Monitoring and projecting land use and land cover change at multiple spatial scales

2020 ◽  
pp. 68-79
Author(s):  
Isabel M.D. Rosa
2009 ◽  
Vol 38 (4) ◽  
pp. 1473-1482 ◽  
Author(s):  
William C. Floyd ◽  
Stephen H. Schoenholtz ◽  
Stephen M. Griffith ◽  
Parker J. Wigington ◽  
Jeffrey J. Steiner

2017 ◽  
Vol 8 (2) ◽  
pp. 369-386 ◽  
Author(s):  
Reinhard Prestele ◽  
Almut Arneth ◽  
Alberte Bondeau ◽  
Nathalie de Noblet-Ducoudré ◽  
Thomas A. M. Pugh ◽  
...  

Abstract. Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in terrestrial biosphere models (TBMs). The published results of these models are used in major assessments of processes and impacts of global environmental change, such as the reports of the Intergovernmental Panel on Climate Change (IPCC). Fully coupled models of climate, land use and biogeochemical cycles to explore land use–climate interactions across spatial scales are currently not available. Instead, information on land use is provided as exogenous data from the land-use change modules of integrated assessment models (IAMs) to TBMs. In this article, we discuss, based on literature review and illustrative analysis of empirical and modeled LULCC data, three major challenges of this current LULCC representation and their implications for land use–climate interaction studies: (I) provision of consistent, harmonized, land-use time series spanning from historical reconstructions to future projections while accounting for uncertainties associated with different land-use modeling approaches, (II) accounting for sub-grid processes and bidirectional changes (gross changes) across spatial scales, and (III) the allocation strategy of independent land-use data at the grid cell level in TBMs. We discuss the factors that hamper the development of improved land-use representation, which sufficiently accounts for uncertainties in the land-use modeling process. We propose that LULCC data-provider and user communities should engage in the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories, to improve the representation of land use in TBMs. Moreover, we suggest concentrating on the development of integrated modeling frameworks that may provide further understanding of possible land–climate–society feedbacks.


GeoJournal ◽  
2019 ◽  
Vol 85 (6) ◽  
pp. 1529-1543 ◽  
Author(s):  
Akhtar Alam ◽  
M. Sultan Bhat ◽  
M. Maheen

Abstract Land use and land cover (LULC) change has been one of the most immense and perceptible transformations of the earth’s surface. Evaluating LULC change at varied spatial scales is imperative in wide range of perspectives such as environmental conservation, resource management, land use planning, and sustainable development. This work aims to examine the land use and land cover changes in the Kashmir valley between the time periods from 1992–2001–2015 using a set of compatible moderate resolution Landsat satellite imageries. Supervised approach with maximum likelihood classifier was adopted for the classification and generation of LULC maps for the selected time periods. Results reveal that there have been substantial changes in the land use and cover during the chosen time periods. In general, three land use and land cover change patterns were observed in the study area: (1) consistent increase of the area under marshy, built-up, barren, plantation, and shrubs; (2) continuous decrease in agriculture and water; (3) decrease (1992–2001) and increase (2001–2015) in forest and pasture classes. In terms of the area under each LULC category, most significant changes have been observed in agriculture (−), plantation (+), built-up (+), and water (−); however, with reference to percent change within each class, the maximum variability was recorded in built-up (198.45%), plantation (87.98%), pasture (− 71%), water (− 48%) and agriculture (− 28.85%). The massive land transformation is largely driven by anthropogenic actions and has been mostly adverse in nature, giving rise to multiple environmental issues in the ecologically sensitive Kashmir valley.


2016 ◽  
Author(s):  
Reinhard Prestele ◽  
Almut Arneth ◽  
Alberte Bondeau ◽  
Nathalie de Noblet-Ducoudré ◽  
Thomas A. M. Pugh ◽  
...  

Abstract. Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in Dynamic Global Vegetation Models (DGVMs) and Earth System Models (ESMs), whose published results are used in major assessments of processes and impacts of global environmental change such as the reports of the Intergovernmental Panel on Climate Change (IPCC). In the absence of coupled models of climate, land use and biogeochemical cycles to explore land use – climate interactions across spatial scales, information on LULCC is currently provided as exogenous data from the land-use change modules of Integrated Assessment Models (IAMs) to ESMs and DGVMs, while data from dedicated land-use change models (LUCMs) are rarely considered. In this article, we discuss major uncertainties and existing shortcomings of current implementation strategies originating in both LULCC data-provider and LULCC data-user communities. We identify, based on literature review and the analysis of empirical and modeled LULCC data, three major challenges related to LULCC representation, which are currently not or insufficiently accounted for: (1) provision of consistent, harmonized LULCC time series spanning from historical reconstructions to future projections while accounting for uncertainties due to different land-use modeling approaches, (2) accounting for sub-grid processes and bi-directional changes (gross changes) across spatial scales and (3) the allocation strategy of LULCC at the grid cell level in ESMs and DGVMs. Based on these three challenges, we discuss the reasons that hamper the development of implementation strategies that sufficiently account for uncertainties in the land-use modeling process and conclude that both providers and users of LULCC data products often miss appropriate knowledge of the requirements and constraints of one another’s models, thus leading to large discrepancies between the representation of LULCC data and processes in both communities. We propose to focus future research on the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories.


2011 ◽  
Vol 13 (5) ◽  
pp. 695-700
Author(s):  
Zhihua TANG ◽  
Xianlong ZHU ◽  
Cheng LI

2021 ◽  
Vol 10 (5) ◽  
pp. 325
Author(s):  
Ima Ituen ◽  
Baoxin Hu

Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing methods sometimes pose a barrier to the effective monitoring of changes in land cover and land use, since a threshold parameter is often needed and determined based on trial and error. This study aimed to develop an automatic and operational method for change detection on a large scale from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Super pixels were the basic unit of analysis instead of traditional individual pixels. T2 tests based on the feature vectors of temporal Normalized Difference Vegetation Index (NDVI) and land surface temperature were used for change detection. The developed method was applied to data over a predominantly vegetated area in northern Ontario, Canada spanning 120,000 sq. km from 2001–2016. The accuracies ranged between 78% and 88% for the NDVI-based test, from 74% to 86% for the LST-based test, and from 70% to 86% for the joint method compared with manual interpretation. Our proposed method for detecting land cover change provides a functional and viable alternative to existing methods of land cover change detection as it is reliable, repeatable, and free from uncertainty in establishing a threshold for change.


2021 ◽  
Vol 125 ◽  
pp. 107447 ◽  
Author(s):  
Rehana Rasool ◽  
Abida Fayaz ◽  
Mifta ul Shafiq ◽  
Harmeet Singh ◽  
Pervez Ahmed

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