Resources
Displaying 736 - 740 of 2258Spatial Agglomeration Pattern and Driving Factors of Grain Production in China since the Reform and Opening Up
Since the reform and opening up, regional imbalances in the development of market economy and urbanization have significantly changed the spatial agglomeration pattern of grain production (GP) in China.
Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt
Regional land use transitions driven by the adaptive reconciliation of existing land use conflict with socioeconomic development can lead to positive economic effects as well as new land use conflict. Although research on land use transition has progressed considerably, limited studies have explored the spatiotemporal dynamic pattern of land use conflict during the land use transition period. Previous evaluation approaches on land use conflict that mainly focus on status or potential conflict lack conflict intensity evaluation during the land use transition process.
The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios
Maytenus senegalensis subsp. europaea is a shrub belonging to the Celastraceae family, whose only European populations are distributed discontinuously along the south-eastern coast of the Iberian Peninsula, forming plant communities with great ecological value, unique in Europe.
The Legal Element of Fixing the Boundary for Indonesian Complete Cadastre
In 2017, the Indonesian government implemented the systematic land registration (PTSL) process, projected to be finished by 2025. However, this process faces some challenges in the spatial and legal data collection process, resulting in the Indonesian cadastral system still being incomplete. For instance, during the three years of its implementation, out of about 135 million parcels, only 49.5% have been registered. Therefore, the level of completeness needs to be improved.
Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability
Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM).