Methods to Quantify Regional Differences in Land Cover Change

Comber, Alexis; Balzter, Heiko; Cole, Beth; Fisher, Peter; Johnson, Sarah; Ogutu, Booker

doi:10.3390/rs8030176

Cited by 24 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To explore indicative differences in land cover change in different types of regions in the study areas, the ratio of the odds of change between Region 1 and Region 2 was calculated, as described in Comber et al [42]. These approaches can be used to compare per class differences in change, specific class-to-class changes as well as regional differences.…”

Section: Inter-regional Analysismentioning

confidence: 99%

“…This may be indicative of a misclassification in this region between IS and Bare Soil due to their spectral similarity and a potential overestimation of IS. It is also possible to quantify the regional differences in the probability of different changes using relative odds ratios [42]. The relative likelihood of change to IS from any land cover class was 6.7 times more likely in Region 2 (peri-urban) than in Region 1 (urban).…”

Section: Inter-region Analysis Of Region 1 and Regionmentioning

confidence: 99%

“…IS to WT 0.000 WT to IS 0.054 IS to FP 0.326 FP to IS 0.489 IS to VG 0.000 VG to IS 0.090 It is also possible to quantify the regional differences in the probability of different changes using relative odds ratios [42]. The relative likelihood of change to IS from any land cover class was 6.7 times more likely in Region 2 (peri-urban) than in Region 1 (urban).…”

Section: Loss Class Likelihood Gain Class Likelihoodmentioning

confidence: 99%

“…These were used to train statistical classifier and used to predict class-to-class change, including no change. Regional variations in LCLU change were determined using the methods described in Comber et al [42]. This is a novel approach for determining LCLU change, one that uses measures derived from change vectors and applied to bi-temporal image data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Modified Change Vector Approach for Quantifying Land Cover Change

Lin

Lü

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

This paper develops and applies a novel method for inferring land cover/land use (LCLU) change that combines direct multi-date classification with measures from a change vector analysis. The model predicts change directly rather than the land cover at either time, although these could be inferred. Unsupervised classifications of bi-temporal imagery were manually labeled and used to generate reference data for class-to-class changes. These were used to train a Random Forest model with inputs from the bi-temporal image bands and change vector measures (change vector direction, angle and the spectral angle) and used to generate a predicted surface of land cover change for a case study in the Pearl River Delta, China. The overall accuracy of LCLU change prediction was 96% and specific class-to-class changes had errors rates of 0-12.8%. Some errors were related the semi-automated labeling of the training data. The spectral angle variables and Near Infra-Red image bands for both years were found to be strong predictors of change. Odd ratios were used to quantify regional differences in land cover change rates in urban and peri-urban areas. The regional differences and origins of the observed errors are discussed, along with some areas of further work. The key contributions of this paper are the focus on change rather than LCLU through the construction of a model to predict changes directly and the development of an approach that provides quick, effective and informative analysis of LCLU change in support of policy and planning in rapidly urbanizing areas.

show abstract

Section: Inter-regional Analysismentioning

confidence: 99%

Section: Inter-region Analysis Of Region 1 and Regionmentioning

confidence: 99%

Section: Loss Class Likelihood Gain Class Likelihoodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Change Vector Approach for Quantifying Land Cover Change

Lin

Lü

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The quantification of land cover and land use change is tackled in the paper by Comber et al [46], who outline some statistical approaches for quantifying and visualizing land use and land cover transitions between multiple regions and how these might be used in the development of different land management strategies. Demonstrated using National Trust land use data in the United Kingdom, these approaches could be applied at global scales.…”

Section: Quantification Of Land Use and Land Cover Changementioning

confidence: 99%

Towards an Integrated Global Land Cover Monitoring and Mapping System

et al. 2016

View full text Add to dashboard Cite

Global land cover mapping has evolved in a number of ways over the past two decades including increased activity in the areas of map validation and inter-comparison, which is the main focus of this Special Issue in Remote Sensing. Here we describe the major trends in global land cover mapping that have occurred, followed by recent advances as exemplified by the papers in the Special Issue. Finally, we consider what the future holds for global land cover mapping.Keywords: validation; land cover; land use; data fusion; global land cover mapping; map inter-comparison Trends in Global Land Cover MappingGlobal-scale land cover provides essential information for policy development and scientific applications such as climate modeling, food security, carbon assessment, biodiversity and environmental modeling [1]. Global-scale land cover mapping has therefore been of interest to many researchers over the last two decades. After the initial attempts at producing a global land cover (GLC) map at a one degree resolution using remote sensing [2], a number of different medium-resolution (300-1000 m) GLC maps have been developed [3][4][5][6]. This has, in turn, led to the production of integrated or hybrid maps [7,8], which are based on exploiting the strengths of individual GLC maps. With continued advancements in remote sensing data and technology, more GLC maps are currently being produced. Here we summarize the trends in available GLC maps with respect to spatial, thematic and temporal properties, along with their accuracy assessments and user considerations.In the last five years, at least 14 GLC maps have been produced, which accounts for more than half of the currently available GLC products. Figure 1 summarizes the trends in the production of GLC maps over the last two decades.

show abstract

Improving Business-as-Usual Scenarios in Land Change Modelling by Extending the Calibration Period and Integrating Demographic Data

Mejean

Paegelow

Saqalli

et al. 2019

Lecture Notes in Geoinformation and Cartography

View full text Add to dashboard Cite

Land use and land cover change (LUCC) models are increasingly being used to anticipate the future of territories, particularly through the prospective scenario method. In the case of so-called trend or Business-as-Usual (BAU) scenarios, the aim is to observe the current dynamics and to extend them into the future. However, as they are implemented as baseline simulation in most current software packages, BAU scenarios are calibrated from a training period built from only two dates. We argue that this limits the quantitative estimation of future change intensity, and we illustrate it from a simple model of deforestation in Northern Ecuadorian Amazon using the Land Change Modeler (LCM) software package. This paper proposes a contribution to improve BAU scenarios calibration by mainly two enhancements: taking into account a longer calibration period for estimating change quantities and the integration of thematic data in change probabilities matrices. We thus demonstrate the need to exceed the linear construction of BAU scenarios as well as the need to integrate thematic and particularly socio-demographic data into the estimation of future quantities of change. The spatial aspects of our quantitative adjustments are discussed and tend to show that improvements in the quantitative aspects should not be dissociated from an improvement in the spatial allocation of changes, which may lead to a decrease in the predictive accuracy of the simulations.

show abstract

Methods to Quantify Regional Differences in Land Cover Change

Cited by 24 publications

References 26 publications

A Modified Change Vector Approach for Quantifying Land Cover Change

A Modified Change Vector Approach for Quantifying Land Cover Change

Towards an Integrated Global Land Cover Monitoring and Mapping System

Improving Business-as-Usual Scenarios in Land Change Modelling by Extending the Calibration Period and Integrating Demographic Data

Contact Info

Product

Resources

About