Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Chang-Martínez, Laura Alfonsina; Mas, Jean-François; Valle, Nuria Torrescano; Torres, Pedro Sergio Urquijo; Folan, William J.

doi:10.3390/ijgi4041791

Cited by 36 publications

(23 citation statements)

References 83 publications

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“…According to the classification proposed by National Research Council (NRC, 2014), LULC change modeling approaches can be placed on a spectrum of pattern-to process-based models (Table 1). There are two representative types of models along the spectrum: one is oriented strongly towards describing and extrapolating past patterns (Figure 1), and the other is designed to represent the environmental and human decision processes that cause changes in patterns (Brown et al, 2013;Chang-Martinez et al, 2015). However, these approaches usually are implemented jointly and iteratively in practice.…”

Section: Spectrum Of Lulc Modelsmentioning

confidence: 99%

Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects

Ren

Comber

et al. 2019

Earth-Science Reviews

125

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Section: Spectrum Of Lulc Modelsmentioning

confidence: 99%

Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects

Ren

Comber

et al. 2019

Earth-Science Reviews

125

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“…The impact of changes in land cover over historical periods should be included in the process of building models that accurately simulate global environmental change [7]. Moreover, land-use history is very important for assessing present landscape conditions, because land-use legacies influence climatic and ecological processes that occur today [8,9]. This often requires the reconstruction of land-use/cover history over the time slice.…”

Section: Introductionmentioning

confidence: 99%

Historical Arable Land Change in an Eco-Fragile Area: A Case Study in Zhenlai County, Northeastern China

Yang

Zhang

2018

Sustainability

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Long-term land changes are cumulatively a major driver of global environmental change. Historical land-cover/use change is important for assessing present landscape conditions and researching ecological environment issues, especially in eco-fragile areas. Arable land is one of the land types influenced by human agricultural activity, reflecting human effects on land-use and land-cover change. This paper selected Zhenlai County, which is part of the farming–pastoral zone of northern China, as the research region. As agricultural land transformation goes with the establishment of settlements, in this research, the historical progress of land transformation in agricultural areas was analyzed from the perspective of settlement evolution, and the historical reconstruction of arable land was established using settlement as the proxy between their inner relationships, which could be reflected by the farming radius. The results show the following. (1) There was little land transformation from nonagricultural areas into agricultural areas until the Qing government lifted the ban on cultivation and mass migration accelerated the process, which was most significant during 1907–1912; (2) The overall trend of land transformation in this region is from northeast to southwest; (3) Taking the topographic maps as references, the spatial distribution of the reconstructed arable land accounts for 47.79% of the maps. When this proxy-based reconstruction method is applied to other regions, its limitations should be noticed. It is important to explore the research of farming radius calculations based on regional characteristics. To achieve land-system sustainability, long-term historical land change trajectories and characteristics should be applied to future policy making.

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“…Spatially explicit LULC models analyses the relationship between the LULC changes and the drivers causing the change in the past, and predict the scenarios of changes in future (Mas & Sandoval 2011). The performance and evaluation of a LULC model are directly determined by the quantity and quality of input variables (Chang-Martínez et al 2015). Thus, the selection of appropriate driver variables for analysing LULC change for a forest is important.…”

Section: Study Areamentioning

confidence: 99%

“…Spatial modelling using different techniques at different scales has become an important tool to better understand the causes of land-use change/deforestation (Jat et al, 2008). Land-use/landcover (LULC) modelling helps understand current changes and their probable consequences on the surrounding environment (Chang-Martínez et al 2015). Such modelling of LULC change, especially forest change, is becoming essential to adopt appropriate conservation and management planning of natural resources.…”

Section: Progress In Physical Geography Sage Introductionmentioning

confidence: 99%

“…FORTRAN) to simulate land dynamics using spatially distributed data (Hall, Hall & Taylor 2000). A variety of software, methods and approaches are used for contemporary models (Chang-Martínez et al 2015) (Verburg & Overmars 2009) and InVEST (Olwero 2013). All the spatial models predict deforestation and forest degradation risk as a continuous or discrete (probability) map based on the expected rate of deforestation.…”

Section: Progress In Physical Geography Sage Introductionmentioning

confidence: 99%

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Mapping forest dynamics in Bangladesh from satellite images: implications for the global REDD+ program

Redowan¹

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Reducing emissions from deforestation and forest degradation (REDD+) is a global climate change mitigation initiative under negotiation by the United Nations Framework Convention on Climate Change (UNFCCC). This initiative provides financial incentives to developing countries for enhancing carbon stocks in their forests by abstaining from deforestation and forest degradation, which would lead to emission of CO2 into the atmosphere. To implement a REDD+ project, developing countries need to measure and monitor anthropogenic changes in their forests and as well as forest carbon to account for CO2 emissions and removals from such changes. It is also important to predict future scenarios of forest/landuse changes and to identify the areas at risk of changes so that appropriate conservation initiatives can be taken. Bangladesh is steadily progressing through its REDD+ roadmap. However, several key research issues still to address include: using remote sensing technology to detect deforestation, forest degradation and associated changes in forest carbon stock, and predict future forest/land-use pattern at local environmental setting. This study developed and evaluated approaches to detect the extent of historic (1995-2015) deforestation and forest degradation (Objective 1), to estimate the emissions of forest aboveground carbon due to deforestation and degradation activities (Objective 2), and to assess the future scenarios, driving forces and risk of deforestation and forest degradation (Objective 3) at Raghunandan Hill Reserve in northeastern Bangladesh using a combination of satellite and field-level data employing various geospatial, statistical and modeling tools. The thesis comprises seven chapters. The significance and background of the research along with aim, objectives, research questions and available literature on the topic were described in chapter 1, 2 and 3. Chapter 4 dealt with the first objective, where changes of forest areas to non-forest areas and one forest strata to another during 1995-2015 were detected with high accuracy (>90%) by applying Monte-Carlo spectral unmixing algorithm to Landsat images, followed by knowledge-based classification approach. The classification was verified using independent randomly drawn reference sample points using high-resolution Google Earth images. A post-classification comparison method was applied to quantify the spatial extent, location and rate of changes of forest classes by generating transition matrices. In chapter 5, emissions of carbon (t CO2e yr-1) from deforestation and forest degradation activities during 1995-2005 and 2005-2015 periods at Raghunandan forest were estimated using Landsat satellite and field-level biomass-carbon data applying regression analysis and geospatial techniques with acceptable accuracy. The accuracy of the estimate was verified using root mean square error iii (RMSE) of estimated carbon with field reference carbon. Results indicated that, during 1995-2005, the total estimated emission was 4589 tonnes (t) CO2e yr-1 from deforesta...

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Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Cited by 36 publications

References 83 publications

Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects

Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects

Historical Arable Land Change in an Eco-Fragile Area: A Case Study in Zhenlai County, Northeastern China

Mapping forest dynamics in Bangladesh from satellite images: implications for the global REDD+ program

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