Attach the current situation of Guangdong province China consolidation central, we design and exploitation consolidation project management system, to meet the demand for consolidation project management system; during exploring Land consolidation project management service system with Chinese characteristics, this system builds land consolidation project management department, and the “3-Level and multi-point” management model for work personnel. This paper introduces the system architecture, function and technology achieve, it solves some problems, such as management accident and irregular in the formerly management process, reduces the handing cost, According to the existing lacks, the system not only considers the normal work flow and model, but also the dynamic integration between information network platform and information application system, at the same time, we also think over the different management methods and characteristic in geography and mechanism, then design reasonable hierarchy network topology, present lines for system perfection and majorization.
Dike-ponds in fisheries often present multiple pond conditions such as pure, suspended sediment, water bloom, semidry conditions, etc. However, the impact of these conditions on the performance of extracting dike-pond from remote sensing images has not been studied. To solve this problem, we explore the existence of such impacts by comparing the performance of four rule-based methods in two groups of test regions. The first group has few multiple pond conditions, while the second has more. The results show that various measure values deteriorate as the proportion of multiple pond conditions in the regions increases. All four methods performed worse in the second group than the first, where the overall accuracy decreased by 8.80%, misclassification error increased by 3.69%, omission error raised by 10.53%, and correct quantity rate dropped by 8.23%, respectively. The extraction method that ingested multiple pond conditions performed indistinguishably from the other methods in the first group. However, it outperformed the other methods in the second group, with a 4.22% improvement in overall accuracy, a 10.25% decrease in misclassification error, and a 19.03% increase in the correct quantity rate. These findings suggest that multiple pond conditions can negatively impact the extraction performance and should be considered in dike-pond applications that require a precise pond size, number, and shape.
With a series of redevelopment activities, such as land consolidation and urban renewal, many cities in China have experienced land de-urbanization phenomena. These include the conversion of construction land into green spaces (such as parks, forests, and lawns), blue spaces (such as rivers, lakes, and wetlands), and farmland. However, there is currently limited research on diverse land de-urbanization types and pathways. This study focuses on investigating the land de-urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2014 to September 2023 using the Continuous Change Detection and Classification (CCDC) method. The results demonstrate that the GBA experienced 72.74 square kilometers of de-urbanization during the study period, primarily through the conversion of construction land to land with low plant coverage, including grassland and farmland. There were significant differences in the quantity and spatial agglomeration of de-urbanization between cities and within individual cities. Temporally, de-urbanization predominantly occurred in the period of 2016 to 2021, with a sharp decline in 2022. The temporal changes were significantly influenced by urban renewal policies and the impact of the COVID-19 pandemic. In terms of spatial clustering characteristics, the de-urbanization process in the GBA exhibited spatial agglomeration but was primarily characterized by low-level clustering. This study also examines the correlations between de-urbanization and factors including location and the stage of urbanization. The analysis showed that de-urbanization within cities tended to concentrate near the main urban roads within a range of 10–30 km from city centers. The trend of de-urbanization followed a pattern that is consistent with the Northam curve, where de-urbanization tends to increase during the rapid urbanization phase and decline as urbanization reaches a mature stage. Overall, this study provides valuable insights for the redevelopment of construction land within the context of ecological civilization construction. It also offers suggestions for urban land development and redevelopment in metropolitan areas.
A type of aquaculture pond called a dike-pond system is distributed in the low-lying river delta of China’s eastern coast. Along with the swift growth of the coastal economy, the water surfaces of the dike-pond system (WDPS) play a major role attributed to pond aquaculture yielding more profits than dike agriculture. This study aims to explore the performance of deep learning methods for extracting WDPS from high spatial resolution remote sensing images. We developed three fully convolutional network (FCN) models: SegNet, UNet, and UNet++, which are compared with two traditional methods in the same testing regions from the Guangdong–Hong Kong–Macao Greater Bay Area. The extraction results of the five methods are evaluated in three parts. The first part is a general comparison that shows the biggest advantage of the FCN models over the traditional methods is the P-score, with an average lead of 13%, but the R-score is not ideal. Our analysis reveals that the low R-score problem is due to the omission of the outer ring of WDPS rather than the omission of the quantity of WDPS. We also analyzed the reasons behind it and provided potential solutions. The second part is extraction error, which demonstrates the extraction results of the FCN models have few connected, jagged, or perforated WDPS, which is beneficial for assessing fishery production, pattern changes, ecological value, and other applications of WDPS. The extracted WDPS by the FCN models are visually close to the ground truth, which is one of the most significant improvements over the traditional methods. The third part is special scenarios, including various shape types, intricate spatial configurations, and multiple pond conditions. WDPS with irregular shapes or juxtaposed with other land types increases the difficulty of extraction, but the FCN models still achieve P-scores above 0.95 in the first two scenarios, while WDPS in multiple pond conditions causes a sharp drop in the indicators of all the methods, which requires further improvement to solve it. We integrated the performances of the methods to provide recommendations for their use. This study offers valuable insights for enhancing deep learning methods and leveraging extraction results in practical applications.
This study is based on land consolidation project management policies, industry needs and current situation of land department. According to the characteristics of land consolidation, a multi-source heterogeneous integration database is established. It could be used to solve several problems, including disagreement caused by diversity of data sources and variety of structure. Base on this, a Land Consolidation Project Management Information System (LCPMIS) was built with some features as follows: object-oriented and project-driven workflow technology was used, according to two facts, land consolidation's core is project management and it has procedural characteristics; Business functions were divided into atomic modules by deconstructing and reconstructing basic elements of land consolidation project management. With all these features, the system was supposed to adapt to different business goals and to have a quick response to any changes on projects, so that it would improve the standardization of work processes.
Pests have been greatly damaging the rice yield in Guangdong Province, China in recent year. Traditional pests warning models, based on the mathematical statistics, only considering the relation between weather and monitoring data factors, are limited in large place. As a result, multi-factor spatial overlay model was construction to research the damage of Cnaphalocrocis medinalis in Guangdong Province. The factors include the Cnaphalocrocis medinalis situation, crop condition, and external environment data. By using the GIS technology, the model can make the thematic maps of Cnaphalocrocis medinalis damage in different time. The result can reproduce the spatio-temporal change of Cnaphalocrocis medinalis in Guangdong Province, as well as offer scientific support on prevention of rice pests.
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