Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery

Ellis, Erle C.; Wang, Hongqing; Xiao, He; Peng, Kui; Liu, Xin Ping; Li, Shou Cheng; Ouyang, Hao; Xu, Chuncheng; Yang, Lin

doi:10.1016/j.rse.2005.11.002

Cited by 98 publications

(56 citation statements)

References 68 publications

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“…The supervised classification approach employed here involved training a Maximum Likelihood classifier algorithm to first recognise particular spectral patterns as representative of certain user-defined land-cover classes of interest, then classify unknown pixels into one of the land-cover classes based on the likelihood of that pixel's spectral signature falling within a normal distribution of the spectral values of a particular land class. Supervised classification's binning of a pixel into one of several user-defined land classes based on overall spectral profile may fail to capture the more nuanced biophysical meaning behind a pixel, and such classification schemes may also substantially bypass land-change processes such as forest degradation that tend to be heterogeneous on finer, sub-pixel scales [58,59]. Moreover, dividing continuous quantitative information, such as those found in satellite images, into a finite number of discrete land classes that are considered at the outset to be exhaustively defined and mutually exclusive may lend itself to the further loss of information [60].…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Section: Discussionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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“…Land management and vegetation cover change substantially and landscape complexity increases (Ellis et al, 2005). The urban area of Arequipa city expanded from 60.56-69.13 km² in a period of 17 years, which is an average rate of 0.5 km² per year.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Environmental Impacts of Urban Growth Using Land Use/Land Cover, Water Quality and Health Indicators: A Case Study of Arequipa, Peru

Carpio¹

2011

American Journal of Environmental Sciences

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Problem statement:This research assesses the direct effects of urban expansion on land cover/use, river flow, water quality and the indirect effects of these variables in the rate of gastrointestinal disease in people in Arequipa, Peru through the combined use of satellite remote sensing and geographic information systems. Approach: It also uses information about demographic changes, hydrologic data and land cover change in the Arequipa region for the last 17 years. The goal is to understand the relationship between urbanization, water quality in the Chili River and incidence of gastrointestinal diseases. Results: Landsat imagery was used to determine this relationship and to extrapolate business as usual trends into the future ten years from now. Results indicate that there has been notable urban growth and a loss in volcanic material land and cropland between 1990 and 2007, as new urban developments have appeared in these areas. The population expansion over volcanically active area is particularly troubling since it poses a potential human health risk. We also model a business as usual scenario out to the year 2020, which shows continued loss of these land use types and serves as a warning for land managers to consider alternate policies. Conclusion/Recommendations: The analysis also shows a direct correlation between urbanization with the decrease of water quality and the increase in the incidence of gastrointestinal diseases.

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“…Crop type information is crucial for water use assessment, productivity assessments, and many other practical applications of data and maps at local levels. Accurate crop classification is the key to determining many other crop specific parameters [70] such as water use by crops, water productivity, biomass, yield, and carbon sequestration [19,71].…”

Section: Referencesmentioning

confidence: 99%

A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

Thenkabail

Hanjra²,

Dheeravath³

et al. 2010

Remote Sensing

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This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world's leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R 2 -value of 0.89-0. of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40-62 percent. The weaknesses, trends, and future directions to precisely estimate the

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Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery

Cited by 98 publications

References 68 publications

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Assessing the Environmental Impacts of Urban Growth Using Land Use/Land Cover, Water Quality and Health Indicators: A Case Study of Arequipa, Peru

A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

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