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Using Sentinel-2A to identify the change in dry marginal agricultural land occupation

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Języki publikacji
EN
Abstrakty
EN
Dry marginal agricultural land (DryMAL) potentially use as an alternative resource for crop production. DryMAL defined as land having low natural fertility due to its intrinsic properties and forming environmental factors. This study uses Sentinel-2A imagery to map the spatial extent, compare the result of the classification, and identify the change in DryMAL occupation. The area of study (461.9 km2) is part of Situbondo Regency and is located at the eastern part of East Java, Indonesia. Sentinel-2A image captured in dry-season of 2018 use for this study. Then, supervised image classification using a maximum likelihood algorithm use for image treatment and processing. Furthermore, 450 ground control points for training areas collected during the field surveys. Five bands use in the classification process. The maps produced from the classification process were then compared to the land-use map from the year 2000. The change in DryMAL occupation from 2000 to 2018 was calculated by comparing the classified and land-use map. Supervised classification yielded an overall accuracy of 95.8% and a kappa accuracy of 93.2%. The classification produced six (6) classes of land use: (1) forest, (2) pavement or built-up area, (3) irrigated paddy field, (4) non-irrigated rural area, (5) dry marginal land and (6) water body. Globally, during the last two decades, regional development led by the Regency occupied more DryMAL area for developing plantation. The effort reduces the amount of non-irrigated and converting to the plantation, pavement areas, and irrigated paddy-field.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
89--95
Opis fizyczny
Bibliogr. 24 poz., fot., rys., tab.
Twórcy
  • University of Jember, Faculty of Agricultural Technology, Jl kalimantan No. 37 Kampus Tegalboto, 68121, Jember, Jawa Timur, Indonesia
  • University of Jember, Faculty of Agricultural Technology, Jl kalimantan No. 37 Kampus Tegalboto, 68121, Jember, Jawa Timur, Indonesia
  • University of Jember, Faculty of Agricultural Technology, Jl kalimantan No. 37 Kampus Tegalboto, 68121, Jember, Jawa Timur, Indonesia
Bibliografia
  • ABDI A. M. 2019. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing. Vol. 57. Iss. 1 p. 1–20. DOI 10.1080/15481603. 2019.1650447.
  • BIEHL L. 2018. Intro to remote sensing using MultiSpec [online]. In: NEXUS Remote Sensing Workshop. 6.08.2018. [Access 15.12.2019]. Available at: https://engineering.purdue.edu/~biehl/MultiSpec/tutorials/20180806_Nexus_Remote_Sensing_Exercise_English.pdf
  • BIG 2019. Ina-Geoportal. Geo-spatial untuk Negri [Indonesia Geospatial Portal] [online]. [Access 15.12.2019]. Available at: http://tanahair.indonesia.go.id/portal-web
  • CONGEDO L. 2017. Semi-automatic classification plugin semi-automatic classification plugin documentation. Technical Report pp. 198. DOI 10.13140/RG.2.2.29474.02242/1.
  • DONATTI C.I., HARVEY C.A.,MARTINEZ-RODRIGUEZ M., VIGNOLA R., RODRIGUEZ C.M. 2019. The vulnerability of smallholder farmers to climate change in Central America and Mexico: Current knowledge and research gaps. Climate and Development. Vol. 11(3) p. 264–286. DOI 10.1080/17565529. 2018.1442796.
  • ELBERSEN B., VAN VERZANDVOORT M., BOOGAARD S., MUCHER S., CICARELLI T., ELBERSEN W., MANTEL S., BAI Z., MCALLUM I., IQBAL Y. 2018. Deliverable 2.1 Definition and classification of marginal lands suitable for industrial crops in Europe. EU Horizon 2020; MAGIC; GANo.: 727698. Wageningen, The Netherlands. University and Research pp. 60.
  • ESA 2013. Sentinel-2 user handbook. Iss. 1. Rev. 1. European Space Agency pp. 64.
  • FORKUOR G., FORKUOR G., DIMOBE K., SERME I., TONDOH J.E. 2018. Landsat-8 vs. Sentinel-2: Examining the added value of Sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience & Remote Sensing. Vol. 55. Iss. 3p. 331–354. DOI 10.1080/15481603.2017.1370169.
  • GERWIN W., REPMANN F., GALATSIDAS S., VLACHAKI D., GOUNARIS N., BAUMGARTEN W., VOLKMANN C., KERAMITZIS D., KIOURTSIS F., FREESE D. 2018. Assessment and quantification of marginal lands for biomass production in Europe using soil-quality indicators. Soil. 4(4) p. 267–290. DOI 10.5194/soil-4-267-2018.
  • GOGA T., FERANEC J., BUCHA T., RUSNÁK M., SAČKOV I., BARKA I., KOPECKÁ M., PAPČO J., OŤAHEĽ J., SZATMÁRI D., PAZÚR R., SEDLIAK M., PAJTÍK J., VLADOVIČ J. 2019. A review of the application of remote sensing data for abandoned agricultural land identification with focus on Central and Eastern Europe. Remote Sensing. DOI 10.3390/rs11232759.
  • INDARTO I. 2013. Variabilitas spasial hujan harian di Jawa Timur [Spatial variability of number rainy-day in East Java] [online]. Jurnal Teknik Sipil. Vol. 20. No. 2 p. 107–120. [Access 15.10.2019]. Available at: http://journals.itb.ac.id/index.php/jts
  • INDARTO I., MANDALA M. 2019. Final Report (Lap. Akhir) – Internal Research Grant (Hibah Keris) – Inventory and mapping of Marginal agricultural land in Situbondo Regency (Inventarisasi dan Pemetaan Lahan Sub-Optimal di Wilayah Situbondo). LP2M-Univertity of Jember. Jember.
  • LONGATO D., GAGLIOC M., BOSCHETTI M., GISSI E. 2019. Bioenergy and ecosystem services trade-offs and synergies in mar-ginal agricultural lands: A remote-sensing-based assessment method. Journal of Cleaner Production. Vol. 237, 117672. DOI 10.1016/j.jclepro.2019.117672.
  • MANSARAY L.R.,WANG F., HUANG J., YANG L., KANU A.S. 2019. Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets. Geocarto International. Vol. 35. Iss. 10 p. 1088–1108. DOI 10.1080/10106049.2019.1568586.
  • MILBRANDT A., OVEREND R.P. 2008. Assessment of biomass resources from marginal lands in APEC Economies, August 2009 [online]. [Access. 5.04.2020]. Available at: https://www.nrel.gov
  • MULYANI A., SARWANI M. 2013. Karakteristik dan Potensi Lahan Sub-Optimal untuk Pengembangan Pertanian di Indonesia. [The Characteristic and Potential of Sub Optimal Land for Agricultural Development in Indonesia] [online]. Jurnal Sumberdaya Lahan Vol. 7(1) p. 47–55. [Access 26.11.2019]. Available at: http://ejurnal.litbang.pertanian.go.id/index.php/jsl/article/view/6429 DOI 10.21082/jsdl.v7n1.2013.%25p.
  • NYAMBO D.G., LUHANGA E.T., YONAH Z.Q. 2019. A review of characterization approaches for smallholder farmers: Towards predictive farm typologies. The Scientific World Journal. Vol. 2019. Art. ID 6121467 pp. 9. DOI 10.1155/2019/ 6121467.
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  • RICHARDS J.A. 2013. Remote sensing digital image analysis: An introduction, Remote Sensing Digital Image Analysis: An Introduction. Berlin–Heidelberg. Springer-Verl. DOI 10.1007/978-3-642-30062-2.
  • RUJOIU-MARE M.R.,MARINA R., OLARIU B.,MIHAI B., NISTOR C., SĂVULESCU I. 2017. Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery. European Journal of Remote Sensing. Vol. 50(1) p. 496–508. DOI 10.1080/22797254. 2017.1365570.
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  • VON COSSEL M., LEWANDOWSKI I., ELBERSEN B., STARITSKY I., VAN EUPEN M., IQBAL Y., …, ALEXOPOULOU E. 2019. Marginal agricultural land low-input systems for biomass production. Energies. Vol. 12(16), 3123. DOI 10.3390/ en12163123.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-76c29dce-6faa-4734-bcc2-acabd2cc5897
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