Spatial resampling of remote sensing data – accuracy vs. redundancy

Piotr Bartmiński, Marcin Siłuch

Abstract


Active surface reflectance in a UV/VIS/NIR range deserve special attention among remote sensing techniques due to the potential of information it carries. Data are diversified in terms of spatial, spectral and temporal resolution, resulting in differences in data comparison and collection of material that may be redundant. The aim of the study was to assess whether the use of high-resolution data in analysis of an intensively used meadow is justified. 116 images from Planet sensor were analysed, registered from 2016 to 2019. NDVI, EVI and GLI were calculated for all of the terms. Resampling of data was carried out, with the use of 30 m grid, prepared on the basis of 3 m Planet pixel. Data with different resolution was compared. Seasonal course of values was similar in all cases, values of chosen deciles were nearly the same, however, differences in minimum and maximum values were noted.  It was concluded that the use of high-resolution data is not advisable in the context of the spatial variability of seasonal vegetation indices in the case of a terrain with homogeneous land cover. Values of structurally simplified indices are less homogeneous than that of indicators consisting of a greater number of modifying factors.


Keywords


active surface reflectance, vegetation index, data resampling

Full Text:

PDF

References


Bannari A., Morin D., Bonn F., Huete A., 1995. A review of vegetation indices. Remote Sensing Reviews, 13: 95-120. DOI: 10.1080/02757259509532298.

Barrero O., Perdomo S.A., 2018. RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture, 19: 809–822.

Caras T., Hedley J., Karnielia A., 2017. Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales. International Journal of Applied Earth Observation and Geoinformation, 63: 68-77.

Fawcett D., Panigada C., Tagliabue G., Boschetti M., Celesti M., Evdokimov A., Biriukova K., Colombo R., Miglietta F., Rascher U., Anderson K., 2020. Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions. Remote Sensing, 12: 514.

Fraga H., Amraoui M., Malheiro A.C., Moutinho-Pereira J., Eiras-Dias J., Silvestre J., Santos J.A., 2014. Examining the relationship between the Enhanced Vegetation Index and grapevine phenology. European Journal of Remote Sensing, 47: 753-771. DOI: 10.5721/EuJRS20144743.

Gao L., Wang X., Johnson B. A., Tian Q., Wang Y., Verrelst J., Mu X., Gu X., 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 159: 364-377. DOI 10.1016/j.isprsjprs.2019.11.018

Hall C. A., Meyer W. W., 1976. Optimal Error Bounds for Cubic Spline Interpolation. Journal of Approximation Theory, 16: 105–122. DOI:10.1016/0021-9045(76)90040-X.

Heiskanen J., Liu J., Valbuena R., Aynekulu E., Packalen P., Pellikka P., 2017. Remote sensing approach for spatial planning of land management interventions in West African savannas. Journal of Arid Environments, 140: 29-41.

Huete A. R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295-309. DOI: 10.1016/0034-4257(88)90106-X.

Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83: 195-213. DOI:10.1016/S0034-4257(02)00096-2.

Hwang T., Song C., Bolstad P. V., Band L. E., 2011. Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain. Remote Sensing of Environment, 115: 2499–2512.

Karkauskaite P., Tagesson T., Fensholt R., 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone. Remote Sensing, 9: 485. DOI:10.3390/rs9050485.

Kim D., Silva R.R., Kim J., Kim Y., Kim H., Chung J.S., 2020. Comparison of Various Kinds of Vegetative Indices for Chlorophyll Contents Using Low-Resolution Camera. Journal of Crop Science and Biotechnology, 23: 73–79. DOI: 10.1007/s12892-019-0347-0.

Li F., Chen W., Zeng Y., Zhao Q., Wu B., 2014. Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China. Remote Sensing, 6: 4705-4722. DOI: 10.3390/rs6064705.

Liu K., Wang S., Li X., Li Y., Zhang B., Zhai R., 2020. The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region. International Journal of Remote Sensing, 41: 1907-1926. DOI: 10.1080/01431161.2019.1677969

Liu W., Zenga Y., Lic S., Huang W., 2020. Spectral unmixing based spatiotemporal downscaling fusion approach. International Journal of Applied Earth Observation and Geoinformation, 88: 102054.

Lyons M. B., Keith D. A., Phinn S. R., Mason T. J., Elith J., 2018. A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sensing of Environment, 208: 145-153.

Mancino G., Ferrara A., Padula A., Nolè A., 2020. Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment. Remote Sensing, 12: 291.

Marín J., Yousfi S., Mauri P.V., Parra L., Lloret J., Masaguer A., 2020. RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions. Sustainability, 12: 2160.

Melesse A. M., Weng, Q., Thenkabail, P. S., Senay, G. B., 2007. Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling. Sensors, 7: 3209–3241. DOI: 10.3390/s7123209.

Moreira A., Bremm C., Fontana D.C., Kuplich T.M., 2017. Seasonal dynamics of vegetation indices as a criterion for grouping grassland. Scientia Agricola, 76: 24-32.

Ovakoglou G., Alexandridis T. K., Clevers J., Gitas I., 2020. Downscaling of MODIS Leaf Area Index Using Landsat Vegetation Index. Geocarto International. DOI: 10.1080/10106049.2020.1750062.

Palanisamy S., Rathika S., Thanakkan R., Ponnusamy J., 2019. Applications of Remote Sensing in Agriculture - A Review. International Journal of Current Microbiology and Applied Sciences, 8: 2270-2283. DOI: 10.20546/ijcmas.2019.801.238.

Planet Team. 2020. Planet Application Program Interface: In Space for Life on Earth. Available online: https://api.planet.com (access: 3.01.2020).

Press W. H., Teukolsky S. A., Vetterling W.T., Flannery B. P., 1992. Numerical recipes in C: the art of scientific computing. Cambridge University Press: New York, pp. 123-128.

Prey L., Schmidhalter U., 2019. Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat. ISPRS Journal of Photogrammetry and Remote Sensing, 149: 176-187.

Purevdorj T.S., Tateishi R., Ishiyama T., Honda Y., 1998. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 19: 3519-3535. DOI: 10.1080/014311698213795

Sharma K.V., Khandelwal S., Kaul N., 2020. Downscaling of Coarse Resolution Land Surface Temperature Through Vegetation Indices Based Regression Models. [In:] Ghosh J., da Silva I. (eds.) Applications of Geomatics in Civil Engineering. Lecture Notes in Civil Engineering, Springer, Singapore.

Shirsath P. B., Sehgal V. K., Aggarwal P. K., 2020. Downscaling Regional Crop Yields to Local Scale Using Remote Sensing. Agriculture, 10: 58. DOI 10.3390/agriculture10030058.

Sousa V., Salami G., Silva M., Silva M., Monteiro J., Alba E., 2019. Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology, and Landscapes. DOI: 10.1080/24749508.2019.160840

Tao J., Dong J., Zhang Y., Yu X., Zhang G., Cong N., Zhu J., Zhang X., 2020. Elevation-dependent effects of growing season length on carbon sequestration in Xizang Plateau grassland. Ecological Indicators, 110: 105880.

Teillet P.M., Staenz K., William D.J., 1997. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 61: 139-149.

Thenkabail P.S., Lyon J. G., Huete A., 2019. Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation — Summary, Insights, and Highlights of Volume IV Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation. In Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation, Thenkabail, P.S., Lyon, J. G., Huete, A. (eds.), CRC Press.

Xue J., Su B., 2017. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 1: 1-17. DOI: 10.1155/2017/1353691.




DOI: http://dx.doi.org/10.17951/pjss.2020.53.2.293-306
Date of publication: 2020-12-26 01:25:49
Date of submission: 2020-05-12 10:37:21


Statistics


Total abstract view - 1063
Downloads (from 2020-06-17) - PDF - 903

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Piotr Bartmiński, Marcin Siłuch

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.