Comparative Analysis of Fractional Vegetation Cover Estimation Based on Multi-sensor Data in a Semi-arid Sandy Area
Tóm tắt
The estimation of fractional vegetation cover (FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A (S2) multispectral instrument (MSI) and Landsat 8 (L8) operational land imager (OLI) data regarding the retrieval of FVC in a semi-arid sandy area (Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle (UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index (NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination (R2) of S2 increased by 26.0%, and the root mean square error (RMSE) and the sum of absolute error (SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index (RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors (especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters (FVC).
Tài liệu tham khảo
Barati S, Rayegani B, Saati M et al., 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Science, 14(1): 49–56. doi: 10.1016/j.ejrs.2011.06.001
Bi Kaiyi, Niu Zheng, Huang Ni et al., 2017. Identifying vegetation with decision tree model based on object-oriented method using multi-temporal sentinel-2A images. Geography and Geo-Information Science, 33(5): 16–20. (in Chinese)
Buyantuyev A, Wu J, Gries C, 2007. Estimating vegetation cover in an urban environment based on Landsat ETM+ imagery: a case study in Phoenix, USA. International Journal of Remote Sensing, 28(2): 269–291. doi: 10.1080/01431160600658149
Chen Y, Gillieson D, 2009. Evaluation of Landsat TM vegetation indices for estimating vegetation cover on semi-arid rangelands: a case study from Australia. Canadian Journal of Remote Sensing, 35(5): 435–446. doi: 10.5589/m09-037
Chen Z, CHEN W E, Leblanc S G et al., 2010. Digital photograph analysis for measuring percent plant cover in the Arctic. Arctic, 63(3): 315–326. doi: 10.14430/arctic1495
Cheng Zhigang, Yang Xinyue, Dong Siyan et al., 2016. Vegetation coverage changes in chengdu based on DMSP/OLS and SPOT-VEG NDVI. Advances in Meteorological Science & Technology, 6(1): 14–20. (in Chinese)
Curran P J, Williamson H D, 1986. Sample size for ground and remotely sensed data. Remote Sensing of Environment, 20 (1): 31–41. doi: 10.1016/0034-4257(86)90012-X
Fernández-Manso A, Fernández-Manso O, Quintano C, 2016. Sentinel-2A Red-edge spectral indices suitability for discriminating burn severity. International Journal of Applied Earth Observation and Geoinformation, 50: 170–175. doi: 10.1016/j.jag.2016.03.005
Frampton W J, Dash J, Watmough G et al., 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82: 83–92. doi: 10.1016/j.isprsjprs.2013.04.007
Gago J, Douthe C, Coopman R et al., 2015. UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153: 9–19. doi: 10.1016/j.agwat.2015.01.020
Gao Lei, Lu gang, 2016. Estimating vegetation coverage of Nanjing Jiangbei district based on the GF-1 data. Journal of Anhui Agricultural Sciences, 44(10): 246–248. (in Chinese)
Godínez-Alvarez H, Herrick J, Mattocks M et al., 2009. Comparison of three vegetation monitoring methods: their relative utility for ecological assessment and monitoring. Ecological indicators, 9(5): 1001–1008. doi: 10.1016/j.ecolind.2008.11.011
Graetz R, Pech R P, Davis A, 1988. The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data. International Journal of Remote Sensing, 9(7): 1201–1222. doi: 10.1080/01431168808954929
Gutman G, Ignatov A, 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19(8): 1533–1543. doi: 10.1080/014311698215333
Hamedianfar A, Shafri H Z M, Mansor S et al., 2014. Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data. International Journal of Remote Sensing, 35(5): 1876–1899. doi: 10.1080/01431161.2013.879350
Huete A R, 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295–309. doi: 10.1016/0034-4257(88)90106-X
Jakubauskas M, Kindscher K, Fraser A et al., 2000. Close-rangeremote sensing of aquatic macrophyte vegetation cover. International Journal of Remote Sensing, 21(18): 3533–3538. doi: 10.1080/014311600750037543
Jesús D, Jochem V, Luis A et al., 2011. Evaluation of Sentinel-2 Red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7): 7063–7081. doi: 10.3390/s110707063
Jia K, Liang S, Gu X et al., 2016. Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data. Remote Sensing of Environment, 177: 184–191. doi: 10.1016/j.rse.2016.02.019
Jiang X, Wang D, Tang L et al., 2008. Analysing the vegetation cover variation of China from AVHRR-NDVI data. International Journal of Remote Sensing, 29(17–18): 5301–5311. doi: 10.1080/01431160802036466
Jiapaer G, Chen X, Bao A, 2011. A comparison of methods for estimating fractional vegetation cover in arid regions. Agricultural and Forest Meteorology, 151(12): 1698–1710. doi: 10.1016/j.agrformet.2011.07.004
Ju C, Cai T, Yang X, 2008. Topography-based modeling to estimate percent vegetation cover in semi-arid Mu Us sandy land, China. Computers and electronics in agriculture, 64(2): 133–139. doi: 10.1016/j.compag.2008.04.008
Kaufman Y J, Tanre D, 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2): 261–270. doi: 10.1109/36.134076
Kobayashi T, Liao R T, and Li S Q, 1995. Ecophysiological behavior of Artemisia ordosica on the process of sand dune fixation. Ecological Research, 10(3): 339–349. doi: 10.1007/BF02347860
Lehner A, Steinnocher K, 2017. Mapping population distribution in urban areas: using sentinel-2A in comparison with Landsat 8. Journal for Geographic Information Science, 1: 93–105. doi: 10.1553/giscience2017_01_s93
Leprieur C, Kerr Y, Mastorchio S et al., 2000. Monitoring vegetation cover across semi-arid regions: comparison of remote observations from various scales. International Journal of Remote Sensing, 21(2): 281–300. doi: 10.1080/014311600210830
Li Xiaosong, Li Zengyuan, Gao Zhihai et al., 2010. Estimation of sparse vegetation cover in arid regions based on vegetation indices derived from hyperion data. Journal of Beijing Forestry University, 32(3): 95–100. (in Chinese)
Nemani R R, Running S W, Pielke R A et al., 1996. Global vegetation cover changes from coarse resolution satellite data. Journal of Geophysical Research: Atmospheres, 101: 7157–7162. doi: 10.1029/95JD02138
Novelli A, Aguilar M A, Nemmaoui A et al., 2016. Performance evaluation of object based greenhouse detection from Sentinel- 2 MSI and Landsat 8 OLI data: a case study from Almería (Spain). International Journal of Applied Earth Observation and Geoinformation, 52: 403–411. doi: 10.1016/j.jag.2016.07.011
Sivakumar M, 2007. Interactions between climate and desertification. Agricultural and Forest Meteorology, 142(2): 143–155. doi: 10.1016/j.agrformet.2006.03.025
Souza A A, Galvão L S, Santos J R, 2010. Relationships between hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment. Remote Sensing Letters, 1(1): 55–64. doi: 10.1080/01431160903329364
Tømmervik H, Høgda K A, Solheim I, 2003. Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 85(3): 370–388. doi: 10.1016/S0034-4257(03)00014-2
Van de Voorde T, Vlaeminck J, Canters F, 2008. Comparing different approaches for mapping urban vegetation cover from Landsat ETM+ data: a case study on Brussels. Sensors, 8(6): 3880–3902. doi: 10.3390/s8063880
Verstraete M M, Schwartz S, 1991. Desertification and global change. Vegetatio, 91(1–2): 3–13. doi: 10.1007/BF00036043
Wang Xinyun, Guo Yige, 2013. Estimation of vegetation coverage using hyperion image. Journal of Yangtze River Scientific Research Institute, 30(7): 106–1007. (in Chinese)
White M A, Asner G P, Nemani R R et al., 2000. Measuring fractional cover and leaf area index in arid ecosystems: digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment, 74(1): 45–57. doi: 10.1016/S0034-4257(00)00119-X
Wu D, Wu H, Zhao X et al., 2014. Evaluation of spatiotemporal variations of global fractional vegetation cover based on GIMMS NDVI data from 1982 to 2011. Remote Sensing, 6(5): 4217–4239. doi: 10.3390/rs6054217
Yu F, Dong M, Krüsi B, 2004. Clonal integration helps Psammochloa villosa survive sand burial in an inland dune. New Phytologist, 162(3): 697–704. doi: 10.1111/j.1469-8137.2004.01073.x
Zarco-Tejada P J, Diaz-Varela R, Angileri V et al., 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55: 89–99. doi: 10.1016/j.eja.2014.01.004
Zeng X, Rao P, DeFries R S et al., 2003. Interannual variability and decadal trend of global fractional vegetation cover from 1982 to 2000. Journal of Applied Meteorology, 42(10): 1525–1530. doi:10.1175/1520-0450(2003)042<1525:IVADTO>2.0.CO;2
Zhang N, Zhao J, Zhang L, 2016. Comparison and evaluation on image fusion methods for GaoFen-1 imagery. Proceedings of the Spie, 157: 101571V. doi: 10.1117/12.2246695
Zhang X, Liao C, Li J et al., 2013. Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 21: 506–512. doi: 10.1016/j.jag.2012.07.003
Zhang Z, Zhang H, Chang Y et al., 2015. Review of radiometric calibration methods of Landsat series optical remote sensors. Journal of Remote Sensing, 19(5): 719. doi: 10.11834/jrs.20154240
Zhou Z, Shangguan Z, Zhao D, 2006. Modeling vegetation coverage and soil erosion in the Loess Plateau Area of China. Ecological Modelling, 198(1): 263–268. doi: 10.1016/j.ecolmodel.2006.04.019
Zhou Z, Yang Y, Chen B, 2017. Estimating Spartina alterniflora fractional vegetation cover and aboveground biomass in a coastal wetland using SPOT6 satellite and UAV data. Aquatic Botany, 144: 38–45. doi: 10.1016/j.aquabot.2017.10.004
Zhu Lei, Xu Junfeng, Huang Jingfeng et al., 2008. Study on hyperspectral estimation model of crop vegetation cover percentage. Spectroscopy and Spectral Analysis, 28(8): 1827–1831. (in Chinese)
Zribi M, Dridi G, Amri R et al., 2016. Analysis of the effects of drought on vegetation cover in a mediterranean region through the use of SPOT-VGT and TERRA-MODIS long time series. Remote Sensing, 8(12): 992. doi: 10.3390/rs8120992