Can we combine structural functionality and landscape services assessments in order to estimate the impact of landscape structure on landscape services?

Skokanová, Hana

doi:10.2478/mgr-2013-0016

Cited by 12 publications

(5 citation statements)

References 41 publications

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“…Yield productivity zones were evaluated as a feasible way of discovering the areas within a plot with higher-than-average and lower-than-average yields. The identification and computation of yield productivity zones is crucial for increasing the yield while minimizing the environmental pollution from agricultural activities by reducing the amount of fertilizer(s) added to the soil [60,61]. The authors would also like to emphasize that yield productivity zones are not a comprehensive solution but an additional input for further modelling, processes and decision-making.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

et al. 2020

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Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.

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Section: Discussionmentioning

confidence: 99%

Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

et al. 2020

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“…European nitrate directive and its influence on the farm performance were described by [21]. Discussion on accessing structural functionality and landscape service is provided in [27].…”

Section: Methodsmentioning

confidence: 99%

Open Data Model for (Precision) Agriculture Applications and Agricultural Pollution Monitoring

Řezník¹,

Charvát²,

Vojtěch³

et al. 2015

Advances in Computer Science Research

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Both agricultural and environmental domains have to manage many different and heterogeneous sources of information that need to be combined in order to make environmentally and economically sound decisions. Such examples may be found at the definition of subsidies, national strategies for rural development, development of sustainable agriculture etc. This paper describes in detail the development of an open data model for (precision) agriculture applications and agricultural pollution monitoring when aiming at identification of requirements from users from agricultural and environmental domains. The presented open data model for (precision) agriculture applications and agricultural pollution monitoring has been registered under the GEOSS (Global Earth Observation System of Systems) Architecture Implementation Pilot-Phase 8 in order to support the wide variety of demands that are primary aimed at agriculture and water pollution monitoring.

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“…times (Burkhard et al 2012, Schneiders et al 2012, Skokanová 2013, Stoll et al 2015. In addition to the semiquantitative approach, Burkhard et al (2009Burkhard et al ( , 2012 provided quantitative approaches for 2 ecosystem services, food provisioning and energy provisioning, based on indicators (e.g., energy yield from crops in GJ ha 21 y 21 or wind energy in GJ ha 21 y 21 ) that were linked to landscape classes.…”

Section: Landscape Classification Systems For Riverine Ecosystem Servmentioning

confidence: 99%

Suitable landscape classification systems for quantifying spatiotemporal development of riverine ecosystem services

et al. 2018

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River systems provide numerous ecosystem services that contribute to human well-being. Biophysical quantification of spatiotemporal development of ecosystem services is useful for environmental impact assessments or scenario analyses of river management and could be done by linking biophysical indicators of relevant ecosystem services to landscape classifications that allow analyses of natural and management-induced changes in riverscape characteristics. We analyzed 126 case studies in which landscape classification systems (LCSs) were applied over the period 1989-2014. LCSs were mostly applied at regional (subnational) scales and linked to ecosystem services in 46 case studies. Ecosystem services were linked to landscape patches based on quantitative (monetary or biophysical) or semiquantitative approaches. Only 6 case studies linked ecosystem services to river systems. The number of ecosystem services quantified by biophysical indicators and linked to landscape classes also was limited. Moreover, the spatiotemporal development of these indicators in relation to landscape changes is poorly elaborated. Six selected LCSs were considered suitable for application to river systems and biophysical quantification of spatiotemporal development of ecosystem services (e.g., Coordination of Information on the Environment [CORINE] Land Cover, River Ecotope Classification). Future research should be directed to developing sound indicators for quantification of river ecosystem services and analyzing how these services develop spatiotemporally in relation to natural and anthropogenic changes of the riverscape.

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Can we combine structural functionality and landscape services assessments in order to estimate the impact of landscape structure on landscape services?

Abstract: Abstract

Cited by 12 publications

References 41 publications

Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

Open Data Model for (Precision) Agriculture Applications and Agricultural Pollution Monitoring

Suitable landscape classification systems for quantifying spatiotemporal development of riverine ecosystem services

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