Search Results (8)

The Yellow River Delta region is not only a concentrated area of human activities in coastal zones, but also a zone strongly influenced by regional environmental changes, where land cover changes are significantly affected by natural factors. Current historical LUCC datasets overlook the importance of partitioning to obtain accurate information on the potential maximum distribution range, which may lead to uncertainties in climate and environmental predictions. This study aims to reconstruct historical cropland changes in the Yellow River Delta via a region-adapted allocation model, supporting improved LUCC data accuracy and related research. Based on historical river course, settlement, and cropland survey data, this study identifies natural factors using historical settlement density through correlation analysis. Subsequently, a reclamation suitability model conforming to regional characteristics was constructed, and it obtains the cropland changes in the Yellow River Delta Basin at a spatial resolution of 0.5′ × 0.5′ over five time periods since 1855. The research indicates the following: (1) Through the method of analyzing the correlation between historical settlement density and natural factors, it is found that elevation (−), soil pH (+), soil organic carbon density (−), and NDVI (+) are the primary natural factors influencing the distribution of farmland in the Yellow River Delta. (2) The amount of farmland in the Yellow River Delta increased initially and then decreased after 1885; the average reclamation rate increased from 5.65%, peaked at 23.46% in the early 20th century, and then fell back to 7.68%. Spatially, the reclamation area expanded from scattered local areas along the Yellow River towards the sea, with a distinct coastal distribution. (3) Evaluation through absolute difference analysis shows that, compared with the HYDE 3.2 data, our reconstruction reflects the impacts of coastal changes, river distribution, and regional policy history on the allocation results. Based on the findings of this study, relevant issues can be improved from two aspects: first, by correlating settlement density with natural factors to identify key regional natural factors, which can then be applied to the update of LUCC data in small spatial units and similar regions to enhance data accuracy; second, by referring to the historical laws of cropland reclamation and suitability conditions, to optimize the current land planning of the Yellow River Delta and balance cropland utilization with ecological protection. Full article

Investigating the ecological and environmental impacts stemming from historical land use and land cover change (LUCC) holds paramount importance in systematically comprehending the fundamental human-land relationship, a pivotal focus within geographical research. The Yellow River Basin (YRB), often referred to as the cradle of Chinese civilization, ranks as the fifth-largest river basin globally. Early inhabitants made significant alterations to the landscape, resulting in substantial damage to natural vegetation, giving rise to prominent regional ecological challenges. By now, the examination of historical LUCC in the YRB over the past millennium remains in the qualitative research stage, primarily due to the limited availability of high-confidence gridded historical LUCC data. This study aims to advance the current historical LUCC research in the YRB from primarily qualitative analysis to an exploration incorporating timing, positioning, and quantification. Based on reconstructed historical cropland, forest, and grassland grid data of 10 km × 10 km from 1000 AD to 2000 AD, the degree of cropland development and the depletion of forests and grasslands were calculated, respectively. Then, the kernel density method was employed for spatiotemporal analysis and interpretation of dynamic changes in land cover. Subsequently, a cartographic visualization depicting the migration trajectories of the land cover gravity centers was generated, allowing for an assessment of the distance and direction of the centroids’ movement of cropland, forest, and grassland. The results indicate that the cropland coverage in the YRB escalated from the initial 11.65% to 29.97%, while the forest and grassland coverage dropped from 63.36% to 44.49%. The distribution of cultivated land continually expanded outward from the southeast of the Loess Plateau and the southwest of the North China Plain. All three types of land cover experienced a westward shift in their gravity centers between 1000 and 2000 AD. Besides the population growth and technological advancements, the regime shifts induced by wars, along with land use policies in distinct periods, always served as the predominant factors influencing the conversion between different land covers. This research will present a paradigmatic regional case study contributing to the investigation of historical changes in land use and land cover. Additionally, it will offer historical perspectives beneficial for the advancement of China’s objectives in “Ecological Conservation and High-Quality Development of the Yellow River Basin”. Full article

As a crucial component of studies on land use and cover change (LUCC), the reconstruction of historical cropland cover is important for assessing human impact on the environment. This study collects cropland records of each county in Taiyuan City based on historical documents, agricultural statistics, and survey data such as the Gazetteers, Agriculture and Commercial Statistics Table and Datasets of Land and Resources of China. The cropland area at the county level from 1650 to 1980 is determined by revising, correcting, and extrapolating the obtained historical records. By assessing the driving physiogeographic factors for the distribution of cropland through GeoDetector, we establish a land suitability-based gridded allocation model. The cropland areas at the county level are allocated into 1 km × 1 km grid cells. Our results indicated the following. (1) The total cropland area increased since the Qing Dynasty, reaching its maximum value in 1937, after which it declined due to the impact of urbanization after 1937. (2) In terms of the spatial distribution patterns of cropland, from 1650 to 1980, the cropland was mainly distributed in the Fenhe River Valley Plain, and the cropland expanded from the center to the south after 1952. (3) Comparing the reconstruction results for 1980 with the 1 km resolution satellite-based cropland cover data, differences of most (95.77%) grids are between −20% and +20%, comparing the HYDE3.2 dataset with our results. The HYDE3.2 dataset is distinctly lower than our datasets, and the grids with large differences are mainly in the central and southern parts of the study area, especially in the Qing Dynasty. Our reconstruction could evaluate the accuracy of the global dataset when applied to regional areas and serve as base data in studying historical climate change. Full article

Spatially explicit reconstruction of historical land cover change is a prerequisite for a more comprehensive understanding of environmental changes. Anthropogenic activities have dramatically altered the land cover of the conterminous United States (CONUS), encroaching heavily on the primary vegetation. However, few datasets exist that depict the historical trajectory of forest and grassland cover changes in CONUS over the last millennium, and previous efforts have only focused on reconstructions for the last four centuries. By integrating remote sensing-derived land use/cover change (LUCC) data and potential vegetation data, we determined the potential extent of natural forest (PENF) and grassland (PENG) in CONUS. Based on a qualitative analysis of the trends and driving forces of forest and grassland changes, we devised a method of subtracting reconstructed historical cropland (1000–2000 AD) and built-up land (1850–2000 AD) from PENG and PENF to reconstruct a 5 min × 5 min grid dataset of forest and grassland cover at 13 time-points over the past millennium. The results showed that forest and grassland cover in CONUS underwent a slow decline (1000–1600 AD), an accelerated decline (1600–1800 AD), a dramatic decline (1800–1950 AD), and finally, a recovery (1950–2000 AD) over the study period. The modelled forest fraction decreased from 49% in 1000 AD to 33% in 2000 AD, representing a 32% area reduction, whereas the modelled grassland fraction decreased from 37% to 22%, representing a 42% area reduction. The reduction occurred primarily in the last 200 years, with forest and grassland reductions accounting for 86% and 97% of the total reduction over the millennium, respectively. Spatially, more than 80% of the land was originally covered by forests and grasslands, and the loss occurred mainly in the eastern CONUS and Great Plains over the past millennium. After the 1930s, farmland abandonment began in central and eastern CONUS, simultaneously with environmental protection laws. Federal government regeneration programs for forest and grassland resources and the Shelterbelt Project all contributed to a slowdown in forest and grassland decline and recovery in cover. Full article

Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R² = 0.857), followed by the data-fusion method (R² = 0.709) and curve-fitting method (R² = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies. Full article

A key focus in research on changes in historical land cover has been to improve existing gridded cropland allocation methods based on land suitability for cultivation to generate credible historical cropland cover data. This study developed a settlement-density-based method for gridded cropland allocation using the locations of settlements to identify the cropland grid and the settlement density as the weight for allocating the cropland area to the grid. This method was applied to allocate the provincial cropland areas in Jilin Province, China, to a 5′ × 5′ cropland cover at six time points during the last 300 years. The credibility of the reconstruction was assessed using three methods. The following conclusions emerged. First, the settlement density method is funded on the fact of coexistence between rural settlements and cropland. Cropland is only distributed in the grid where the settlements exist, and the cropland area of a grid equals to the cropland area per settlement multiplying by the number of settlements within the grid, without considering differences of settlement size. Second, all three quantitative or qualitative assessments of Jilin Province confirmed the credibility and feasibility of the settlement density method. Therefore, the use of this method to reproduce the temporal and spatial changes in cropland cover in new reclamation regions, such as Jilin Province, is valid. This study provides valuable inputs for enhancing the credibility of historical global land cover data by incorporating human factors into the cropland allocation method. Full article

Wide-coverage spatial information on irrigated croplands is a vital foundation for food security and water resources studies at the regional level. Several global irrigated-cropland maps have been released to the public over the past decade due to the efforts of the remote sensing community. However, the consistency and discrepancy between these maps is largely unknown because of a lack of comparative studies, limiting their use and improvement. To close this knowledge gap, we compared the latest four irrigated-cropland datasets (GMIA, GRIPC, GlobCover, and GFSAD) in mainland China. First, the four maps were compared quantitatively and neutral regional- and provincial-level statistics of the relative proportions of irrigated land were obtained through regression analysis. Second, we compared the similarities and discrepancies of the datasets on spatial grids. Furthermore, the contributions of mosaic cropland pixels in GlobCover and GFSAD were also analyzed because of their extensive distribution and ambiguous content. Results showed that GMIA has the lowest dispersion and best statistical correlation followed by GRIPC, while the corresponding features of GlobCover and GFSAD are approximately equal. Spatial agreement of the four maps is higher in eastern than western China, and disagreement is contributed mostly by GlobCover and GFSAD. However, divergence exists in the ratios of the different agreement levels, as well as their sources, on a regional scale. Mosaic pixels provide more than half of the irrigated areas for GlobCover and GFSAD, and they include both correct and incorrect information. Our results indicate a need for a uniform quantitative classification system and for greater focus on heterogeneous regions. Furthermore, the results demonstrate the advantage of numerical restriction in the calculations. Therefore, special attention should be paid to integrating databases and to exploring remote sensing features and methods for spatial reconstruction and identification of untypical irrigation areas. Full article

Cropping intensity is an important indicator of the intensity of cropland use and plays a very important role in food security. In this study, we reconstructed a normalized difference vegetation index (NDVI) time-series from 1982 to 2012 using the Savitzky-Golay (S-G) technique and used it to derive a multiple cropping index (MCI) combined with land use data. Spatial–temporal patterns of variation in the MCI of northern China were as follows: (1) The MCI in northern China increased gradually from north-west to south-east; from 1982 to 2012, the mean cropping index across grid-cells over the study area increased by 4.36% per 10 years (p < 0.001) with fluctuations throughout the study period; (2) The mean MCI across grid-cells over the whole of northern China increased from 107% to 115% with all provinces showing an increasing trend throughout the 1980s and 1990s. Aside from Tianjin, Hebei, Beijing, and Shandong, all provinces also displayed an increasing trend between the 1990s and 2000s. Arable slope played an important role in the variation of the MCI; regions with slope ≤3° and the regions with slope >3° were characterized by inverse temporal MCI trends; (3) Drivers of change in the MCI were diverse and varied across different spatial and temporal scales; the MCI was affected by the changing agricultural population, deployment of food policies, and methods introduced for maximizing farmer benefits. For the protection of national food security, measures are needed to improve the MCI. However, more attention should also be given to the negative impacts that these measures may have on agricultural sustainability, such as soil pollution by chemical fertilizers and pesticides. Full article