Discrimination and characterization of management systems in semi-arid rangelands of South Africa using RapidEye time series

Brüser, Katharina; Feilhauer, Hannes; Linstädter, Anja; Schellberg, J.; Oomen, Roelof; Ruppert, Jan C.; Ewert, Frank

doi:10.1080/01431161.2014.882028

Cited by 11 publications

(7 citation statements)

References 53 publications

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“…However, trait-based indicators showed that, in both biomes, tenure-related management effects on vegetation were small, if piospheres were compared to piospheres and pastures to pastures. For farms in the grassland biome, these results are in general agreement with two accompanying studies from the same growing season, focusing on vegetation responses in piospheres based on a taxon-free sampling of vegetative traits [38] , and on a time series analysis based on high-resolution spectral imagery of pastures [74] .…”

Section: Discussionsupporting

confidence: 88%

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Are There Consistent Grazing Indicators in Drylands? Testing Plant Functional Types of Various Complexity in South Africa’s Grassland and Savanna Biomes

et al. 2014

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Despite our growing knowledge on plants’ functional responses to grazing, there is no consensus if an optimum level of functional aggregation exists for detecting grazing effects in drylands. With a comparative approach we searched for plant functional types (PFTs) with a consistent response to grazing across two areas differing in climatic aridity, situated in South Africa’s grassland and savanna biomes. We aggregated herbaceous species into PFTs, using hierarchical combinations of traits (from single- to three-trait PFTs). Traits relate to life history, growth form and leaf width. We first confirmed that soil and grazing gradients were largely independent from each other, and then searched in each biome for PFTs with a sensitive response to grazing, avoiding confounding with soil conditions. We found no response consistency, but biome-specific optimum aggregation levels. Three-trait PFTs (e.g. broad-leaved perennial grasses) and two-trait PFTs (e.g. perennial grasses) performed best as indicators of grazing effects in the semi-arid grassland and in the arid savanna biome, respectively. Some PFTs increased with grazing pressure in the grassland, but decreased in the savanna. We applied biome-specific grazing indicators to evaluate if differences in grazing management related to land tenure (communal versus freehold) had effects on vegetation. Tenure effects were small, which we mainly attributed to large variability in grazing pressure across farms. We conclude that the striking lack of generalizable PFT responses to grazing is due to a convergence of aridity and grazing effects, and unlikely to be overcome by more refined classification approaches. Hence, PFTs with an opposite response to grazing in the two biomes rather have a unimodal response along a gradient of additive forces of aridity and grazing. The study advocates for hierarchical trait combinations to identify localized indicator sets for grazing effects. Its methodological approach may also be useful for identifying ecological indicators in other ecosystems.

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Section: Discussionsupporting

confidence: 88%

“…Bloemfontein data 1904–2011 (n = 100), Kuruman data 1905–1997 (n = 62). Soil types are given as WRB type [74] ; grassland soils after [50] ; savanna soils after [24] . Vegetation characteristics of the grassland after [25] ; savanna: after [24] .…”

Section: Methodsmentioning

confidence: 99%

Are There Consistent Grazing Indicators in Drylands? Testing Plant Functional Types of Various Complexity in South Africa’s Grassland and Savanna Biomes

et al. 2014

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“…Furthermore, in-field ground truth measurements are hard to compare to remotely sensed data. Relevant processes may display a small-scale heterogeneity (e.g., spatially varying grazing intensities or heterogeneous abiotic site conditions [6]) that a selective sampling of species may not capture. Remotely sensed data exhibiting both high temporal and high spatial resolution show great promise for the exploration of processes taking place rapidly at scales similar to that of field assessments.…”

Section: Introductionmentioning

confidence: 99%

Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

et al. 2015

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Image time series of high temporal and spatial resolution capture land surface dynamics of heterogeneous landscapes. We applied the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm to multi-spectral images covering two semi-arid heterogeneous rangeland study sites located in South Africa. MODIS 250 m resolution and RapidEye 5 m resolution images were fused to produce synthetic RapidEye images, from June 2011 to July 2012. We evaluated the performance of the algorithm by comparing predicted surface reflectance values to real RapidEye images. Our results show that ESTARFM predictions are accurate, with a coefficient of determination for the red band 0.80 < R 2 < 0.92, and for the near-infrared band 0.83 < R 2 < 0.93, a mean relative bias between 6% and 12% for the red band and 4% to 9% in the near-infrared band. OPEN ACCESSRemote Sens. 2015, 7 6511Heterogeneous vegetation at sub-MODIS resolution is captured adequately: A comparison of NDVI time series derived from RapidEye and ESTARFM data shows that the characteristic phenological dynamics of different vegetation types are reproduced well. We conclude that the ESTARFM algorithm allows us to produce synthetic remote sensing images at high spatial combined with high temporal resolution and so provides valuable information on vegetation dynamics in semi-arid, heterogeneous rangeland landscapes.

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“…Main drivers causing variation in space are abiotic factors such as topoedaphic conditions or climate, and biotic factors such as grazing pressure (Guuroh et al, 2018;Oomen et al, 2016). Variation in time is generally caused by the vegetation's phenological development, by variable weather conditions, and by management decisions (Berger et al, 2019;Brüser et al, 2014). Here, rainfall is regarded as the most important driver of forage production (e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards Forage Resource Monitoring in subtropical Savanna Grasslands: going multispectral or hyperspectral?

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Linstädter

Rogaß

et al. 2021

European Journal of Remote Sensing

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Forage supply of savanna grasslands plays a crucial role for local food security and consequently, a reliable monitoring system could help to better manage vital forage resources. To help installing such a monitoring system, we investigated whether in-situ hyperspectral data could be resampled to match the spectral resolution of multi-and hyperspectral satellites; if the type of sensor affected model transfer; and if spatio-temporal patterns of forage characteristics could be related to environmental drivers. We established models for forage quantity (green biomass) and five forage quality proxies (metabolisable energy, acid/neutral detergent fibre, ash, phosphorus). Hyperspectral resolution of the Hyperion satellite mostly resulted in higher accuracies (i.e. higher R 2 , lower RMSE). When applied to satellite data, though, the greater quality of the multispectral Sentinel-2 satellite data leads to more realistic forage maps. By analysing a three-year time series, we found plant phenology and cumulated precipitation to be the most important environmental drivers of forage supply. We conclude that none of the investigated satellites provide optimal conditions for monitoring purposes. Future hyperspectral satellite missions like EnMAP, combining the high information level of Hyperion with the good data quality and resolution of Sentinel-2, will provide the prerequisites for installing a regular monitoring service.

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Discrimination and characterization of management systems in semi-arid rangelands of South Africa using RapidEye time series

Cited by 11 publications

References 53 publications

Are There Consistent Grazing Indicators in Drylands? Testing Plant Functional Types of Various Complexity in South Africa’s Grassland and Savanna Biomes

Are There Consistent Grazing Indicators in Drylands? Testing Plant Functional Types of Various Complexity in South Africa’s Grassland and Savanna Biomes

Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

Towards Forage Resource Monitoring in subtropical Savanna Grasslands: going multispectral or hyperspectral?

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