Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia.

In the recent few decades, Vietnam has experienced a considerable change in land use/land cover (LULC), especially forest land. However, there is not a comprehensive analysis of the dynamics and drivers at the nationwide spatial scale for a long-term period. In this research, we estimate the socioeconomic and biophysical drivers of forest changes at the commune scale. Utilizing our results from the Vietnam-wide annual LULC database available in the Japan Aerospace Exploration Agency (JAXA), we first computed the dynamic changes in forest land from 1990 to 2020. To decide the major drivers of the changes, we conduct a synthesis of case studies working on the analysis of the forest changes in Vietnam at various spatial levels. Subsequently, a machine learning technique was adopted to measure the drivers of the forest changes. Our results indicate that although the forest area has increased from 2005 to 2010, it has undergone a decrease over the full study period. There is a dramatic conversion between forest and agricultural land, especially in the North-West and Central Highlands. This conversion is mainly driven by agricultural expansion/shifting, topographic position index, accessibility/infrastructure, population growth/migration, and distance to systems such as irrigation, drainage, and mining/industry. The identification of the drivers in this study is likely to help enhance the accuracy of the land use/land cover change prediction. These findings provide coherent evidence-based information about the dynamics and drivers of forest changes at the nationwide spatial and decadal temporal scales and thus can support informing land policies in Vietnam.

The countries of South and Southeast Asia (SSEA) are home to a diverse array of large native herbivores, but the majority of these species are currently threatened with extinction. Ensuring the future survival of these species and the integrity of the ecological services they provide will require concerted management efforts, but these need to be built on a strong scientific foundation, which is currently lacking. In particular, there is an urgent need for research efforts to: (i) generate baseline data on the current status and distribution of large herbivores species in South and Southeast Asia, (ii) quantify vital rates of species and identify factors that regulate the population dynamics of different species across their ranges, (iii) understand the role of large herbivores in regulating community and ecosystem processes, and how their losses are likely to affect ecosystems, and (iv) characterize the ecological and socioeconomic drivers of human–herbivore conflicts to identify the most effective ways of reducing conflict and thereby sustain large herbivore populations across the landscape. The large herbivores of South and Southeast Asia also offer unlimited opportunities for addressing a diverse array of other basic, as well as applied, scientific questions ranging from evolution and behavior to disease dynamics and the responses of herbivore populations to changing climates. Besides establishing and sustaining research initiatives that will generate much-needed long-term scientific data on large herbivores, there is also an urgent need for greater cooperation between ecologists, sociologists, economists, politicians, land managers, and the public if we are to ensure the long-term survival of large herbivores in the region.

The Indo-Malaysian region is a hot spot of rapid land-cover and land-use change (LCLUC) with little consensus about the rates and magnitudes of such change. Here we use temporal convolutional neural networks (TempCNNs) to generate a spatiotemporally consistent LCLUC data set for nearly thirty-five years (1982–2015), validated against two reference data sets with over 80 percent accuracy, better than other LCLUC products for the region. Our results both confirm and complicate estimates from earlier work that relied on decadal, rather than interannual, changes in regional land cover. We find forests decrease in mainland and maritime Southeast Asia and increase in South China and South Asia. Consistent with geographic theory about the drivers of land-use change, we find cropland expansion is a driving force for deforestation in mainland Southeast Asia with savannas playing a superior role, suggesting widespread forest degradation in this region. In contrast to earlier work and theory, we find that South China’s increasing forest cover comes principally from savanna (rather than cropland) conversion. The explicit interannual LCLUC patterns, rates, and transitions identified in this study provide a valuable data source for studies of land-use theory, environmental and climate changes, and regional land-use policy evaluations.

Worldwide Land Use and Land Cover Change (LULCC) plays an important role in global climate change. To be able to describe and analyze possible LULCC in the near future, spatially explicit modeling approaches are necessary. In this paper, applications of a LULCC-model, CLUE (Conversion of Land Use and its Effects), are demonstrated. The model uses quantitative information on the main drivers of land use change at different spatial scales, derived from historical and actual land use patterns. For a base-resolution (usually in the order of several square kilometers), georeferenced data for the study areas were collected and stored, and aggregated to different resolutions to create a series of artificial spatial scales. The collected data contains information on biophysical and socioeconomic variables that were considered theoretically important land use drivers; the relative contribution at various spatial scales remained to be determined. By means of stratified, multi-scale, spatial statistical analyses, the relative importance of the independent drivers in explaining land use patterns were quantitatively determined for different spatial scales. The multi-scale information on land use drivers was implemented in the dynamic CLUE model that simulates LULCC, using time steps of one year. It consists of a demand and allocation module. The demand module estimates the demanded physical output from agricultural land use systems at the highest aggregation level considered. In a multi-scale iteration-algorithm, the allocation module calculates spatially explicit LULCC at the level of individual cells (for different resolutions), considering (changes in) national demands as well as site-specific values of biophysical and socio-economic drivers.

Bangladesh has undergone dramatic land use and land cover changes (LULCC) in recent years, but no quantitative analysis of LULCC drivers at the national scale exists so far. Here, we quantified the drivers of major LULCC in combination with biophysical and socioeconomic observations at the sub-district level. We used Landsat satellite data to interpret LULCC from 2000 to 2010 and employed a Global Surface Water Dataset to account for the influences of water seasonality. The results suggest that major LULCC in Bangladesh occur between agricultural land and waterbodies and between forest and shrubland. Exclusion of seasonal waterbodies can improve the accuracy of our LULCC results and driver analysis. Although the gross gain and loss of agricultural land are large on the local scale, the net change (gross gain minus gross loss) at a country scale is almost negligible. Climate dynamics and extreme events and changes in urban and rural households were driving the changes from forest to shrubland in the southeast region. The conversion from agricultural land to standing waterbodies in the southwest region was mainly driven by urban household dynamics, population growth, distance to cities and major roads, and precipitation dynamics. This study, which is the first effort accounting for water seasonality and quantifying biophysical and socioeconomic drivers of LULCC at the national scale, provides a perspective on overall LULCC and underlying drivers over a decadal time scale and national spatial scale and can serve as a scientific basis for developing land policies in Bangladesh.

ABSTRACTUnderstanding the nature and relative importance of various drivers of change is crucial for sustainable management of natural resources and in prioritizing management efforts, allocating limited resources, and understanding cumulative effects. For this article, we employed structured an expert judgments approach to identify, characterize, and assess the relative importance of the key biophysical and socioeconomic drivers of change within the Volta River Basin, West Africa. Precipitation variability, water availability, land use change, drought events, and population growth were perceived as most important, while biodiversity loss, social conflicts, pest and disease occurrence, urbanization, and pollution were viewed as less critical. A majority of these drivers were characterized as “slow” acting processes as compared to rapidly changing drivers. Intra- and interexpert groups agreement were found to be significant and convergent, indicating the reliability of the results. The implications of these results for sustainable water resources management and agricultural production are discussed.

Land Use/Land Cover Change (LUCC) is the core components of global change researches. It is significant for understanding regional ecological environment and LUCC mechanism of large scale to develop the study of LUCC of regional level. Nujiang River is the upper reaches of a big river in the South Asia--Salween River. Nujiang River is a typical mountainous river which is 3200 kilometer long and its basin area is 32.5 × 105 square kilometer. It locates in the core of "Three Parallel Rivers" World Natural Heritage. It is one of international biodiversity conservation center of the world, the ecological fragile zone and key ecological construction area, as well as a remote undeveloped area with high diversity ethnic. With the rapidly development of society and economy, the land use and land cover changed in a great degree. The function of ecosystem has being degraded in some areas which will not only impact on the ecological construction of local area, but also on the ecological safety of lower reaches -- Salween River. Therefore it is necessary to carry out the research of LUCC of Nujiang River. Based on the theory and methods of geo-information Tupu, the "Spatial Pattern" and "Change Process" of land use of middle reach in Nujiang River from 1974 to 2004 had been studied in quantification and integration, so as to provide a case study in local area and mesoscale in time. Supported by the remote sensing and GIS technology, LUCC Tupu of 1974-2004 had been built and the characteristics of LUCC have been analyzed quantificationally. The results showed that the built-up land (Included in this category are cities, towns, villages, strip developments along highways, transportation, power, and communications facilities, and areas such as those occupied by mills, shopping centers, industrial and commercial complexes, and institutions that may, in some instances, be isolated from urban areas), agriculture land, shrubbery land, meadow & grassland, difficultly/unused land increased from 1974 to 2004, the increased area of shrubbery land was the greatest, while the area of forest, artificial forest, waters, glacier and snow covered land decreased. The biggest decreased area was forest land. The biggest LUCC was the transformation from forest land to shrubbery land, the transformation from forest land to rangeland and agriculture land was the second. The main area of LUCC located at Nujiang River valley, between 2200-3700m of the east slope in the Gaoligong Mountain and 2800-3900m of the west slope of the Biluo Snow Mountain. From the valley to peak of mountain, the main land use type was transited from built-up land, agricultures land, artificial forest land to natural forest, shrubbery and grass land. The natural forest was the main land in the past 30 years. The main driving forces were the increase of population of local area, the governmental policies (Conversion of Farmland to Forests and Grass Land Projects, etc.) and urbanization. In order to accelerate the sustainable development of society economy and the ecological environment protection in this ecological fragile zone, strict management should be adopted to adjust the behaviors of human beings. Finally, VCM (variable clumping method) curve had been used to analyses the internal spatial distribution difference of land-use/land cover which shown that the landscape fragmentation was increased, the number of patches was added, the distance between patches was diminished during the past thirty years (1974-2004).

Long-term trends and spatial patterns of PM2.5-induced premature mortality in South and Southeast Asia from 1999 to 2014

Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014

Understanding a species’ foraging habits and preferences is fundamental to understanding its overall ecology and essential for its management and conservation. In general, large herbivores are classified as either grazers, browsers , or mixed feeders , and a species’ diet preference is related to its body mass and digestive trait syndrome. Here, we analyze feeding strategies of large herbivores in South and Southeast Asia (SSEA) as related to their body mass and digestive trait syndromes. Overall, our results are similar to patterns observed on other continents. The majority of large herbivore species in SSEA are mixed feeders. Browsers and frugivores dominate the smallest body mass classes, while bulk feeders , predominantly grazers, dominate the largest body mass classes. There is an absence of hindgut fermenters in the lower body mass classes, and an absence of ruminants in the megaherbivore class (>1000 kg). Cervids in SSEA do not get as large as Bovids, and in both Cervids and Bovids the greatest number of species are found in the smaller body mass classes. Although large herbivores in SSEA occur across a wide range of different habitat types, there are discernible habitat associations with different groups of species. While this chapter sheds light on this important facet of large herbivore ecology in the region, there remains an acute lack of data on the foraging ecology of the majority of species in SSEA.

Land use/land cover (LULC) changes in the watershed (2,157 km2) of Lake Kasumigaura during 1979–1996 (Period-1: 1979–1990, Period-2: 1990–1996) were analyzed, and their socio-economic and biophysical drivers were compared using time-series, high-quality GIS datasets in order to examine the characteristics of a model forecasting the future LULC. The changes occurred over an area of more than 90 km2 during the overall period at changing rates of 0.22% year−1 in Period-1 and 0.25% year−1 in Period-2. Forestland decreased most in both periods at changing rates of 0.45% year−1 in Period-1 and 0.61% year−1 in Period-2. However, predominant changing patterns differed, i.e., from forest to golf course in Period-1 and from forest to artificial field in Period-2. Particularly in Period-2, a significant LULC change was observed in an area of high population increase on the edge of an already high-population area. Relationships examined among LULC change, population, and rate of population change suggested that the urbanized area was highly resistant to LULC change, and that such change was less frequent in areas of population decline. Statistical analyses indicated that the most influential drivers for total LULC changes were population in Period-1 and distance from the Tokyo Station in Period-2. Since the change potentials differed between the periods, we could not assume a stationary process for the corresponding drivers. Somewhat low S values (indices for demonstrability) show that LULC changes in the watershed of Lake Kasumigaura occurred rather randomly, probably resulting in fragmentation of the landscape.

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

<strong class="journal-contentHeaderColor">Abstract.</strong> Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1&deg; resolution for Europe with annual LULC maps from 1950&ndash;2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by &nbsp;Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25&deg; resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

<strong class="journal-contentHeaderColor">Abstract.</strong> Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1&deg; resolution for Europe with annual LULC maps from 1950&ndash;2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by &nbsp;Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25&deg; resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

Human activity have caused significant increase in greenhouse gas (GHG) concentration and land use and land cover change (LULCC), which can in turn affect regional and global climate. We investigate the relative contributions of increased GHG concentration and LULCC to the precipitation and surface air temperature (SAT) in Asia through three dynamical downscaling simulations with different land cover maps and GHG concentrations. The results suggest that the increased GHG concentration leads to an increase (decrease) in precipitation over East Asia and Southeast Asia (South Asia) in spring and winter. Over Southeast Asia and the Bay of Bengal, LULCC induced a significant decrease in precipitation in autumn and winter, and the change is greater than that induced by GHG. Moreover, the enhanced GHG concentration generally leads to a decrease in dry days or light rain days and an increase in heavy rain days over the land area. However, LULCC‐induced changes in extreme precipitation are generally weak. The increased GHG concentration induces a significant increase of 1–3°C in the SAT throughout the year. LULCC plays a more important role than the increased GHG concentration in modulating the SAT over South Asia, Southeast Asia, and East Asia throughout the year. The increased GHG concentration and LULCC cause a broader range of changes in the daily temperature frequency. The anomalous warm event of 8–10°C increases by 1–1.5 days per season in the middle latitude regions in boreal spring due to the increased GHG concentration. LULCC also leads to an increase in high‐temperature events except central Asia.