The development of cities is influenced by geographical and geological conditions. Taking Jiangxi in China for example, this paper compared the geographical conditions of the urban areas between 1984 and 2020, and predicted the areas suitable for future urbanization in the study area through Random Forest (RF) modeling. By identifying the changes of the average elevation, and slope of cities, we understand that cities tend to expand toward the plains but in the valleys and basins, cities are also forced to develop toward high elevation and high slope belts.
The built-up areas of 194 cities and towns (> 10 000 in population) in 2020 were taken as positive samples, and the buffer ranges of 400 non-urban random points generated in areas where slope > 10° and distance to river > 10 km as negative samples. Positive and negative samples were divided into a training set and a validation set in a ratio of 7:3 by random selection, and 300 classification trees were set within the RF model to classify the whole study area. The results were verified through the validation set and an overall accuracy (OA) of 94.88% and a Kappa Coefficient (KC) of 89.03% were obtained. This means that the prediction of the optimal urbanization candidate areas seems reliable. These areas are distributed in basins and valleys around the Poyang Lake, along the rivers Yangtze, Ganjiang, Xinjiang, Yuanhe, Jinjiang and Fuhe, and those along the deep and large fault zones are the most suitable ones for urban development.
In view of the insensitivity of the available radar vegetation indices to dryland ecosystems, it is the purpose of this paper to develop a more pertinent new radar vegetation index, i.e., dryland radar vegetation index (DRVI), for land characterization using Sentinel 1 data based on the scattering entropy, scattering angle and cross polarization. After a comparison with the available radar indices and sensitivity analysis versus (vs) the optical remote sensing indices, the new index, DRVI, was found to have a stronger correlation with the optical vegetation indices such as the normalized difference vegetation index (NDVI), generalized difference vegetation index (GDVI), and mining and restoration assessment indices (MRAIs) and a higher sensitivity to dryland biomes than other radar vegetation indices. It is hence concluded that this DRVI has high potential of application in characterizing the dryland ecosystems despite it is derived from the Mu Us Sandy Land, China.
Land use change detection by remote sensing (RS) and simulation-based prediction may provide support to rational spatial planning of territory, which is of great significance to ensure sustainable land management. For this purpose, this paper employed multitemporal Landsat images dated 2010, 2015 and 2020 and county-level socioeconomic data for achieving such an integrated study taking Hefei city as an example. After atmospheric correction using the COST (Cosine Theta) model, the maximum likelihood approach and post-classification differencing were applied respectively to classify the images and to reveal the land use changes. The Logistic-Multi-Criteria Evaluation-Celluar Automata-Markov (LMCM) hybrid model was harnessed to simulate the land use pattern of 2025 under four different scenarios namely business as usual (BAU), ecological protection (EP), economic development (ED) and sustainable development (SD). The results show that (1) during the period 2010-2020, the built-up areas increased by 804.69 km2 and the croplands decreased by 568.14 km2; (2) There seems to be a big difference between the predicted croplands and built-up areas in the four scenarios in 2025. Under the EP scenario, the built-up areas may likely gain an increase of 244.27 km2, indicating that the environment shall be protected but the urbanization will be also limited. Under the ED scenario, the built-up areas may be extended by 829.54 km2 and the croplands and forests are likely to decline significantly in 2025, meaning that overemphasizing urban development would not only threaten food security, but also bring harm to the local ecosystem. Under the SD scenario, the increase of built-up areas and the reduction of croplands would both come between the other scenarios, and the land use structure seems more stable and rational. Clearly, the SD scenario is the potential pathway for sustainable land use management in the long run. These results may provide the local governments with reference for their decision-making to conduct sustainable land use and urban planning.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.