Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers

Numata, Izaya; Roberts, Dar A.; Chadwick, Oliver A.; Schimel, Joshua P.; Galvão, Lênio Soares; Soares, João Vianei

doi:10.1016/j.rse.2007.08.014

Cited by 84 publications

(90 citation statements)

References 38 publications

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“…Furthermore, it is likely that subtle differences in the reflectance of the plant canopies in the early stages of both growths have been concealed by the influence of soil on the reflectance signal (cf. [18]). At later stages of the two growths, biomass increased steadily, which mitigating the contribution of soil reflectance to the spectral reflectance (cf.…”

Section: Testing the Classification Accuracy Of The Fifteen Vis Usingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Although several efforts have been made to classify grasslands using broadband data [15][16][17], it was found that they are not spectrally distinct in the broadband system throughout all times of a growing season [18]. Numata et al [18] have shown that the utilization of hyperspectral data may significantly improve the quality of maps displaying the distribution of different grassland communities. Novel or planned hyperspectral satellite missions (such as Hyperion, EnMAP and Hyperspectral Infrared Imager-HyspIRI) may open new perspectives for mapping grasslands using narrowband VIs and are of special importance for distinguishing grassland types and management characteristics, as well as for monitoring these plant communities [1].…”

Section: Introductionmentioning

confidence: 99%

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Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices

Hollberg

Schellberg

2017

Remote Sensing

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Abstract:Monitoring the reaction of grassland canopies on fertilizer application is of major importance to enable a well-adjusted management supporting a sustainable production of the grass crop. Up to date, grassland managers estimate the nutrient status and growth dynamics of grasslands by costly and time-consuming field surveys, which only provide low temporal and spatial data density. Grassland mapping using remotely-sensed Vegetation Indices (VIs) has the potential to contribute to solving these problems. In this study, we explored the potential of VIs for distinguishing five differently-fertilized grassland communities. Therefore, we collected spectral signatures of these communities in a long-term fertilization experiment (since 1941) in Germany throughout the growing seasons 2012-2014. Fifteen VIs were calculated and their seasonal developments investigated. Welch tests revealed that the accuracy of VIs for distinguishing these grassland communities varies throughout the growing season. Thus, the selection of the most promising single VI for grassland mapping was dependent on the date of the spectra acquisition. A random forests classification using all calculated VIs reduced variations in classification accuracy within the growing season and provided a higher overall precision of classification. Thus, we recommend a careful selection of VIs for grassland mapping or the utilization of temporally-stable methods, i.e., including a set of VIs in the random forests algorithm.

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Section: Testing the Classification Accuracy Of The Fifteen Vis Usingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices

Hollberg

Schellberg

2017

Remote Sensing

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“…For the space-borne data, the 1-nm interval ASD canopy reflectance data were fitted to the wide bands of HJ-1 A/B CCD and Landsat-5 TM by the spectral resampling method using ENVI software (RSI, 2006) according to the spectral responding functions of the sensors (Numata et al, 2008;Yuan et al, 2009). In addition, vegetation indices (VIs) for estimating LAI and LNA were extracted using these resampling data.…”

Section: Data Utilization and Analysis (1) Monitoring Models For Lna mentioning

confidence: 99%

Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter forImproving Regional Wheat Yield Forecasts

Huang

Zhu

et al. 2013

Plant Production Science

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“…This shows the potential of these two indices in the discrimination between all doses. Other authors also observed that the spectral response of forage may be used to discriminate different concentrations of leaf nitrogen (BEERI et al, 2007;XIE et al, 2008;NUMATA et al, 2008;FAVA et al, 2009).…”

Section: Another Trend Observed Inmentioning

confidence: 98%

Using an aerial system of remote sensing to detect different nutritional status in Brachiaria decumbens

Júnior

Pinto²,

Queiroz³

et al. 2013

Eng. Agríc.

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ABSTRACT:The aim of this study was to use digital images acquired by cameras attached to a helium balloon to detect variation of the nutritional status in Brachiaria decumbens. The treatments consisted of five doses of nitrogen (0, 50, 100, 150 e 200kg ha -1 ) with six replications each, evaluated in a completely randomized statistical design. A remote sensing system composed of digital cameras and microcomputers was used for image acquisition, and a helium balloon lifted the cameras to the heights of 15, 20, 25 and 30m. A portable chlorophyll meter and analyses of leaf nitrogen content were used to make comparisons with data obtained by the remote sensing system. Data was acquired in two phases, in different climatic conditions. At the end of each phase, dry matter production was measured. Three vegetation indices were used to evaluate the detection of different nutritional status. The three indices were able to detect the effects of N doses. The indices constructed with the Green spectral band showed to be more efficient. KEYWORDS: digital images, vegetation indices, nitrogen. UTILIZAÇÃO DE UM SISTEMA AÉREO DE SENSORIAMENTO REMOTO PARA DETECTAR DIFERENTES STATUS NUTRICIONAIS EM Brachiaria decumbens RESUMO: O objetivo deste trabalho foi utilizar imagens digitais adquiridas por câmeras acopladas em um balão a gás hélio para detectar variação nos status nutricionais em Brachiaria decumbens.Os tratamentos consitiram em cinco doses de nitrogênio (0; 50; 100; 150 e 200 kg ha -1 ) com 6 repetições cada, avaliados em delineamento inteiramente casualizado. Para a aquisição das imagens, foi utilizado um sistema de sensoriamento remoto composto por câmeras digitais e microcomputadores, utilizando um balão a gás hélio, para levantamento das câmeras nas alturas de 15; 20; 25 e 30 m. Também se utilizou de um medidor de clorofila portátil e análise do teor de nitrogenio foliar para efetuar comparações com os dados obtidos pelo sistema de sensoriamento remoto. Os dados foram adquiridos em duas fases, em condições climáticas distintas. Ao final de cada fase, foi medida a produtividade de matéria seca. Foram utilizados três índices de vegetação para avaliar a detecção dos diferentes status nutricionais. Os três índices foram capazes de detectar os efeitos das doses de N. Os índices formados com a banda verde mostraram-se mais eficientes. PALAVRAS-CHAVE:imagens digitais, índices de vegetação, nitrogênio.

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Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers

Cited by 84 publications

References 38 publications

Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices

Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices

Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter forImproving Regional Wheat Yield Forecasts

Using an aerial system of remote sensing to detect different nutritional status in Brachiaria decumbens

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