Ground and satellite based assessment of rangeland management in sub-tropical Argentina

Kurtz, Ditmar Bernardo; Schellberg, J.; Braun, Matthias

doi:10.1016/j.apgeog.2009.01.006

Cited by 36 publications

(17 citation statements)

References 22 publications

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“…Seaquist et al 2003, Mutanga et al 2004, Boschetti et al 2007, Vescovo and Gianelle 2008, but only rarely on grassland management. In addition, mostly large-scale biomes such as semi-arid or subtropical grasslands have been assessed (Numata et al 2007, Kurtz et al 2010. Another issue is the temporal resolution of grassland observations, since with high spatial resolution data the coverage of a certain study area at regular intervals is often impeded, especially in areas prone to frequent cloud cover such as the Alpine area.…”

Section: Introductionmentioning

confidence: 99%

Estimation of grassland use intensities based on high spatial resolution LAI time series

Asam

Klein

Dech

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The identification and surveillance of agricultural management and the measurement of biophysical canopy parameters in grasslands is relevant for environmental protection as well as for political and economic reasons, as proper grassland management is partly subsidized. An ideal monitoring tool is remote sensing due to its area wide continuous observations. However, due to small-scaled land use patterns in many parts of central Europe, a high spatial resolution is needed. In this study, the feasibility of RapidEye data to derive leaf area index (LAI) time series and to relate them to grassland management practices is assessed. The study area is the catchment of river Ammer in southern Bavaria, where agricultural areas are mainly grasslands. While extensively managed grasslands are maintained with one to two harvests per year and no or little fertilization, intensive cultivation practices compass three to five harvests per year and turnover pasturing. Based on a RapidEye time series from 2011 with spatial resolution of 6.5 meters, LAI is derived using the inverted radiation transfer model PROSAIL. The LAI in this area ranges from 1.5 to 7.5 over the vegetation period and is estimated with an RMSE between 0.7 and 1.1. The derived LAI maps cover 85 % of the study area's grasslands at least seven times. Using statistical metrics of the LAI time series, different grassland management types can be identified: very intensively managed meadows, intensively managed meadows, intensively managed pastures, and extensively managed meadows and moor. However, a precise identification of the mowing dates highly depends on the coincidence with satellite data acquisitions. Further analysis should focus therefor on the selection of the temporal resolution of the time series as well as on the performance of further vegetation parameters and indices compared to LAI.

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Section: Introductionmentioning

confidence: 99%

Estimation of grassland use intensities based on high spatial resolution LAI time series

Asam

Klein

Dech

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…B7-75,85.Wetness). Ao compararmos as estimativas feitas com essa nova parametrização e os dados de disponibilidade de biomassa, medidos em campo, verificou-se um coeficiente de correlação linear múltiplo de 0,47 e erro-médio de 0,16 kg ha valores são considerados baixos em comparação aos trabalhos de Paruelo et al (2000) e Kurtz et al (2010). Os baixos valores de correlação encontrados justificam-se pelo delineamento experimental empregado no presente trabalho (amostragem multitemporal) que, diferentemente dos autores citados, utilizou amostras coletadas em datas distintas para estimar a disponibilidade de biomassa aérea de uma área experimental grande e com heterogeneidade na composição florística.…”

Section: Resultsunclassified

Contribuições do solo e dossel em modelo de estimativa de biomassa aérea no Bioma Pampa

Terra¹,

Fonseca

Saldanha

2010

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o desempenho preditivo do submodelo espectral do modelo JONG, com a inserção de variáveis espectrais que considerassem a densidade de biomassa do dossel e as contribuições dos diferentes solos subjacentes. Índices calculados pela diferença e razão simples -entre as bandas 4 e 3, 4 e 5, 4 e 7, do sensor orbital ETM+/Landsat 7 -foram sugeridos para representar a contribuição espectral dos solos subjacentes e a influência das diferenças estruturais dos dosséis. A parametrização da componente espectral foi implementada por regressão linear múltipla e, em seguida, foi comparada aos dados de biomassa obtidos em campo. As variáveis espectrais que melhor expressaram as variações da disponibilidade inicial de forragem foram a fração solo (modelo linear de mistura espectral) e a razão entre as bandas 4 e 7. A componente espectral do modelo JONG, com a nova parametrização, apresenta sensibilidade para eliminar as influências do solo e dossel na disponibilidade inicial de biomassa e facilita a interpretação dos resultados, em razão da relação entre as variáveis espectrais selecionadas.Termos para indexação: disponibilidade de forragem, Landsat, modelo agrometeorológico-espectral, variáveis espectrais. Soil and canopy contributions in a predictive model of aerial biomass in the Pampa BiomeAbstract -The objective of this work was to evaluate the predictive performance of the JONG model's spectral submodel, with the insertion of variables considering contributions of different underlying soils and canopy densities. Indices calculated by subtraction and simple ratio between 4 and 3, 4 and 5, 4 and 7 bands, of Landsat 7/ETM+ sensor -were suggested in order to represent the spectral contribution of the different underlying soils and the influence of canopy structural differences. The spectral component parameterization was implemented by multiple linear regression and, then, it was compared to the biomass data measured in the field. Spectral variables that better describe the variations of initial biomass availability and soil spectral contributions were the soil fraction (spectral mixture linear model), and ratio between 4 and 7 bands. The spectral component of the JONG model, with the new parameterization, showed sensibility in eliminating the canopy and soil influences in the biomass initial availability and, also, improved the interpretation of results due to the relationship between selected spectral variables.

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“…Yield and NPP are often satellite driven, recorded and modelled (Smit et al, 2008;Prieto-Blanco et al, 2009;Kurtz et al, 2009). Also, permanent recording of spatial crop yield data as done in precision farming (Ritter et al, 2008;Schellberg et al, 2008;Lukas et al, 2009) may produce databases which have the potential to predict the productivity of land by statistical procedures of spatio-temporal auto-regressive forecasting, state-space approaches (Wendroth et al, 2003) or combinations of models and data (Reuter et al, 2005;Schellberg et al, 2008).…”

Section: Direct Recordings Of Biomass and Crop Yield Datamentioning

confidence: 99%

Assessing the productivity function of soils. A review

Mueller

Schindler

Mirschel

et al. 2010

Agron. Sustain. Dev.

191

117

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-The development and survival or disappearance of civilizations has been based on the performance of soils to provide food, fibre, and further essential goods for humans. Amongst soil functions, the capacity to produce plant biomass (productivity function) remains essential. This function is closely associated with the main global issues of the 21st century like food security, demands of energy and water, carbon balance and climate change. A standardised methodology for assessing the productivity function of the global soil resource consistently over different spatial scales will be demanded by a growing international community of land users and stakeholders for achieving high soil productivity in the context of sustainable multifunctional use of soils. We analysed available methods for assessing the soil productivity function. The aim was to find potentials, deficiencies and gaps in knowledge of current approaches towards a global reference framework. Our main findings were (i) that the soil moisture and thermal regime, which are climate-influenced, are the main constraints to the soil productivity potential on a global scale, and (ii) that most taxonomic soil classification systems including the World Reference Basis for Soil Resources provide little information on soil functionality in particular the productivity function. We found (iii) a multitude of approaches developed at the national and local scale in the last century for assessing mainly specific aspects of potential soil and land productivity. Their soil data inputs differ, evaluation ratings are not transferable and thus not applicable in international and global studies. At an international level or global scale, methods like agro-ecological zoning or ecosystem and crop modelling provide assessments of land productivity but contain little soil information. Those methods are not intended for field scale application to detect main soil constraints and thereby to derive soil management and conservation recommendations in situ. We found also, that (iv)

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Ground and satellite based assessment of rangeland management in sub-tropical Argentina

Cited by 36 publications

References 22 publications

Estimation of grassland use intensities based on high spatial resolution LAI time series

Estimation of grassland use intensities based on high spatial resolution LAI time series

Contribuições do solo e dossel em modelo de estimativa de biomassa aérea no Bioma Pampa

Assessing the productivity function of soils. A review

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