Monitoring land use and cover around parks: A conceptual approach

Jones, Danielle A.; Hansen, Andrew J.; Bly, Kristy; Doherty, Kevin E.; Verschuyl, Jake; Paugh, Justin I.; Carle, Robin; Story, Scott

doi:10.1016/j.rse.2008.08.018

Cited by 80 publications

(55 citation statements)

References 35 publications

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“…For example, the benefits from the mitigation of the urban heat island effect by urban trees flow to urban dwellers, but the carbon sequestration benefits of those same trees are potentially shared by all of humanity. These mismatches can result from (i) administrative boundaries that do not match ecological entities, (ii) temporal dynamics of ecological processes not being considered and (iii) the interplay with adjacent systems being neglected (Bai et al, 2010;Borgström et al, 2006;Jones et al, 2009;Ramalho and Hobbs, 2012).…”

Section: Ecological Structuresmentioning

confidence: 99%

A review of urban ecosystem services: six key challenges for future research

et al. 2015

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a b s t r a c tGlobal urbanization creates opportunities and challenges for human well-being and transition towards sustainability. Urban areas are human-environment systems that depend fundamentally on ecosystems, and thus require an understanding of the management of urban ecosystem services to ensure sustainable urban planning. The purpose of this study is to provide a systematic review of urban ecosystems services research, which addresses the combined domain of ecosystem services and urban development. We examined emerging trends and gaps in how urban ecosystem services are conceptualized in peer-reviewed case study literature, including the geographical distribution of research, the development and use of the urban ecosystem services concept, and the involvement of stakeholders. We highlight six challenges aimed at strengthening the concept's potential to facilitate meaningful inter-and transdisciplinary work for ecosystem services research and planning. Achieving a cohesive conceptual approach in the research field will address (i) the need for more extensive spatial and contextual coverage, (ii) continual clarification of definitions, (iii) recognition of limited data transferability, (iv) more comprehensive stakeholder involvement, (v) more integrated research efforts, and (vi) translation of scientific findings into actionable knowledge, feeding information back into planning and management. We conclude with recommendations for conducting further research while incorporating these challenges.

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Section: Ecological Structuresmentioning

confidence: 99%

A review of urban ecosystem services: six key challenges for future research

et al. 2015

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“…Object-based classification of land use and land cover often results in higher accuracies, when compared to pixel-based classifications [41][42][43][44]. Some studies also used hierarchical classification approaches, mainly to integrate different data types into a comprehensive mapping framework [45][46][47][48]. However, to our knowledge, no study has used segmentation algorithms to make better use of field size information inherent in satellite images in order to improve the mapping of agricultural management intensity.…”

Section: Introductionmentioning

confidence: 99%

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

et al. 2014

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Abstract:The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, Remote Sens. 2014, 6 5280 object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors.

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“…Furthermore, land cover change has substantial impacts on a vast array of environmental systems including hydrological (Eshleman, 2004), ecological (Vitousek, 1994) and geomorphological (Foulds and Macklin, 2006). Land cover change 4 is, for example, one of the greatest causes of biodiversity loss and hence a central variable in studies of biodiversity conservation (Duro et al, 2007;Gillespie et al, 2008;Jones et al, 2009). Accurate and up-to-date information on land cover and land cover change is, therefore, required for many applications.…”

Section: Introductionmentioning

confidence: 99%

Assessing the accuracy of land cover change with imperfect ground reference data

Foody

2010

Remote Sensing of Environment

279

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The manuscript of the above article revised after peer review and submitted to the journal for publication, follows. Please note that small changes may have been made after submission and the definitive version is that subsequently published as: AbstractThe ground data used as a reference in the validation of land cover change products are often not an ideal gold standard but degraded by error. The effects of ground reference data error on the accuracy of land cover change detection and the accuracy of estimates of the extent of change was evaluated. Twelve data sets were simulated to allow the exploration of the impacts of a spectrum of ground data imperfections on the estimation of the producer's and user's accuracy of change as well as of change extent. Simulated data were used since this ensured that the actual properties of the data were known and to exclude effects due to other sources of ground reference data error; although the impacts of simulated reference data on two real confusion matrices is also illustrated. The imperfections evaluated ranged from the inclusion of small amounts of known error into the ground reference data through to the extreme situation in which ground data were absent. The results show that even small amounts of error in the ground reference data can introduce large error into studies of land cover change by remote sensing and reinforce the desire to avoid the expression ground truth as this might imply that the data are a gold standard reference. The effect of reference data imperfections was dependent on the degree of association between the errors in the cross tabulated data sets. For 3 example, in the scenarios investigated, a 10% error in the reference data set introduced an under-estimation of the producer's accuracy of 18.5% if the errors were independent but an over-estimation of the producer's accuracy of 12.3% if the errors were correlated. The magnitude of the mis-estimation of the producer's accuracy was also a function of the amount of change and greatest at low levels of change. The amount of land cover change estimated also varied greatly as a function of ground reference data error. Some possible methods to reduce or even remove the impacts of ground reference data error were illustrated. These ranged from simple algebraic means to estimate the actual values of accuracy and change extent if the imperfections were known through to a latent class analysis that allowed the assessment of classification accuracy and estimation of change extent without the use of ground reference data if the underlying model is defined appropriately.

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Monitoring land use and cover around parks: A conceptual approach

Cited by 80 publications

References 35 publications

A review of urban ecosystem services: six key challenges for future research

A review of urban ecosystem services: six key challenges for future research

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

Assessing the accuracy of land cover change with imperfect ground reference data

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