Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Gaitán, Juan; Bran, Donaldo; Oliva, Gabriel; Ciari, Georgina; Nakamatsu, Viviana; Salomone, Jorge; Ferrante, Daniela; Buono, Gustavo; Massara, Virginia; Humano, Gervasio; Celdrán, Diego; Opazo, Walter; Maestre, Fernando T.

doi:10.1016/j.ecolind.2013.05.007

Cited by 87 publications

(74 citation statements)

References 56 publications

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“…A better result was achieved for the total vegetative ground cover model, where 80.2% of the variance could be explained (Table 2f), a result similar to other studies in arid, respectively mountainous, regions, e.g., [5,47].…”

Section: Model Evaluationsupporting

confidence: 74%

“…This is in contrast to other studies [47] and demonstrates the usefulness of this variable for remote sensing vegetation studies in arid lands. Among the single-band reflectances, only Band 5 reflectance (IR) significantly contributed to the model accuracies.…”

Section: Discussioncontrasting

confidence: 53%

“…Compared with other studies about species composition, our results give a considerably accurate picture about the distribution of single species. The other studies on arid and similar mountain ecosystems do not map single species, but species composition or vegetation classes [4,5,7,8], plant richness [47]; however, using similar indices. Studies on species separation mainly use hyperspectral data often under experimental conditions [9].…”

Section: Species Distribution Modelmentioning

confidence: 99%

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Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

Vanselow

Samimi

2014

Remote Sensing

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Abstract:In many arid mountains, dwarf shrubs represent the most important fodder and firewood resources; therefore, they are intensely used. For the Eastern Pamirs (Tajikistan), they are assumed to be overused. However, empirical evidence on this issue is lacking. We aim to provide a method capable of mapping vegetation in this mountain desert. We used random forest models based on remote sensing data (RapidEye, ASTER GDEM) and 359 plots to predictively map total vegetative cover and the distribution of the most important firewood plants, K. ceratoides and A. leucotricha. These species were mapped as present in 33.8% of the study area (accuracy 90.6%). The total cover of the dwarf shrub communities ranged from 0.5% to 51% (per pixel). Areas with very low cover were limited to the vicinity of roads and settlements. The model could explain 80.2% of the total variance. The most important predictor across the models was MSAVI2 (a spectral vegetation index particularly invented for low-cover areas). We conclude that the combination of statistical models and remote sensing data worked well to map vegetation in an arid mountainous environment. With this approach, we were able to provide tangible data on dwarf shrub resources in the Eastern Pamirs and to relativize previous reports about their extensive depletion.

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Section: Model Evaluationsupporting

confidence: 74%

Section: Discussioncontrasting

confidence: 53%

Section: Species Distribution Modelmentioning

confidence: 99%

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Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

Vanselow

Samimi

2014

Remote Sensing

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“…LFA infiltration and nutrient cycling indexes have been observed to relate significantly to perennial species richness in Mediterranean drylands (Maestre and Cortina, 2004). In addition, some of the LFA indices, especially infiltration and nutrient cycling, show good correlations with remote sensing indices such as the NDVI (Gaitán et al, 2013). The combination of these two approaches at such different scales may provide useful information on ecosystem functioning and might be a good tool for dryland management by selecting and prioritizing areas to restore.…”

Section: Functional Approaches In the Monitoring Of Dryland Ecosystemmentioning

confidence: 99%

Soil indicators to assess the effectiveness of restoration strategies in dryland ecosystems

Costantini¹,

et al. 2016

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Abstract. Soil indicators may be used for assessing both land suitability for restoration and the effectiveness of restoration strategies in restoring ecosystem functioning and services. In this review paper, several soil indicators, which can be used to assess the effectiveness of ecological restoration strategies in dryland ecosystems at different spatial and temporal scales, are discussed. The selected indicators represent the different viewpoints of pedology, ecology, hydrology, and land management. Two overall outcomes stem from the review. (i) The success of restoration projects relies on a proper understanding of their ecology, namely the relationships between soil, plants, hydrology, climate, and land management at different scales, which are particularly complex due to the heterogeneous pattern of ecosystems functioning in drylands.(ii) The selection of the most suitable soil indicators follows a clear identification of the different and sometimes competing ecosystem services that the project is aimed at restoring.

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“…The prevalent MODfrm includes linear, polynomial, power, logarithmic, and exponential function forms (Gao et al, 2013a;Jin et al, 2014;Yang et al, 2009). These regression models have performed well in Sahel of Africa (Tucker et al, 1985), Patagonia of Argentina (Gaitan et al, 2013), Colombia (Anaya et al, 2009), and China (Jin et al, 2014). Furthermore, a considerable amount of uncertainties remain in model parameters such as the ratios of root and shoot (R/S) (Wang et al, 2010;Yang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Jia

Liu

Yang

et al. 2016

Ecological Indicators

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a b s t r a c tAccurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during [2001][2002][2003][2004][2005]. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 ± 180.84 Tg (i.e., 532.02 ± 99.71 g/m 2 ) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo > DATsrc > MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.

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Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Cited by 87 publications

References 56 publications

Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

Soil indicators to assess the effectiveness of restoration strategies in dryland ecosystems

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

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