015) Land suitability analysis for rice production: a GIS based multi-criteria decision approach. Am J Geogr Inf Syst 4:95–104. https://doi.org/10.5923/j.ajgis.20150403.02CrossRef

Bangladesh Bureau of Statistics (BBS) (2011) Statistics and Informatics Division (SID) Ministry of Planning: Population and housing census 2011. Bangladesh Bureau of Statistics, Dhaka

Bangladesh Bureau of Statistics (BBS) (2018) Statistical pocket book Bangladesh 2016. Ministry of Planning, Dhaka

Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17:141. https://doi.org/10.1287/mnsc.17.4.b141CrossRef

Bozdağ A, Yavuz F, Günay AS (2016) AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) county. Environ Earth Sci 75:813. https://doi.org/10.1007/s12665-016-5558-9CrossRef

Buthelezi NN, Hughes JC, Modi A (2013) The use of scientific and indigenous knowledge in agricultural land evaluation and soil fertility studies of two villages in KwaZulu-Natal, South Africa. Afr J Agric Res 8:507–518

Campos I, González-Gómez L, Villodre J, González-Piqueras J, Suyker AE, Calera A (2018) Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields. Field Crop Res 216:175–188. https://doi.org/10.1016/j.fcr.2017.11.025CrossRef

Ceglar A, Toreti A, Prodhomme C, Zampieri M, Turco M, Doblas-Reyes FJ (2018) Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast. Sci Rep 8:1–9. https://doi.org/10.1038/s41598-018-19586-6CrossRef

Cho MA, Skidmore AK (2008) Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. Int J Remote Sens 30:499–515. https://doi.org/10.1080/01431160802392596CrossRef

Das AC, Noguchi R, Ahamed T (2020) Integrating an expert system, GIS, and satellite remote sensing to evaluate land suitability for sustainable tea production in Bangladesh. Remote Sens 12:4136. https://doi.org/10.3390/rs12244136CrossRef

De Lima TM, Weindorf DC, Curi N, Guilherme LR, Lana RM, Ribeiro BT (2019) Elemental analysis of Cerrado agricultural soils via portable X-ray fluorescence spectrometry: inferences for soil fertility assessment. Geoderma 353:264–272. https://doi.org/10.1016/j.geoderma.2019.06.045CrossRef

El Kateb H, Zhang H, Zhang P, Mosandl R (2013) Soil erosion and surface runoff on different vegetation covers and slope gradients: a field experiment in Southern Shaanxi Province, China. Catena 105:1–10. https://doi.org/10.1016/j.catena.2012.12.012CrossRef

Elsheikh R, Shariff ARBM, Amiri F, Ahmad NB, Balasundram SK, Soom MAM (2013) Agriculture land suitability evaluator (ALSE): a decision and planning support tool for tropical and subtropical crops. Comput Electron Agric 93:98–110. https://doi.org/10.1016/j.compag.2013.02.003CrossRef

Ennouri K, Kallel A (2019) Remote sensing: an advanced technique for crop condition assessment. Math Probl Eng 2019:1–8. https://doi.org/10.1155/2019/9404565CrossRef

Essougong UPK, Slingerland M, Mathé S, Vanhove W, Ngome PIT, Boudes P, Giller KE, Woittiez LS, Leeuwis C (2020) Farmers’ perceptions as a driver of agricultural practices: understanding soil fertility management practices in cocoa agroforestry systems in Cameroon. Hum Ecol 48:709–720. https://doi.org/10.1007/s10745-020-00190-0CrossRef

Fern RR, Foxley EA, Bruno A, Morrison ML (2018) Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecol Indic 94:16–21. https://doi.org/10.1016/j.ecolind.2018.06.029CrossRef

Food and Agriculture Organization (1976) A framework for land evaluation. Food and Agriculture Organization, Rome

Gerpacio RV, Pingali PL (2007) Tropical and subtropical maize in Asia: production systems, constraints, and research priorities. CIMMYT, Texcoco

Gilabert M, González-Piqueras J, Garcia-Haro F, Melia J (2002) A generalized soil-adjusted vegetation index. Remote Sens Environ 82:303–310. https://doi.org/10.1016/s0034-4257(02)00048-2CrossRef

Gitari HI, Gachene CKK, Karanja NN, Kamau S, Nyawade S, Schulte-Geldermann E (2019) Potato-legume intercropping on a sloping terrain and its effects on soil physico-chemical properties. Plant Soil 438:447–460. https://doi.org/10.1007/s11104-019-04036-7CrossRef

GRiSP (2013) Rice almanac, 4th edn. Global Rice Science Partnership, Los Baños

Habibie MI, Noguchi R, Shusuke M, Ahamed T (2019) Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. GeoJournal 86:777–807. https://doi.org/10.1007/s10708-019-10091-5CrossRef

Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352. https://doi.org/10.1016/j.rse.2003.12.013CrossRef

Huete A (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. https://doi.org/10.1016/0034-4257(88)90106-xCrossRef

Jeevalakshmi D, Narayana Reddy S, Manikiam B (2017) Land surface temperature retrieval from LANDSAT data using emissivity estimation. Int J Appl Eng Res 12:9679–9687

Jiang Z, Huete AR, Chen J, Chen Y, Li J, Yan G, Zhang X (2006) Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens Environ 101:366–378. https://doi.org/10.1016/j.rse.2006.01.003CrossRef

Jyoti NA, Lal R, Das AK (2015) Ethnopedology and soil quality of bamboo (Bambusa sp.) based agroforestry system. Sci Total Environ 521:372–379. https://doi.org/10.1016/j.scitotenv.2015.03.059CrossRef

Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens 30:261–270. https://doi.org/10.1109/36.134076CrossRef

Kazemi H, Akinci H (2018) A land use suitability model for rainfed farming by multi-criteria decision-making analysis (MCDA) and geographic information system (GIS). Ecol Eng 116:1–6. https://doi.org/10.1016/j.ecoleng.2018.02.021CrossRef

Kennedy CM, Hawthorne PL, Miteva DA, Baumgarten L, Sochi K, Matsumoto M, Evans JS, Polasky S, Hamel P, Vieira EM et al (2016) Optimizing land use decision-making to sustain Brazilian agricultural profits, biodiversity and ecosystem services. Biol Conserv 204:221–230. https://doi.org/10.1016/j.biocon.2016.10.039CrossRef

Kihoro J, Bosco NJ, Murage H (2013) Suitability analysis for rice growing sites using a multicriteria evaluation and GIS approach in great Mwea region, Kenya. Springerplus 2:265. https://doi.org/10.1186/2193-1801-2-265CrossRef

Kim MS, Daughtry CST, Chappelle EW, McMurtrey JE, Walthall CL (1994) The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation. In Proceedings of the 6th symposium on physical measurements and signatures in remote sensing, pp 299–306

Koulouri M, Giourga C (2007) Land abandonment and slope gradient as key factors of soil erosion in Mediterranean terraced lands. Catena 69:274–281. https://doi.org/10.1016/j.catena.2006.07.001CrossRef

Li Y, Chen W, Zhang Y, Tao C, Xiao R, Tan Y (2020) Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning. Remote Sens Environ 250:112045. https://doi.org/10.1016/j.rse.2020.112045CrossRef

Mao Z-H, Deng L, Duan F-Z, Li X-J, Qiao D-Y (2020) Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize. Int J Appl Earth Obs Geoinf 93:102198. https://doi.org/10.1016/j.jag.2020.102198CrossRef

Mazza A, Gargiulo M, Scarpa G, Gaetano R (2018) Estimating the NDVI from SAR by convolutional neural networks. In: Proceedings of the IGARSS 2018 IEEE international geoscience and remote sensing symposium. IEEE, New York, pp 1954–1957CrossRef

Mitchell S, Cohen K (2014) Fuzzy logic decision making for autonomous robotic applications. In: Proceedings of the 2014 IEEE 6th international conference on awareness science and technology (iCAST). IEEE, New York, pp 1–6

Mottaleb KA, Kruseman G, Erenstein O (2018) Determinants of maize cultivation in a land-scarce rice-based economy: the case of Bangladesh. J Crop Improv 32:453–476. https://doi.org/10.1080/15427528.2018.1446375CrossRef

Nahusenay A, Kibebew K (2015) Land suitability evaluation in Wadla Delanta Massif of north central highlands of Ethiopia for rainfed crop production. Afr J Agric Res 10:1595–1611. https://doi.org/10.5897/ajar2014.9248CrossRef

Niemeijer D, Mazzucato V (2003) Moving beyond indigenous soil taxonomies: local theories of soils for sustainable development. Geoderma 111:403–424. https://doi.org/10.1016/S0016-7061(02)00274-4CrossRef

Noorollahi E, Fadai D, Shirazi MA, Ghodsipour SH (2016) Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP)—a case study of Iran. Energies 9:643. https://doi.org/10.3390/en9080643CrossRef

Novara A, Gristina L, Sala G, Galati A, Crescimanno M, Cerdà A, Badalamenti E, La Mantia T (2017) Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Sci Total Environ 576:420–429. https://doi.org/10.1016/j.scitotenv.2016.10.123CrossRef

Olivero J, Real R, Márquez AL (2011) Fuzzy chorotypes as a conceptual tool to improve insight into biogeographic patterns. Syst Biol 60:645–660. https://doi.org/10.1093/sysbio/syr026CrossRef

Pilevar AR, Matinfar HR, Sohrabi A, Sarmadian F (2020) Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming. Ecol Indic 110:105887. https://doi.org/10.1016/j.ecolind.2019.105887CrossRef

Pimentel D, Burgess M (2013) Soil erosion threatens food production. Agriculture 3:443–463. https://doi.org/10.3390/agriculture3030443CrossRef

Purnamasari RA, Noguchi R, Ahamed T (2019) Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing. Comput Electron Agric 166:105018. https://doi.org/10.1016/j.compag.2019.105018CrossRef

Radočaj D, Jurišić M, Gašparović M, Plaščak I (2020) Optimal soybean (Glycine max L.) land suitability using GIS-based multicriteria analysis and sentinel-2 multitemporal images. Remote Sens 12:1463. https://doi.org/10.3390/rs12091463CrossRef

Ren H, Feng G (2014) Are soil-adjusted vegetation indices better than soil-unadjusted vegetation indices for above-ground green biomass estimation in arid and semi-arid grasslands? Grass Forage Sci 70:611–619. https://doi.org/10.1111/gfs.12152CrossRef

Ren H, Zhou G, Zhang F (2018) Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. Remote Sens Environ 209:439–445. https://doi.org/10.1016/j.rse.2018.02.068CrossRef

Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43:1541–1552

Romano G, Sasso PD, Liuzzi GT, Gentile F (2015) Multi-criteria decision analysis for land suitability mapping in a rural area of Southern Italy. Land Use Policy 48:131–143. https://doi.org/10.1016/j.landusepol.2015.05.013CrossRef

Samanta S, Pal B, Pal DK (2011) Land suitability analysis for rice cultivation based on multi-criteria decision approach through GIS. Data Base Int J Sci Emerg Technol 2:12–20

Senanayake S, Pradhan B, Huete A, Brennan J (2020) Assessing soil erosion hazards using land-use change and landslide frequency ratio method: a case study of Sabaragamuwa Province, Sri Lanka. Remote Sens 12:1483. https://doi.org/10.3390/rs12091483CrossRef

Seyedmohammadi J, Sarmadian F, Jafarzadeh AA, McDowell RW (2019) Development of a model using matter element, AHP and GIS techniques to assess the suitability of land for agriculture. Geoderma 352:80–95. https://doi.org/10.1016/j.geoderma.2019.05.046CrossRef

Shimoda S, Kanno H, Hirota T (2018) Time series analysis of temperature and rainfall-based weather aggregation reveals significant correlations between climate turning points and potato (Solanum tuberosum L) yield trends in Japan. Agric For Meteorol 263:147–155. https://doi.org/10.1016/j.agrformet.2018.08.005CrossRef

Somvanshi SS, Kumari M (2020) Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data. Appl Comput Geosci 7:100032. https://doi.org/10.1016/j.acags.2020.100032CrossRef

Sonobe R, Yamaya Y, Tani H, Wang X, Kobayashi N, Mochizuki K-I (2018) Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. J Appl Remote Sens 12:026019. https://doi.org/10.1117/1.jrs.12.026019CrossRef

Sulaiman AA, Sulaeman Y, Mustikasari N, Nursyamsi D, Syakir AM (2019) Increasing sugar production in Indonesia through land suitability analysis and sugar mill restructuring. Landscape 8:61. https://doi.org/10.3390/land8040061CrossRef

Svinurai W, Hassen A, Tesfamariam E, Ramoelo A (2018) Performance of ratio-based, soil-adjusted and atmospherically corrected multispectral vegetation indices in predicting herbaceous aboveground biomass in a Colophospermum mopane tree-shrub savanna. Grass Forage Sci 73:727–739. https://doi.org/10.1111/gfs.12367CrossRef

Tashayo B, Honarbakhsh A, Akbari M, Eftekhari M (2020) Land suitability assessment for maize farming using a GIS-AHP method for a semi-arid region, Iran. J Saudi Soc Agric Sci 19:332–338. https://doi.org/10.1016/j.jssas.2020.03.003CrossRef

Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150. https://doi.org/10.1016/0034-4257(79)90013-0CrossRef

Venancio LP, Mantovani EC, Amaral CHD, Neale CMU, Gonçalves IZ, Filgueiras R, Campos I (2019) Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agric Water Manag 225:105779. https://doi.org/10.1016/j.agwat.2019.105779CrossRef

Yalew SG, Van Griensven A, Mul ML, Van Der Zaag P (2016) Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Model Earth Syst Environ 2:1–14. https://doi.org/10.1007/s40808-016-0167-xCrossRef

Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. https://doi.org/10.1016/s0019-9958(65)90241-xCrossRef

Zinat MRM, Salam R, Badhan MA, Islam ARMT (2020) Appraising drought hazard during Boro rice growing period in western Bangladesh. Int J Biometeorol 64:1687–1697. https://doi.org/10.1007/s00484-020-01949-2CrossRef

Zolekar RB, Bhagat VS (2015) Multi-criteria land suitability analysis for agriculture in hilly zone: remote sensing and GIS approach. Comput Electron Agric 118:300–321. https://doi.org/10.1016/j.compag.2015.09.016CrossRef

Titel: Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices
verfasst von: Rubaiya Binte Mustafiz
Ryozo Noguchi
Tofael Ahamed
Verlag: Springer Nature Singapore
Buch: Remote Sensing Application
Print ISBN: 978-981-19-0212-3

Electronic ISBN: 978-981-19-0213-0

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-981-19-0213-0_3

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