Issues in using landscape indicators to assess land changes

Dale, Virginia H.; Kline, Keith L.

doi:10.1016/j.ecolind.2012.10.007

Cited by 64 publications

(26 citation statements)

References 49 publications

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“…Deforestation under the tropics contributes to the increasing rate of emission of greenhouse gases in the atmosphere and regional albedo (Skole and Tucker 1993;Laurance 1999;Sierra 2000;Justice et al 2001;Miles 2006;Brooks 2008). Monitoring of forest ecosystems in the context of climate global warming research is becoming a great deal in ecological, environmental, and socioeconomic fields (Dale and Kline 2012). Several approaches regarding carbon dioxide assessment, including land use studies and estimation of biomass production, net primary production rate, and potential for carbon sequestration, were developed to ensure the sustainable management of carbon flux in tropical forest ecosystems (Fahey et al 2009;Hairiah et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Long term evaluation of green vegetation cover dynamic in the Atacora Mountain chain (Togo) and its relation to carbon sequestration in West Africa

et al. 2015

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Evaluat ora Mou uestratio A 1, 3 http:// WOEGAN 1 http://orci A 1 http://o LA 1 http:// BURANGA 2 http://orc ttp://orcid.org http://orcid.o vegetation, including its potentiality in carbon sinking, was negatively affected in this area, which has already been under threatened. A perpetual monitoring of these ecosystems by means of efficient techniques could enhance the sustainable management tools of in the framework of reducing emissions from deforestation and forest degradation (REDD).

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Section: Introductionmentioning

confidence: 99%

Long term evaluation of green vegetation cover dynamic in the Atacora Mountain chain (Togo) and its relation to carbon sequestration in West Africa

et al. 2015

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“… Landscape isolation index refers to the degree of separation of individual patches of a given landscape type, which relates with landscape diffuse distribution and fragmentation (Li, Li, Fu, Fen, & Shi, ):

V_{i} = \frac{D_{italicij}}{A_{italicij}},

where V i is the separation degree of landscape type i , D ij is the distance index of landscape type i , and A ij is the area index of landscape type i . Landscape dominance index is used to describe the dominance of patches in a given landscape type and indicates their position in the landscape (Dale & Kline, ):

D = H_{\max} + \sum_{i = 1}^{m} ((), P_{i}) \times log ((), P_{i}),

where D is the landscape dominance index, H max is the maximum value of the dominance index, P i is the probability of patch type i being present in the landscape, and m is the total number of patch types in the landscape.…”

Section: Methodsmentioning

confidence: 99%

“…3. Landscape dominance index is used to describe the dominance of patches in a given landscape type and indicates their position in the landscape (Dale & Kline, 2013):…”

Section: Construction Of Landscape Ecological Risk Assessment Modelmentioning

confidence: 99%

Landscape ecological risk assessment in Qinling Mountain

et al. 2018

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In this paper, landscape ecological risk in Qinling Mountain was studied. Using the remote sensing images of Landsat TM and DEM data in 1984, 2000, 2005, and 2014, an ecological risk assessment model was constructed, and landscape ecological risk indexes were calculated for four time periods 1984, 2004, 2005 and 2014. The spatial distribution of ecological risk was obtained with ArcGIS and geostatistics, and changes in the landscape patterns and spatiotemporal characteristics of ecological risk were analysed. As shown in the results; (a) from 1984 to 2014, the landscape pattern index of Qinling forest area was relatively stable; fragmentation and segregation decreased, and dominance and area increased. The fragmentation and separation of cultivated land increased over time, and the geographical distribution of cultivated land diversified, while its dominance decreased. (b) The areas of extremely low and extremely high ecological risk level in the study area is gradually reduced. But the area of high ecological risk level increased obviously. The extremely high ecological risk area was mainly distributed in the middle and south‐eastern regions. The extremely low and low risk areas were mainly distributed in the low hilly areas of northern Qinling.

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“…This strength can become a weakness any time a given indicator is not considered in context or when approximations and limitations are not clear and not properly taken into account or communicated. There are clear examples of misuse of indicators (e.g., [1]). Issues may be related to different aspects included, but not limited to, accuracy, original intent and extent.…”

Section: Introductionmentioning

confidence: 99%

Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis

Pileggi

2019

Sustainability

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Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about various aspects of the World performance may suggest that it should be "easy" to evaluate the overall success of human enterprise on this planet. Moreover, it also points out the intrinsic importance in the selection of indicators. However, people have different values, biases, and preferences about the importance of various indicators, making it almost impossible to find an objective answer to this question. To address the variety and the heterogeneity of available indicators and world views, we present the analysis of global World performance as a multi-criteria decision problem, making sure that the assessment method remains as transparent as possible. By dynamically selecting a set of indicators of interest, defining the weights that we attach to various indicators and specifying the desired trends associated with each indicator, we make the assessment adaptive to individual values. We also try to deal with the inherent bias that may exist in the set of indicators that are chosen. As a study case, from various data sets that are openly available online, we have selected several that are most relevant and easy to interpret in the context of the question in the title of the paper. We demonstrate how the choice of personal preferences, or weights, can strongly change the result. Our method also provides analysis of the weights space, showing how results for particular value sets compare to the average and extreme (optimistic and pessimistic) combinations of weights that may be chosen by users.

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Issues in using landscape indicators to assess land changes

Cited by 64 publications

References 49 publications

Long term evaluation of green vegetation cover dynamic in the Atacora Mountain chain (Togo) and its relation to carbon sequestration in West Africa

Long term evaluation of green vegetation cover dynamic in the Atacora Mountain chain (Togo) and its relation to carbon sequestration in West Africa

Landscape ecological risk assessment in Qinling Mountain

Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis

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