scholarly journals Global Transition Rules for Translating Land-use Change (LUH2) To Land-cover Change for CMIP6 using GLM2

2019 ◽  
Author(s):  
Lei Ma ◽  
George C. Hurtt ◽  
Louise P. Chini ◽  
Ritvik Sahajpal ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Human Impacts ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Transition Rule ◽  
The Relationship ◽  
Earth System Models

Abstract. Information on historical land-cover change is important for understanding human impacts on the environment. Over the last decade, global models have characterized historical land-use changes, but few have been able to relate these changes with corresponding changes in land-cover. Utilizing the latest global land-use change data, we make several assumptions about the relationship between land-use and land-cover change, and evaluate each scenario with remote sensing data to identify optimal fit. The resulting transition rule can guide the incorporation of land-cover information within earth system models.

scholarly journals Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

2020 ◽  
Vol 13 (7) ◽  
pp. 3203-3220 ◽  
Author(s):  
Lei Ma ◽  
George C. Hurtt ◽  
Louise P. Chini ◽  
Ritvik Sahajpal ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Forest Cover ◽  
Land Use Changes ◽  
Change Models ◽  
Translation Rule

Abstract. Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

scholarly journals An analysis of land use change and the workers’ perception towards changes from 2007 – 2017: A case in Nam Tu Liem District, Hanoi, Vietnam

2019 ◽  
Vol 35 (3) ◽  
Author(s):  
Pauline Violanda Hostalero ◽  
Nguyen Thi Ha
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Use Changes ◽  
The Self ◽  
The Other ◽  
Land Type ◽  
Bare Land ◽  
The Government

Land use change has been assessed widely using Remote Sensing (RS) and Geographic Information System (GIS) techniques. The analysis of land use change was done by detecting land cover change. A study about land cover change, along with the self-employed workers’ perception towards changes between 2007 and 2017 were carried out in Nam Tu Liem District, Hanoi, Vietnam. The result of the study shows that the built-up lands have increased and remained to be the dominant land cover types in 2017. The agriculture has been declining mainly due to conversion into built-up land. Other land type including water, bare land, and vegetation have shown slight changes throughout the years. Overall changes from 2007 to 2017 shown that built-up land gained the most and agriculture land lost the most. On the other hand, the perception study’s major findings indicate that about two-thirds (69%) of respondents are aware of changes. However, almost one-third (31%) are unaware of the said topic. There are several factors that may affect the awareness of self-employed workers which will be cursory discussed in the study. This study in Nam Tu Liem District is a first step to determine and understand the major driving factors and their impacts on the land use changes in the area. A detailed land use/cover change study and a larger population size for perception studies are recommended in order for the government to formulate policies to achieve sustainable development.      

scholarly journals Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Program ◽  
Land Use Land Cover ◽  
Irrigation Scheme ◽  
Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

scholarly journals Land Cover/ Land Use Change and Climate Change in Dhofar Governorate, Oman

2021 ◽  
pp. 41-47
Author(s):  
E. Ramadan ◽  
T. Al-Awadhi ◽  
Y. Charabi
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Use Changes ◽  
Spatial Differentiation ◽  
Study Results ◽  
Vegetation Activity ◽  
The Status ◽  
Landsat Satellite

The study of land cover/land use dynamics under climate change conditions is of great significance for improving sustainable ecological management. Understanding the relationships between land cover and land use changes and climate change is thus very important. Understanding the interactive and cumulative effects of climate and land-use changes are a priority for urban planners and policy makers. The present investigation is based on Landsat satellite imagery to explore changes in vegetation spatial distribution between the years from 2000 to2018 The methodology is focused on vegetation indexes tracking and algebraic overlay calculation to analyzed vegetation and their spatial differentiation, land cover change pattern, and the relationships between vegetation dynamics and land cover change in Dhofar Governorate. The study results have revealed that the vegetation vigor is lower in all years compared to 2000. The scene of 2010 shows the minimum vegetation vigor, overall. Besides, the investigation shows a statistical relationship between rainfall and the status of the health of vegetation. Monsoon rainfall has an impact of the growth of vegetation. Between 2012 and 2013, the vegetation activity shows a decreasing trend. The analysis diagnoses an area affected by the worst degree of aridity situated in the southeastern of Dhofar Mountains. Climate change is the main driving factor resulted from both human activities and rainfall fluctuation.

scholarly journals Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

2020 ◽  
Vol 12 (24) ◽  
pp. 4048
Author(s):  
Yrneh Ulloa-Torrealba ◽  
Reinhold Stahlmann ◽  
Martin Wegmann ◽  
Thomas Koellner
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Historical Maps ◽  
River Catchment ◽  
Main River ◽  
Object Based

The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

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