Evaluation of Hyperspectral Data as Predictors for Natural Grassland Biomass
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Abstract
The Pampa Biome represents approximately 63% of the territory in the State of Rio Grande do Sul - Brazil. Due to the continuous incorporation of monocultures of exotic species, agricultural crops and sometimes inadequate practices of livestock production, native fields are rapidly being degraded, fragmented and uncharacterized. Seeking to collaborate in developing new management strategies and appropriate monitoring of natural grassland, with a view to minimizing efforts for field collection, this study aimed to characterize and quantify the relationship between hyperspectral data collected by spectroradiometer as biomass predictors country in two methods pasture management with the aid of field data. The study area are plots of grazing cattle by two methods (treatments) management (day 375 to 750 degrees - DG) which were acquired spectral reflectance curves with spectroradiometer over the range 350-2500 nm wavelength. 10 vegetation indices were used and used along with 11 CO intervals in the regression analyzes. Selected indices and ranges simulated wavelength used in the Sentinel 2 satellite MSI sensor bands, available free of charge since 2015. The results showed strong correlations between the variables and it was found that in the spectral regions of blue, red edge and NDLI and DMCI differed statistically among management methods. The spectral regions of Blue, NIR and SWIR were significantly higher in 750 GD treatment. The most accurate model to biomass estimation involved the EVI and CAI indices with adjusted r² = 0.72 and RMSE = 0.10.
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References
BARRACHINA, M., CRIST
BOLFE,
BRANTLEY, S.T., ZINNERT, J.C., YOUNG, D.R. Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies. Remote Sensing Environmental. 115, 514
CHO, M.A., SKIDMORE, A.K. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. International Journal Remote Sensing. 30, 499
CLEVERS, J.G.P.W., VAN DER HEIJDEN, G.W. A. M., VERZAKOV, S., SCHAEPMAN, M.E. Estimating grassland biomass using SVM band shaving of hyperspectral data. Photogrammetric Engineering and Remote Sensing. 73, 1141
CORDEIRO, J.L.P., HASENACK, H. Cobertura vegetal atual do Rio Grande do Sul. In: Pillar, V. D.; Müller, S. C.; Castilhos, Z. M. S.; Jacques, A. V. A. Campos Sulinos: conservação e uso sustentável da biodiversidade. Ministério do Meio Ambiente, Brasilia - DF, pp. 285
CRUZ, P., DE QUADROS, F.L.F., THEAU, J.P., FRIZZO, A., JOUANY, C., DURU, M., CARVALHO, P.C.F. Leaf traits as functional descriptors of the intensity of continuous grazing in native grasslands in the South of Brazil. Rangeland Ecology & Management. 63, 350
GITELSON, A., MERZLYAKB, M.N. Quantitative experiments estimation of chlorophyll-u using reflectance with autumn chestnut and maple leaves and spectra. Journal Photochemistry Photobiology B: Biology. 22, 247
GONZÁLEZ-DUGO, M.P., MATEOS, L. Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops. Agricultural Water Management. 95, 48
HAYDOCK, K.P., SHAW, N.H. The comparative yield method for estimating dry matter yield of pasture. Aust. J. Agric. Anim. 15, 66
HILL, M.J. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing Environmental. 137, 94
HMIMINA, G., DUFR
HUETE, A.R. ET AL, A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS. Remote Sensing Environmental. 59, 440
KAMPE, T.U., GOOD, W.S. Pathway to future sustainable land imaging: the compact hyperspectral prism spectrometer. SPIE 10402. 2017. https://doi.org/10.1117/12.2270932
LI, F., CHEN, W., ZENG, Y., ZHAO, Q., WU, B., Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in Inner Mongolia, China. Remote Sensing. 6, 4705
MARABEL, M., ALVAREZ-TABOADA, F. Spectroscopic determination of aboveground biomass in grasslands using spectral transformations, support vector machine and partial least squares regression. Sensors (Switzerland). 13, 10027
MASELLI, F., ARGENTI, G., CHIESI, M., ANGELI, L., PAPALE, D. Simulation of grassland productivity by the combination of ground and satellite data. Agric. Ecosystems Environmental. 165, 163
MERZLYAK, M.N., GITELSON, A.A., CHIVKUNOVA, O.B., RAKITIN, V.Y. Non-destructive optical detection of leaf senescence and fruit ripening. Physiologia Plantarum. 106, 135. 1999.
MOREIRA, A., FONTANA, D.C., KUPLICH, T.M. Aplicação de ondaleta em série temporal NDVI/MODIS para avaliação da fenologia de fisionomias campestres no Rio Grande do Sul. Anais do XVII Simpósio Bras. Sensoriamento Remoto (SBSR), João Pessoa-PB, 7, 6381
NAGLER, P.L., INOUE, Y., GLENN, E.P., RUSS, A.L., DAUGHTRY, C.S.T. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sensing Environmental. 87, 310
NUMATA, I., ROBERTS, D.A., CHADWICK, O.A., SCHIMEL, J., SAMPAIO, F.R., LEONIDAS, F.C., SOARES, J. V., Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sensing Environmental. 109, 314
OVERBECK, G.E., M
PALUBINSKAS, G., BACHMANN, M., CARMONA, E., GERASCH, B., KRAWCZYK, H., MAKARAU, A., SCHNEIDER, M., SCHWIND, P. Image products from a new german hyperspectral mission enmap. 2017.
REN, S., CHEN, X., AN, S. Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland. International Journal Biometeorol. 61, 601
ROMERO, A., AGUADO, I., YEBRA, M. Estimation of dry matter content in leaves using normalized indexes and prospect model inversion. International Journal Remote Sensing. 33, 396
SERRANO, L., PENUELAS, J., USTIN, S.L. Remote sensing of nitrogen and lignin in Mediterranean vegetationrfrom AVIRIS data:rDecomposing biochemical from structural signals. Remote Sensing Environmental. 81, 355
SIMS, D.A., GAMON, J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environmental. 81, 337
STRECK, E. V. Solos do Rio Grande do Sul, 2a. ed. Revista e ampl. Porto Alegre: Emater-RS, Porto Alegre. 2008.
TRENTIN, A.B., KUPLICH, T.M., MOREIRA, A., GARAGORY, F., QUADROS, F. Relação da biomassa aérea com a resposta espectral de parcelas de campo com diferentes tipos de manejo. Anais do XV Simpósio Bras. Sensoriamento Remoto (SBSR), Curitiba, PR, Brasil, 30 abril a 05 maio 2011, INPE 1555
TUCKER, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environmenyal. 8, 127
ULLAH, S., SI, Y., SCHLERF, M., SKIDMORE, A.K., SHAFIQUE, M., IQBAL, I.A. Estimation of grassland biomass and nitrogen using MERIS data. International Journal of Applied Earth Observation. 19, 196
WANG, L., HUNT, E.R., QU, J.J., HAO, X., DAUGHTRY, C.S.T. Towards estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sens. Environ. 115, 836
WANG, L., QU, J.J., HAO, X., HUNT, E.R, Estimating dry matter content from spectral reflectance for green leaves of different species. International Journal Remote Sensing. 32, 7097
XU, D., GUO, X., LI, Z., YANG, X., YIN, H, Measuring the dead component of mixed grassland with Landsat imagery. Remote Sensing Environmental. 142, 33
YANG, X., XU, B., JIN, Y., QIN, Z., MA, H., LI, J., ZHAO, F., CHEN, S., ZHU, X. Remote sensing monitoring of grassland vegetation growth in the Beijing-Tianjin sandstorm source project area from 2000 to 2010. Ecological Indicators. 51, 244
ZHOU, Y., ZHANG, L., XIAO, J., CHEN, S., KATO, T., ZHOU, G. A comparison of satellite-derived vegetation indices for approximating gross primary productivity of grasslands. Rangeland Ecology & Management. 67, 9