Understanding transportation-caused rangeland damage in Mongolia

Keshkamat, S.S.; Tsendbazar, Nandin‐Erdene; Zuidgeest, Mark; Shiirev-Adiya, S.; Veen, A. van der; Maarseveen, M.F.A.M. van

doi:10.1016/j.jenvman.2012.10.043

Cited by 21 publications

(14 citation statements)

References 26 publications

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“…While constructing more road networks expanded the domain of human activities, it also might lead to more damage to the grassland vegetation due to the easier access to vegetated areas. The side impact on vegetation due to road networks on grasslands has been documented by a few studies investigated in Inner Mongolia and Mongolia [18,57]. The paper [57] also analyzed a few reasons, including the hardness of the top layer and soil erosion, for the damage imposed by road tracks on grasslands.…”

Section: Discussionmentioning

confidence: 99%

“…The side impact on vegetation due to road networks on grasslands has been documented by a few studies investigated in Inner Mongolia and Mongolia [18,57]. The paper [57] also analyzed a few reasons, including the hardness of the top layer and soil erosion, for the damage imposed by road tracks on grasslands. The BLR models in the current study suggest that a one-unit increase in the standardized road network density led to a five to six-fold increase in the probability of vegetation degradation.…”

Section: Discussionmentioning

confidence: 99%

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Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

Wang

Li³

et al. 2018

Sustainability

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Abstract:Grassland ecosystems worldwide are confronted with degradation. It is of great importance to understand long-term trajectory patterns of grassland vegetation by advanced analytical models. This study proposes a new approach called a binary logistic regression model with neighborhood interactions, or BLR-NIs, which is based on binary logistic regression (BLR), but fully considers the spatio-temporally localized spatial associations or characterization of neighborhood interactions (NIs) in the patterns of grassland vegetation. The BLR-NIs model was applied to a modeled vegetation degradation of grasslands in the Xilin river basin, Inner Mongolia, China. Residual trend analysis on the normalized difference vegetation index (RESTREND-NDVI), which excluded the climatic impact on vegetation dynamics, was adopted as a preprocessing step to derive three human-induced trajectory patterns (vegetation degradation, vegetation recovery, and no significant change in vegetation) during two consecutive periods, T 1 (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) and T 2 (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Human activities, including livestock grazing intensity and transportation accessibility measured by road network density, were included as explanatory variables for vegetation degradation, which was defined for locations if vegetation recovery or no significant change in vegetation in T 1 and vegetation degradation in T 2 were observed. Our work compared the results of BLR-NIs and the traditional BLR model that did not consider NIs. The study showed that: (1) both grazing intensity and road density had a positive correlation to vegetation degradation based on the traditional BLR model; (2) only road density was found to positively correlate to vegetation degradation by the BLR-NIs model; NIs appeared to be critical factors to predict vegetation degradation; and (3) including NIs in the BLR model improved the model performance substantially. The study provided evidence for the importance of including localized spatial associations between the trajectory patterns for mapping vegetation degradation, which has practical implications for designing management policies to counterpart grassland degradation in arid and semi-arid areas.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

Wang

Li³

et al. 2018

Sustainability

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“…Vegetation change in the Sahel appears to be more influenced by precipitation than livestock effects and thus is an example of nonequilibrium dynamics (Ellis and Swift 1988) 23,24 . Mongolian rangelands today are increasingly at risk from heavy livestock grazing (Liu et al 2013) as well as soil degradation associated with road networks (Keshkamat et al 2013) and pollution of soil and water from the expansion of mining (Thorslund et al 2012). There is debate, however, about the causes of declining range conditions in Mongolia (Addison et al 2012).…”

Section: Trends In Resource Use and Ecological Condition Of Rangelandsmentioning

confidence: 99%

Rangeland Systems in Developing Nations: Conceptual Advances and Societal Implications

Coppock

Fernández‐Giménez

Hiernaux

et al. 2017

Springer Series on Environmental Management

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Developing-country rangelands are vast and diverse. They are home to millions who are often poor, politically marginalized, and dependent on livestock for survival. Here we summarize our experiences from six case-study sites in subSaharan Africa, central Asia, and Latin America generally covering the past 25 years. We examine issues pertaining to population, natural resource management, climate, land use, livestock marketing, social conflict, and pastoral livelihoods. The six study sites differ with respect to human and livestock population dynamics and This chapter is dedicated to the memory of an exemplary international rangeland scientist, Thomas L. Thurow (1955Thurow ( -2016. the resulting pressures on natural resources. Environmental degradation, however, has been commonly observed. Climate change is also having diverse systemic effects often related to increasing aridity. As rangelands become more economically developed pastoral livelihoods may diversify, food security can improve, and commercial livestock production expands, but wealth stratification widens. Some significant upgrades in rural infrastructure and public service delivery have occurred; telecommunications are markedly improved overall due to widespread adoption of mobile phones. Pressures from grazing, farming, mining, and other land usescombined with drought-can ignite local conflicts over resources, although the intensity and scope of conflicts markedly varies across our case-study sites. Pastoralists and their herds have become more sedentary overall due to many factors, and this can undermine traditional risk-management tactics based on mobility. Remote rangelands still offer safe havens for insurgents, warlords, and criminals especially in countries where policing remains weak; the resulting civil strife can undermine commerce and public safety. There has been tremendous growth in knowledge concerning developing-country rangelands since 1990, but this has not often translated into improved environmental stewardship or an enhanced wellbeing for rangeland dwellers. Some examples of demonstrable impact are described, and these typically have involved longer-term investments in capacity building for pastoralists, local professionals, and other stakeholders. Research is shifting from ecologically centered to more human-centered issues; traditional academic approaches are often being augmented with participatory, community-based engagement. Building human or social capital in ways that are integrated with improved natural resource stewardship offers the greatest returns on research investment. Our future research and outreach priorities include work that fortifies pastoral governance, enhances livelihoods for a diverse array of rangeland residents, and improves land and livestock management in a comprehensive social-ecological systems approach. Keywords IntroductionIn this chapter we focus on rangelands of the developing world. By rangelands we refer to landscapes-largely unsuitable for sustained cultivation-providing forage, water, ...

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“…There are three kinds of methods for mapping PVC using remote sensing data, including parametric methods such as regression modeling, nonparametric methods such as k-nearest neighbors (kNN), and spectral unmixing analyses. Various regression models can be used to develop the relationship of PVC with remote sensing variables, including linear and nonlinear regression models, geographically weight regression (GWR) and so on [25][26][27][28]. However, the parametric methods require the assumption of normal or non-normal distributions of variables and strong relationships of PVC with remote sensing variables.…”

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confidence: 99%

“…GWR is also a parametric method but Remote Sens. 2018, 10, 1248 3 of 27 results in spatially variable coefficients of regression and thus has greater potential to reduce the overestimations and underestimations [26,27].Spectral unmixing analyses can also be utilized to derive the fractions of PVC for arid and semi-arid areas [35][36][37][38][39][40]. Most of the studies are based on linear rather than nonlinear spectral unmixing analysis.…”

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confidence: 99%

Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images

Sun

Wang

et al. 2018

Remote Sensing

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Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas. IntroductionLand degradation and desertification is a serious ecological and environmental problems and has received worldwide attention [1][2][3]. Percentage vegetation cover (PVC) represented with the range of values from 0.0 to 1.0 in this study is one of the effective indicators for assessing land degradation and desertification in arid and semi-arid areas and has been widely used. However, collecting field measurements of PVC in remote and sparsely populated arid and semi-arid areas is labor-intensive and time-consuming [4]. This method works for small areas only, which cannot provide the detailed information of spatial characteristics and temporal trend of PVC at a regional or global scale. Compared with the traditional method, remote sensing technologies can repeatedly offer images that cover a same region and quantify the spatial variability and temporal dynamics of PVC. Moreover, mapping PVC using remotely sensed images al...

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Understanding transportation-caused rangeland damage in Mongolia

Cited by 21 publications

References 26 publications

Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

Rangeland Systems in Developing Nations: Conceptual Advances and Societal Implications

Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images

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