Monitoring 20 years of increased grazing impact on the Greek island of Crete with earth observation satellites

Hill, Joachim; Hostert, Patrick; Tsiourlis, Georgios; Kasapidis, Panagiotis; Udelhoven, T.; Diemer, C.

doi:10.1006/jare.1998.0392

Cited by 67 publications

(36 citation statements)

References 6 publications

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“…Shrub encroachment is not a regional phenomenon, but is observed in various Mediterranean landscapes [5][6][7], as well as in other arid to semi-arid regions around the globe [4,8], which cover approximately 40% of the Earth's terrestrial surface [9]. It is, hence, of special importance to monitor and manage these areas in a sustainable way.…”

Section: Introductionmentioning

confidence: 99%

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

et al. 2014

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Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes. OPEN ACCESSRemote Sens. 2014, 6 3428

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Section: Introductionmentioning

confidence: 99%

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

et al. 2014

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“…C. damasonium and N. ovata are not rare in greece, but they have a restricted distribution on Crete (Kretzschmar et al 2002) and are endangered by overgrazing. as mentioned by Hill et al (1998), Crete represents a characteristic case of land degradation resulting from hundreds of years of intensive grazing. However, both absence of grazing animals and overgrazing could cause the decrease of orchid populations, due to vegetation encroachment or an increase in soil compaction.…”

Section: Discussionmentioning

confidence: 99%

Identifying important areas for orchid conservation in Crete

Tsiftsis¹,

Tsiripidis²,

Trigas³

2012

European Journal of Environmental Sciences

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Crete is especially rich in orchids and their distribution is well known and documented by many floristic accounts. This information, however, is yet to be used to set conservation plans and priorities. We used MaxEnt incorporating both published and unpublished distribution data together with environmental variables to predict the potential distribution of orchids on Crete. The resulting probabilistic maps of species occurrence were used to identify the important areas for orchid conservation on the island. Sites prioritization was performed by applying a species weighting scheme, which was based on species niche breadth. The existence of ecological patterns determining site prioritization was determined using a regression tree analysis based on environmental variables and scores derived from Zonation analysis. The high importance sites were found on Mts Thripti, Ida and Lefka Ori, as well as at low altitude areas east of Heraklion and at the easternmost part of the island. Most of the variation in the site scores was explained by geological substrate, latitude and altitude. Based on the regression tree analysis, sites with the highest scores were at medium and high altitude areas, which are located at the interior of the island. These areas have soils mainly derived from limestones, ophiolites and deposits of calcareous rocks. The lack of a significant effect of vegetation type in explaining the distribution of high importance areas highlights the need for the establishment of micro-reserves for the conservation of orchids in Crete. Finally, endangered orchid species in need of specific conservation actions are indicated.

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“…The unique assemblages of biota associated with BSC create detectable spectral characteristics ( Figure 6) [73][74], [79][80][81][82][83]. Figure 7 shows spectral changes in a Mojave desert scrub (Larrea tridentata, Ambrosia dumosa, and Lycium spp.)…”

Section: Biophysical Information From Biological Soil Crustsmentioning

confidence: 99%

Remote sensing based assessment of biophysical indicators for land degradation and desertification

Li¹,

Ustin²,

Palacios-Orueta³

et al. 2009

International Society for Photogrammetry and Remote Sensing (ISPRS) Book Series

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Desert ecosystems spanning moisture conditions from dry grasslands to barren hyper-arid landscapes are the largest terrestrial biome with more than 40% of the terrestrial landmass. Remote sensing data provide an efficient costeffective means to assess biophysical indicators of land degradation and desertification, providing that essential ecosystem properties can be monitored. We review the spectral characteristics of plants and soils that are detectable using optical sensors and methods to identify and quantify properties that have potential for monitoring arid ecosystem processes. Vegetation indexes have little sensitivity at low leaf area, particularly when the soil background is highly variable, as is characteristic of many arid systems. Additionally, accumulated dry plant material on the soil surface challenges measurement. Although the absorption characteristics of the major biochemical constituents of plants and soils are generally understood, the methods to retrieve this information from reflectance data and to understand the significance of how the structural organization alters the absorption features remains an area of active research. The overlapping absorption features of plants and soils preclude direct assessment of many biogeochemicals of interest. New biophysical methods that take the full spectral shape into account, including the effect of one compound on the spectral absorption of another, are needed to reduce uncertainty in their estimates. As a result, despite significant progress in developing fundamental understanding of ecosystem processes and optical properties, more research is needed before fully predictable quantitative methods are available.

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Monitoring 20 years of increased grazing impact on the Greek island of Crete with earth observation satellites

Cited by 67 publications

References 6 publications

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

Identifying important areas for orchid conservation in Crete

Remote sensing based assessment of biophysical indicators for land degradation and desertification

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