348
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Using Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study

, &
Pages 9544-9559 | Received 20 Jun 2021, Accepted 19 Dec 2021, Published online: 29 Dec 2021

References

  • Abebe T, Sterck FJ, Wiersum KF, Bongers F. 2013. Diversity, composition and density of trees and shrubs in agroforestry homegardens in Southern Ethiopia. Agroforest Syst. 87(6):1283–1293.
  • Addabbo P, Focareta M, Marcuccio S, Votto C, Ullo S. 2016. Contribution of Sentinel-2 data for applications in vegetation monitoring. Acta Imeko. 5(2):44.
  • Balasubramanian KI. 2017. Identifying the most important spectral and textural feature features to map specific crops with very high resolution images. Enschede: University of Twente.
  • Bhattacharjee R, Choubey A, Das N, Ohri A, Gaur S. 2021. Detecting the Carotenoid Pigmentation due to Haloarchaea Microbes in the Lonar Lake, Maharashtra, India Using Sentinel-2 Images. J Indian Soc Remote Sens. 49(2):305–316.
  • Biswas S, Huang Q, Anand A, Mon MS, Arnold F-E, Leimgruber P. 2020. A multi sensor approach to forest type mapping for advancing monitoring of sustainable development goals (SDG) in Myanmar. Remote Sens. 12(19):3220.
  • Bourgine B, Baghdadi N. 2005. Assessment of C-band SRTM DEM in a dense equatorial forest zone. CR Geosci. 337(14):1225–1234.
  • Breiman L. 2001. Random forests. Machine Learn. 45(1):5–32.
  • Carrasco L, O’Neil AW, Morton RD, Rowland CS. 2019. Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with google earth engine. Remote Sens. 11(3):288.
  • Cheng K, Wang J. 2019. Forest type classification based on integrated spectral-spatial-temporal features and random forest algorithm—A case study in the Qinling Mountains. Forests. 10(7):559.
  • Das T, Das AK. 2014. Mapping and identification of homegardens as a component of the trees outside forests using remote sensing and geographic information system. J Indian Soc Remote Sens. 42(1):233–242.
  • Defries RS, Hansen MC. 2010. ISLSCP II University of Maryland Global Land Cover Classifications, 1992–1993. ORNL Distributed Active Archive Center.
  • Deng XP, Guo SX, Sun LY, Chen JS. 2020. Identification of short-rotation eucalyptus plantation at large scale using multi-satellite imageries and cloud computing platform. Remote Sens. 12(13):2153.
  • Dong JW, Xiao XM, Menarguez MA, Zhang GL, Qin YW, Thau D, Biradar C, Moore B. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine [Article]. Remote Sens Environ. 185:142–154.
  • Earth Resources Observation And Science (EROS) Center. 2017. “Global Land Cover Characterization (GLCC).” U.S. Geological Survey.
  • Erasmi S, Kappas M, Twele A, Ardiansyah M, et al. 2007. From global to regional scale: remote sensing-based concepts and methods for mapping land-cover and land-cover change in tropical regions. In: Tscharntke T, Leuschner C, Zeller M., editors. Stability of tropical rainforest margins: linking ecological, economic and social constraints of land use and conservation. Berlin, Heidelberg: Springer, p. 435–460.
  • Feng Q, Liu J, Gong J. 2015. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens. 7(1):1074–1094.
  • Forkuor G, Dimobe K, Serme I, Tondoh JE. 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. GISci Remote Sens. 55(3):331–354.
  • Friedl M, Sulla-Menashe D. 2019. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC.
  • Gong P, Yu L, Li C, Wang J, Liang L, Li X, Ji L, Bai Y, Cheng Y, Zhu Z. 2016. A new research paradigm for global land cover mapping. Ann Gis. 22(2):87–102.
  • Haralick RM, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern. 3(6):610–621.
  • Immitzer M, Vuolo F, Atzberger C. 2016. First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens. 8(3):166.
  • Kehlenbeck K, Maass BL. 2004. Crop diversity and classification of homegardens in Central Sulawesi, Indonesia. Agroforest Syst. 63(1):53–62.
  • Kumar BM, Nair PKR. 2006. Introduction. In: Kumar BM, Nair PKR, editors. Tropical homegardens: a time-tested example of sustainable agroforestry. Dordrecht: Springer Netherlands; p. 1–10.
  • Kuplich TM, Curran PJ, Atkinson PM. 2005. Relating SAR image texture to the biomass of regenerating tropical forests. Int J Remote Sens. 26(21):4829–4854.
  • Land Use Policy Planning Department. 2020. Data and information of home gardens in Ratnapura District. Ministry of Lands and Land Development.
  • Lindström S, Mattsson E, Nissanka SP. 2012. Forest cover change in Sri Lanka: the role of small scale farmers. Appl Geogr. 34:680–692.
  • Liu Y, Gong W, Hu X, Gong J. 2018. Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM Data. Remote Sensing. 10(6):946.
  • Marcel B, Bruno S, Luc B, Bert DR, Myroslava L, Nandin-Erdene T, Martin H, Steffen F. 2020. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe. Zenodo.
  • Martin M, Geiger K, Singhakumara BMP, Ashton MS. 2019. Quantitatively characterizing the floristics and structure of a traditional homegarden in a village landscape, Sri Lanka. Agroforest Syst. 93(4):1439–1454.
  • Mattsson E, Ostwald M, Nissanka SP, Marambe B. 2013. Homegardens as a multi-functional land-use strategy in Sri Lanka with focus on carbon sequestration. Ambio. 42(7):892–902.
  • Mertes CM, Schneider A, Sulla-Menashe D, Tatem AJ, Tan B. 2015. Detecting change in urban areas at continental scales with MODIS data. Remote Sens Environ. 158:331–347.
  • Mngadi M, Odindi J, Peerbhay K, Mutanga O. 2021. Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping. Geocarto Int. 36(1):1–12.
  • Nordin SA, Abd Latif Z, Omar H. 2019. Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data. Geocarto Int. 34(11):1218–1236.
  • Ouma YO, Tetuko J, Tateishi R. 2008. Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery. Int J Remote Sens. 29(12):3417–3456.
  • Perera K, Herath S, Apan A, Tateishi R. 2012. Application of modis data to assess the latest forest cover changes of Sri Lanka. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. I-7:165–170.
  • Perera AH, Rajapakse RMN. 1991. A baseline study of Kandyan forest gardens of Sri Lanka: structure, composition and utilization. For Ecol Manage. 45(1–4):269–280.
  • Perera K, Tsuchiya K. 2009. Experiment for mapping land cover and it’s change in southeastern Sri Lanka utilizing 250m resolution MODIS imageries. Adv Space Res. 43(9):1349–1355.
  • Premakantha KT, Puspakumara DKNG, Dayawansa N. 2009. Identification of tree resources outside forest in up country of Sri Lanka using medium resolution satellite imagery. Trop Agric Res. 20:354–365.
  • Puetz A, Olsen R. 2006. Haralick texture features expanded into the spectral domain. Vol. 6233. SPIE. (Defense and Security Symposium).
  • Pushpakumara D, Marambe B, Gllp S, Weerahewa J, Bvr P. 2012. A review research on homegardens in Sri Lanka: the status, importance and future perspective. Trop Agric. 160:55–125.
  • Rathnayake CW, Jones S, Soto-Berelov M. 2020. Mapping land cover change over a 25-year period (1993–2018) in Sri Lanka using Landsat Time-Series. Land. 9(1):27.
  • Sangakkara UR, Frossard E. 2016. Characteristics of South Asian rural households and associated home gardens – A case study from Sri Lanka. Trop Ecol. 57(4):765–777.
  • 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(9):1483.
  • Sothe C, Almeida CMd, Liesenberg V, Schimalski MB. 2017. Evaluating Sentinel-2 and Landsat-8 data to map sucessional forest stages in a subtropical forest in Southern Brazil. Remote Sens. 9(8):838.
  • Spracklen BD, Spracklen DV. 2019. “Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians.” Forests 10(2):127.
  • Stibig HJ, Belward AS, Roy PS, Rosalina-Wasrin U, Agrawal S, Joshi PK, Beuchle R, Fritz S, Mubareka S, Giri C. 2007. A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data. J Biogeogr. 34(4):625–637.
  • Taufik A, Ahmad SSS, Ahmad A. 2016. Classification of Landsat 8 Satellite Data Using NDVI Tresholds. J Telecommun Electron Comput Eng. 8:37–40.
  • USGS. 2018. NDVI, the Foundation for Remote Sensing Phenology. [accessed 2021 November 13]. https://www.usgs.gov/core-science-systems/eros/phenology/science/ndvi-foundation-remote-sensing-phenology?qt-science_center_objects=0#qt-science_center_objects.
  • Wang T, Zhang H, Lin H, Fang C. 2015. Textural–spectral feature-based species classification of mangroves in Mai Po nature reserve from worldview-3 imagery. Remote Sens. 8(1):24.
  • Wessel M, Brandmeier M, Tiede D. 2018. Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sens. 10(9):1419.
  • Zhang MN, Gong P, Qi SH, Liu C, Xiong TW. 2019. Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine. Int J Remote Sens. 40(24):9541–9555.
  • Zurqani HA, Post CJ, Mikhailova EA, Schlautman MA, Sharp JL. 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. Int J Appl Earth Obs Geoinf. 69:175–185.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.