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Land-use/land-cover changes （LUCCs） have links to both human and nature inter- actions. China＇s Land-Use/cover Datasets （CLUDs） were updated regularly at 5-year inter- vals from the late 1980s to 2010, with standard procedures based on Landsat TM／ETM＋ im- ages. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics, differences, and causes of land-use changes at a national scale were then examined. The main findings are summarized as fol- lows. Land-use changes （LUCs） across China indicated a significant variation in spatial and temporal characteristics in the last 20 years （1990-2010）. The area of cropland change de- creased in the south and increased in the north, but the total area remained almost un- changed. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly, were mainly distributed in the east, and gradually spread out to central and western China. Woodland decreased first, and then increased, but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included （1） an accelerated expansion of built-up land in the Huang-Huai-Hai region, the southeastern coastal areas, the midstream area of the Yangtze River, and the Sichuan Basin; （2） shifted land reclamation in the north from northeast China and eastern Inner Mongolia to the oasis agricultural areas in northwest China; （3） continuous transformation from rain-fed farmlands in northeast China to paddy fields; and （4） effective- ness of the ＂Grain for Green＂ project in the southern agricultural-pastoral ecotones of Inner Mongolia, the Loess Plateau, and southwestern mountainous areas. In the last two decades, although climate change in the north affected the change in cropland, policy regulation and economic driving forces were still the primary causes of LUC across China. During the first decade of the 21st century, the anthropogenic factors that drove variations in land-use pat- terns have shifted the emphasis from one-way land development to both development and conservation. The ＂dynamic regionalization method＂ was used to analyze changes in the spatial patterns of zoning boundaries, the internal characteristics of zones, and the growth and decrease of units. The results revealed ＂the pattern of the change process,＂ namely the process of LUC and regional differences in characteristics at different stages. The growth and decrease of zones during this dynamic LUC zoning, variations in unit boundaries, and the characteristics of change intensities between the former and latter decades were examined. The patterns of alternative transformation between the ＂pattern＂ and ＂process＂ of land use and the causes for changes in different types and different regions of land use were explored.

Land use and land cover（LULC） in Nepal has undergone constant change over the past few decades due to major changes caused by anthropogenic and natural factors and their impacts on the national and regional environment and climate.This comprehensive review of past and present studies of land use and land cover change（LUCC） in Nepal concentrates on cropland, grassland, forest, snow/glacier cover and urban areas. While most small area studies have gathered data from different sources and research over a short period, across large areas most historical studies have been based on aerial photographs such as the Land Resource Mapping Project in 1986. The recent trend in studies in Nepal is to focus on new concepts and techniques to analyze LULC status on the basis of satellite imagery, with the help of geographic information system and remote sensing tools. Studies based on historical documents, and historical and recent spatial data on LULC, have clearly shown an increase in cropland areas in Nepal,and present results indicating different rates and magnitudes. A decrease in forest and snow/glacier coverage is reported in most studies. Little information is available on grassland and urban areas from past research. The unprecedented rate of urbanization in Nepal has led to significant urban land changes over the past 30 years. Meanwhile, long term historical LUCC research in Nepal is required for extensive work on spatially explicit reconstructions on the basis of historical and primary data collection, including LULC archives and drivers for future change.

Temporal trajectories of land-cover change provide important information on landscape dynamics that are critical to our understanding of complex human-environment adaptive systems. The increasing availability of long time series of satellite images, especially the recent free release of multi-decadal Landsat satellite archive, presents a great opportunity to improve our ability to detect land-cover change over multiple dates and advance land change science. In this article, a spatial-temporal modeling approach is developed for reconstructing land-cover change trajectories from time series of satellite images. The change detection method represents an enhancement to the conventional post-classification comparison. The key innovation lies in the use of Markov random field theory to model spatial-temporal contextual information explicitly in the classification of time series images. When evaluated using a time series of seven Landsat images in a case study of southeast Ohio, the spatial-temporal modeling approach yielded significantly more accurate and consistent trajectories of land-cover change than conventional non-contextual approaches. The results from the case study demonstrate the effectiveness of the change detection method in reconstructing land-cover change trajectories and also highlight the utility of spatial-temporal contextual information in improving the accuracy and consistency of land-cover classifications across space and time.

Land use and land cover change （LUCC） is one of the important human forcing on climate. However, it is difficult to infer how LUCC will affect climate in the future from the effects of previous LUCC on regional climates in the past. Thus, based on the land cover data recommended by the Coupled Model Intercomparison Project Phase 5 （CMIP5）, a regional climate model （RegCM4） was used to investigate the climate effects of future land use change over China. Two 15-year simulations （2036-2050）, one with the current land use data and the other with future land cover scenario （2050） were conducted. It is noted that future LUCC in China is mainly characterized by the transition from the grassland to the forest. Results suggest that the magnitudes and ranges of the changes in temperature and precipitation caused by future LUCC show evident seasonality, which are more prominent in summer and autumn. Significant response of climate to future LUCC mainly happens in Northeast China, North China, the Hetao Area, Eastern Qinghai-Tibetan Plateau and South China. Further investigation shows that future LUCC can also produce significant impacts on the atmospheric circulation. LUCC results in abnormal southwesterly wind over extensive areas from the Indian peninsula to the coasts of the South China Sea and South China through the Bay of Bengal. Furthermore, Indian tropical southwest monsoons and South Sea southwest monsoons will both be strong, and the ab- normal water vapor convergence from the South China Sea and the Indian Ocean will result in more precipitation in South China.

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects： （1） Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. （2） Historical maps and pictures offer visual and spatial quantitative land-cover information. （3） Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. （4） Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. （5） Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.

The classic story of historical land-cover change in the United States suggests that agricultural clearing in the 1800s was followed by agricultural abandonment at the turn of the twentieth century and subsequent forest regrowth-often referred to as a forest transition. Most descriptions present statistical data from historical censuses and surveys to make this case. Here we show that the historical data on cropland and forest area change for the United States need to be interpreted with care. Some earlier studies have exaggerated the extent of cropland abandonment and forest regrowth by failing to account for changes in definitions of croplands over time and changes in political boundaries in the case of forests. We reexamined the historical data to find that cropland and forest area for the United States as a whole have not undergone large-scale abandonment and regrowth but rather stabilized around the mid-twentieth century. Moreover, we find that, consistent with local and regional case studies, croplands were indeed abandoned in the eastern portions of the continent accompanied by forest regrowth, but there was compensating cropland expansion and forest clearing in the west. Our study suggests the need to exercise caution when using historical data to understand land-cover change and for developing theories such as forest transition. [Supplemental material is available for this article. Go to the publisher's online edition of Annals of the Association of American Geographers for the following free supplemental resource: (1) a table of cropland harvested area for the states of the United States from 1879 to 2002.]

Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 landcover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeophysics, biogeochemistry, water cycle, and global climate.

River runoff is affected by many factors, including long-term effects such as climate change that alter rainfall-runoff relationships, and short-term effects related to human intervention（e.g., dam construction, land-use and land-cover change（LUCC））. Discharge from the Yellow River system has been modified in numerous ways over the past century, not only as a result of increased demands for water from agriculture and industry, but also due to hydrological disturbance from LUCC, climate change and the construction of dams. The combined effect of these disturbances may have led to water shortages. Considering that there has been little change in long-term precipitation, dramatic decreases in water discharge may be attributed mainly to human activities, such as water usage, water transportation and dam construction. LUCC may also affect water availability, but the relative contribution of LUCC to changing discharge is unclear. In this study, the impact of LUCC on natural discharge（not including anthropogenic usage） is quantified using an attribution approach based on satellite land cover and discharge data. A retention parameter is used to relate LUCC to changes in discharge. We find that LUCC is the primary factor, and more dominant than climate change, in driving the reduction in discharge during 1956–2012, especially from the mid-1980 s to the end-1990 s. The ratio of each land class to total basin area changed significantly over the study period. Forestland and cropland increased by about 0.58% and 1.41%, respectively, and unused land decreased by 1.16%. Together, these variations resulted in changes in the retention parameter, and runoff generation showed a significant decrease after the mid-1980 s. Our findings highlight the importance of LUCC to runoff generation at the basin scale, and improve our understanding of the influence of LUCC on basin-scale hydrology.

The ecosystems on the Tibetan Plateau (TP) are highly vulnerable to climate change, rising CO 2 concentration, and land-use and land-cover change (LULCC), but their contributions to changes in the gross primary productivity (GPP) of the TP are not clearly understood. In this study, the role of these three factors on the interannual variations (IAVs) and trends of the TP's GPP were investigated using 12 terrestrial biosphere models. The ensemble simulations showed that climate change can explain most of the changes in the GPP, while the direct effect of LULCC and rising CO 2 (mainly fertilization effect) contributed 10% and −14% to the mean GPP values, 37% and −20% to the IAV, and 52% and −24% to the GPP's trend, respectively. The LULCC showed higher contributions to the significant positive trend in the annual GPP of the TP. However, the results from different model simulations showed that considerable uncertainties were associated with the effects of LULCC on the GPP of the TP.

In a developing country like Ghana, the study of land use and land cover change（LULCC） based on satellite imageries still remains a challenge due to cost, resolution and availability with less skilled man power. Existing researches are skewed towards the southerly part of Ghana thereby leaving the Northern sectors uncovered. The maximum likelihood classification（MLC） algorithm was employed for the LULCC between 2000 and 2014 in Nadowli： an area characterized by an upsurge in mining in the Northern belt of Ghana. A spatial-social approach was utilized combining both satellite imagery and socio economic data. Land use transition matrix, land use integrated index/degree indices was used to depict the characters of the change. A semi structured interview, pair wise ranking and key informant interviews were used to correlate the socio economic impact of the different LULC. Overall changes in the landscape showed an increase in bare ground by 19.22%, open savannah by 16.8% whereas closed savanna decreased by 50%. Land use change matrix showed increasing trends of bare ground at the expense of vegetation. The integrated land use index highlighted the bare ground and built up areas rising with a decreasing closed vegetation woodlot. Large farm size are shrinking whiles majority of the people view mining as the main socio economic activity affecting the environment and the reduction in vegetation. This study therefore provides a strategic guide and a baseline data for land use policy actors in the Northern belt of Ghana. This will aid in developing models for future land use change implications in surrounding areas where mining is on the rise.