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Numerous geographic data on peatland exist but definitions vary, and the correspondent classes are often neither harmonized nor interoperable. This hinders the efforts to employ the available national datasets on peatlands and wetlands for policy monitoring and reporting. The existing meta-languages, such as ISO-Land Cover Meta Language (LCML) and EAGLE, offer the possibility to "deconstruct" the relevant nomenclatures in an object-oriented manner, allowing the comparability and interoperable use of related information. The complex nature of peatlands calls for a dedicated and structured vocabulary of keywords and terms, comprising the biotic substrate and the soil. In the SEPLA project, a semantic meta-model has been developed, combining the hierarchical ontology of the LCML with the matrix structure of the EAGLE model. The necessary elements were provided to describe peatland bio-physical characteristics, while representing the definitions in a concise and user-friendly manner (semantic passports). The proposed semantic meta-model is innovative as it enables the documentation of the spatial distribution of peatland characteristics, considering also their temporal dimension, their intrinsic relation with land use, and the soil. It has been successfully implemented for the translation of the national peatland nomenclature into common land categories relevant for reporting under LULUCF regulation, as part of the EU Climate Law. [ABSTRACT FROM AUTHOR]

Numerous geographic data on peatland exist but definitions vary, and the correspondent classes are often neither harmonized nor interoperable. This hinders the efforts to employ the available national datasets on peatlands and wetlands for policy monitoring and reporting. The existing meta-languages, such as ISO-Land Cover Meta Language (LCML) and EAGLE, offer the possibility to “deconstruct” the relevant nomenclatures in an object-oriented manner, allowing the comparability and interoperable use of related information. The complex nature of peatlands calls for a dedicated and structured vocabulary of keywords and terms, comprising the biotic substrate and the soil. In the SEPLA project, a semantic meta-model has been developed, combining the hierarchical ontology of the LCML with the matrix structure of the EAGLE model. The necessary elements were provided to describe peatland bio-physical characteristics, while representing the definitions in a concise and user-friendly manner (semantic passports). The proposed semantic meta-model is innovative as it enables the documentation of the spatial distribution of peatland characteristics, considering also their temporal dimension, their intrinsic relation with land use, and the soil. It has been successfully implemented for the translation of the national peatland nomenclature into common land categories relevant for reporting under LULUCF regulation, as part of the EU Climate Law.

Land use/cover change (LUCC) research is important for regional ecological conservation and sustainable development. There is a lack of exploration of long-time-series dynamics and driving mechanisms at the national scale in the study of land use/cover change in Thailand. Based on the Global Land Cover product with Fine Classification System in 30m (GLC_FCS30) dataset and socioeconomic statistics, we studied the temporal and spatial evolution characteristics and driving mechanisms of LUCC in Thailand from 2000 to 2020 through indicators and methods such as land use dynamic attitude, mapping of a Sankey diagram, principal component analysis, and multiple linear stepwise regression analysis. The results showed that: (1) Thailand has developed in terms of agriculture and forestry. In 2020, the cropland and forest areas accounted for 53.77% and 32.15% of the land area, respectively. (2) From 2000 to 2020, the area of rainfed cropland, irrigated cropland, and forest continued to shrink; the area of impervious surfaces expanded rapidly, and the area of shrubland, other cropland, and wetlands increased. (3) The LUCC process mainly occurred in the two-way conversion between forest and shrubland, rainfed cropland and irrigated farmland, forest and rainfed cropland, and forest and other farmland. The LUC with the largest area transformed into other land types was forest (2.25 × 104 km2), and the LUC with the largest area transformed from other land types transferring into the area was shrubland (1.40 × 104 km2). (4) From 2000 to 2020, the LUCC process in Thailand was mainly influenced by socio-economics and tourism. Gross population, main grain output, industrial value added, passenger income, and urban population were the key factors driving the LUCC in Thailand. Our research can provide the basis and decision support for the future planning and management of land in Thailand. [ABSTRACT FROM AUTHOR]

Land use/cover (LUC) datasets are the basis of global change studies and cross-scale land planning. Data fusion is an important direction for correcting errors and improving the reliability of multisource LUC datasets. In this study, a new fusion method based on Bayesian fuzzy probability prediction was developed, and a case study was conducted in five countries of the Indochina Peninsula to form a fusion dataset with a resolution of 30 m in 2020 (BeyFusLUC30). After precision and uncertainty analysis, it was found that: (1) using accuracy validation information as prior knowledge and considering spatial relations can be well applied to LUC data fusion. (2) When compared to the four source datasets (LSV10, GLC_FCS30, ESRI10, and Globeland30), the accuracy indices of BeyFusLUC30 are all optimal. The average overall consistency increased by 6.42–13.61%, the overall accuracy increased by 4.84–7.11%, and the kappa coefficient increased by 4.98–7.60%. (3) The accuracy of the fusion result improved less for land types with good original accuracy (cropland, forest, water area, and built-up land), and the improved range of F1 score was at least 0.40–2.29%, and at most 6.66–9.88%. For the land types with poor original accuracy (grassland, shrubland, wetland, and bare land), the accuracy of the fusion result improved more, and the F1 score improved by at least 4.02–5.82%, and at most 14.41–48.35%. The LUC dataset fusion and quality improvement method developed in this study can be applied to other regions of the world as well. BeyFusLUC30 can provide reliable LUC data for scientific research and government applications in the peninsula. [ABSTRACT FROM AUTHOR]

Research on the spatial distribution and dynamic evolution of land use/cover (LUC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analyses of the evolution of LUC at the national scale in Cambodia. Based on the GLC_FCS30 dataset, this paper analyzes the temporal and spatial evolution of LUC in Cambodia from 2000 to 2020, as well as its relationship with the country's economic and social development and climate change using dynamic degree, flow direction diagram, principal component analysis, and multivariate stepwise regression. The main findings are as follows. (1) Cropland and forest are Cambodia's most significant land cover types. In 2020, the proportions of cropland and forest accounted for 47.67% and 42.22% of the total land area. Among the cropland area, rainfed cropland accounted for 35.90%, irrigated cropland accounted for 7.26%, and other cropland accounted for 4.51%. (2) From 2000 to 2020, rainfed cropland areas increased significantly (+4.69 × 103 km2, +7.77%), while irrigated cropland areas increased less (+0.37 × 103 km2, 2.91%). The forest area continued to shrink (−7.71 × 103 km2, −9.16%), and the area of impervious surfaces expanded most significantly (+161.16%). (3) In 2000–2020, the integrated dynamic degree of land use presented a spatial differentiation pattern of high in the northeast, low in the middle, and lowest in the plain area. The most critical LUC change processes were the conversion of forest and shrubland to rainfed cropland and the transformation of rainfed cropland to impervious surfaces. (4) The change in LUC in Cambodia has mainly been affected by economic development and human activities, especially regarding the agricultural added value, agricultural raw material export value, urban population, and urbanization rate. This study will help the Cambodian government to strengthen national land management and planning in a targeted manner and may provide a reference for the analysis of land use change processes in similar areas. [ABSTRACT FROM AUTHOR]

Land use/land cover change (LUCC) research is of great significance to land conservation and regional sustainable development. At present, there is a lack of research on the long-term timing of the change process and mechanisms of LUCC that accords with the national level in Laos. Based on the Global Land-Cover product with the Fine Classification System at 30 m (GLC_FCS30) data set as well as economic and social statistical data, the authors analyzed the spatiotemporal regularity and driving mechanism of LUCC in Laos from 2000 to 2020 by using dynamic degree, flow direction analysis, principal component analysis, correlation analysis and other methods. The results show that: (1) Laos is rich in natural ecological resources. In 2020, the forest and shrubland areas accounted for 53.3% and 32.4% of the land area, respectively; (2) from 2000 to 2020, the rate of LUCC across the country continued to rise, and the integrated dynamic degree of LUCC was 14.4%. The change in impervious surfaces is the most drastic. The area of evergreen broad-leaved forest, evergreen needle-leaved forest and grassland continued to shrink, while the area of rainfed cropland, irrigated cropland, deciduous broad-leaved forest, shrubland, wetland and the water body continued to expand; (3) the LUCC process mainly occurred between forest, shrubland and cropland. The LUC with the largest transfer out area is evergreen broad-leaved forest (8.91 × 103 km2), and the LUC with the largest transfer into the area is shrubland (8 × 103 km2); (4) in the past 20 years, the LUCC process in Laos has been mainly affected by macro-socioeconomic development, agricultural development, and forestry development. The population is the key factor driving LUCC in Laos. This study can provide decision-making support for the rational planning and utilization of land resources in Laos. [ABSTRACT FROM AUTHOR]

Research on the spatial distribution and dynamic evolution of land use/land cover (LULC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analysis on the evolution of LULC on the national scale in Vietnam. Based on the GLC_FCS30 dataset, this paper analyzed the temporal and spatial evolution of LULC in Vietnam from 2000 to 2020 as well as its driving mechanism using methods such as dynamicity, flow direction diagrams, principal component analysis, and multivariate stepwise regression. The results show that: (1) cropland, forest, and shrubland are Vietnam's most important land-cover types. In 2020, the above three types of land area accounted for 34.77%, 32.36%, and 26.13% of the total land area, respectively. (2) From 2000 to 2020, the area of cropland and forest areas continued to shrink (−5.64%, −3.96%); the area of shrubland, water bodies, and other land areas expanded (+4.87%, +12.29%, +15.04%); and the area of impervious surfaces expansion was the most significant (+100.40%). (3) The integrated dynamic degree of LULC in Vietnam shows a spatial differentiation of high in the south, followed by the north, and lowest in the center. In the early period (2000–2010), the LULC rate of change in each region was rapid, while it gradually decreased in the later period (2010–2020). The most important LULC changes in Vietnam can be divided into two parts: (a) the mutual conversion of forest, cropland, and shrubland and (b) one-way conversion of cropland to impervious surfaces. (4) LULC changes in Vietnam are mainly affected by economic development and human activities, especially the GDP, population, and urbanization rate. There is no reliable statistical relationship between LULC and climatic factors. The results of this study contribute to the analysis of LULC processes in similar regions, and will also help the Vietnamese government strengthen national land management and planning in a targeted manner. [ABSTRACT FROM AUTHOR]

Open and high-temporal- and spatial-resolution global land use/land cover (LULC) mapping data form the foundation of global change research and cross-scale land management planning. However, the consistency and reliability of the use of multisource LULC datasets in specific regions need to be quantitatively assessed. In this study, we selected the Indochina Peninsula as the research area; considered four datasets: LSV10, GLC_FCS30, ESRI10, and Globeland30; and analyzed them from four dimensions: the similarity of composition type, the degree of category confusion, spatial consistency, and data accuracy. The results show that: (1) the land composition descriptions of the different datasets are consistent. The study area is dominated by forest and cropland, supplemented by grassland, shrubland, and other land types. (2) The correlation coefficient between datasets is between 0.905 and 0.972; the spatial consistency of datasets is good; and the high-consistency area accounts for 77.87% of the total. (3) The overall accuracy of LSV10 is the highest (83.25%), and that of GLC_FCS30 is the lowest (72.27%). The accuracy of cropland, forest, water area, and built-up land is generally high (above 85%); the accuracy of grassland, shrubland, and bare land is low (below 60%). Therefore, researchers must conduct validation for specific regions and specific land types before using the above datasets. Our findings provide a basis for selecting LULC datasets in related research on the Indochina Peninsula and a reference method for assessing the reliability of multisource LULC datasets in other regions. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) research is important for regional ecological conservation and sustainable development. There is a lack of exploration of long-time-series dynamics and driving mechanisms at the national scale in the study of land use/cover change in Thailand. Based on the Global Land Cover product with Fine Classification System in 30m (GLC_FCS30) dataset and socioeconomic statistics, we studied the temporal and spatial evolution characteristics and driving mechanisms of LUCC in Thailand from 2000 to 2020 through indicators and methods such as land use dynamic attitude, mapping of a Sankey diagram, principal component analysis, and multiple linear stepwise regression analysis. The results showed that: (1) Thailand has developed in terms of agriculture and forestry. In 2020, the cropland and forest areas accounted for 53.77% and 32.15% of the land area, respectively. (2) From 2000 to 2020, the area of rainfed cropland, irrigated cropland, and forest continued to shrink; the area of impervious surfaces expanded rapidly, and the area of shrubland, other cropland, and wetlands increased. (3) The LUCC process mainly occurred in the two-way conversion between forest and shrubland, rainfed cropland and irrigated farmland, forest and rainfed cropland, and forest and other farmland. The LUC with the largest area transformed into other land types was forest (2.25 × 104 km2), and the LUC with the largest area transformed from other land types transferring into the area was shrubland (1.40 × 104 km2). (4) From 2000 to 2020, the LUCC process in Thailand was mainly influenced by socio-economics and tourism. Gross population, main grain output, industrial value added, passenger income, and urban population were the key factors driving the LUCC in Thailand. Our research can provide the basis and decision support for the future planning and management of land in Thailand.

Research on the spatial distribution and dynamic evolution of land use/cover (LUC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analyses of the evolution of LUC at the national scale in Cambodia. Based on the GLC_FCS30 dataset, this paper analyzes the temporal and spatial evolution of LUC in Cambodia from 2000 to 2020, as well as its relationship with the country’s economic and social development and climate change using dynamic degree, flow direction diagram, principal component analysis, and multivariate stepwise regression. The main findings are as follows. (1) Cropland and forest are Cambodia’s most significant land cover types. In 2020, the proportions of cropland and forest accounted for 47.67% and 42.22% of the total land area. Among the cropland area, rainfed cropland accounted for 35.90%, irrigated cropland accounted for 7.26%, and other cropland accounted for 4.51%. (2) From 2000 to 2020, rainfed cropland areas increased significantly (+4.69 × 103 km2, +7.77%), while irrigated cropland areas increased less (+0.37 × 103 km2, 2.91%). The forest area continued to shrink (−7.71 × 103 km2, −9.16%), and the area of impervious surfaces expanded most significantly (+161.16%). (3) In 2000–2020, the integrated dynamic degree of land use presented a spatial differentiation pattern of high in the northeast, low in the middle, and lowest in the plain area. The most critical LUC change processes were the conversion of forest and shrubland to rainfed cropland and the transformation of rainfed cropland to impervious surfaces. (4) The change in LUC in Cambodia has mainly been affected by economic development and human activities, especially regarding the agricultural added value, agricultural raw material export value, urban population, and urbanization rate. This study will help the Cambodian government to strengthen national land management and planning in a targeted manner and may provide a reference for the analysis of land use change processes in similar areas.

Land use/land cover change (LUCC) research is of great significance to land conservation and regional sustainable development. At present, there is a lack of research on the long-term timing of the change process and mechanisms of LUCC that accords with the national level in Laos. Based on the Global Land-Cover product with the Fine Classification System at 30 m (GLC_FCS30) data set as well as economic and social statistical data, the authors analyzed the spatiotemporal regularity and driving mechanism of LUCC in Laos from 2000 to 2020 by using dynamic degree, flow direction analysis, principal component analysis, correlation analysis and other methods. The results show that: (1) Laos is rich in natural ecological resources. In 2020, the forest and shrubland areas accounted for 53.3% and 32.4% of the land area, respectively; (2) from 2000 to 2020, the rate of LUCC across the country continued to rise, and the integrated dynamic degree of LUCC was 14.4%. The change in impervious surfaces is the most drastic. The area of evergreen broad-leaved forest, evergreen needle-leaved forest and grassland continued to shrink, while the area of rainfed cropland, irrigated cropland, deciduous broad-leaved forest, shrubland, wetland and the water body continued to expand; (3) the LUCC process mainly occurred between forest, shrubland and cropland. The LUC with the largest transfer out area is evergreen broad-leaved forest (8.91 × 103 km2), and the LUC with the largest transfer into the area is shrubland (8 × 103 km2); (4) in the past 20 years, the LUCC process in Laos has been mainly affected by macro-socioeconomic development, agricultural development, and forestry development. The population is the key factor driving LUCC in Laos. This study can provide decision-making support for the rational planning and utilization of land resources in Laos.

Research on the spatial distribution and dynamic evolution of land use/land cover (LULC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analysis on the evolution of LULC on the national scale in Vietnam. Based on the GLC_FCS30 dataset, this paper analyzed the temporal and spatial evolution of LULC in Vietnam from 2000 to 2020 as well as its driving mechanism using methods such as dynamicity, flow direction diagrams, principal component analysis, and multivariate stepwise regression. The results show that: (1) cropland, forest, and shrubland are Vietnam’s most important land-cover types. In 2020, the above three types of land area accounted for 34.77%, 32.36%, and 26.13% of the total land area, respectively. (2) From 2000 to 2020, the area of cropland and forest areas continued to shrink (−5.64%, −3.96%); the area of shrubland, water bodies, and other land areas expanded (+4.87%, +12.29%, +15.04%); and the area of impervious surfaces expansion was the most significant (+100.40%). (3) The integrated dynamic degree of LULC in Vietnam shows a spatial differentiation of high in the south, followed by the north, and lowest in the center. In the early period (2000–2010), the LULC rate of change in each region was rapid, while it gradually decreased in the later period (2010–2020). The most important LULC changes in Vietnam can be divided into two parts: (a) the mutual conversion of forest, cropland, and shrubland and (b) one-way conversion of cropland to impervious surfaces. (4) LULC changes in Vietnam are mainly affected by economic development and human activities, especially the GDP, population, and urbanization rate. There is no reliable statistical relationship between LULC and climatic factors. The results of this study contribute to the analysis of LULC processes in similar regions, and will also help the Vietnamese government strengthen national land management and planning in a targeted manner.

Open and high-temporal- and spatial-resolution global land use/land cover (LULC) mapping data form the foundation of global change research and cross-scale land management planning. However, the consistency and reliability of the use of multisource LULC datasets in specific regions need to be quantitatively assessed. In this study, we selected the Indochina Peninsula as the research area; considered four datasets: LSV10, GLC_FCS30, ESRI10, and Globeland30; and analyzed them from four dimensions: the similarity of composition type, the degree of category confusion, spatial consistency, and data accuracy. The results show that: (1) the land composition descriptions of the different datasets are consistent. The study area is dominated by forest and cropland, supplemented by grassland, shrubland, and other land types. (2) The correlation coefficient between datasets is between 0.905 and 0.972; the spatial consistency of datasets is good; and the high-consistency area accounts for 77.87% of the total. (3) The overall accuracy of LSV10 is the highest (83.25%), and that of GLC_FCS30 is the lowest (72.27%). The accuracy of cropland, forest, water area, and built-up land is generally high (above 85%); the accuracy of grassland, shrubland, and bare land is low (below 60%). Therefore, researchers must conduct validation for specific regions and specific land types before using the above datasets. Our findings provide a basis for selecting LULC datasets in related research on the Indochina Peninsula and a reference method for assessing the reliability of multisource LULC datasets in other regions.

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Copyright of Agricultural Sciences / Zemès ukio Mokslai is the property of Lithuanian Academy of Sciences Publishers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)