In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CAMarkov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026.
Accurate and timely collection of urban land use and land cover information is crucial for many aspects of urban development and environment protection. Very high-resolution (VHR) remote sensing images have made it possible to detect and distinguish detailed information on the ground. While abundant texture information and limited spectral channels of VHR images will lead to the increase of intraclass variance and the decrease of the interclass variance. Substantial studies on pixel-based classification algorithms revealed that there were some limitations on land cover information extraction with VHR remote sensing imagery when applying the conventional pixel-based classifiers. Aiming at evaluating the advantages of classifier ensemble strategies and object-based image analysis (OBIA) method for VHR satellite data classification under complex urban area, we present an approach-integrated multiscale segmentation OBIA and a mature classifier ensemble method named random forest. The framework was tested on Chinese GaoFen-1 (GF-1), and GF-2 VHR remotely sensed data over the central business district (CBD) of Zhengzhou metropolitan. Process flow of the proposed framework including data fusion, multiscale image segmentation, best optimal segmentation scale evaluation, multivariance texture feature extraction, random forest ensemble learning classifier construction, accuracy assessment, and time consumption. Advantages of the proposed framework were compared and discussed with several mature state-of-art machine learning algorithms such as the k -nearest neighbor (KNN), support vector machine (SVM), and decision tree classifier (DTC). Experimental results showed that the OA of the proposed method is up to 99.29% and 98.98% for the GF-1 dataset and GF-2 dataset, respectively. And the OA is increased by 26.89%, 11.79%, 11.89%, and 4.26% compared with the traditional machine learning algorithms such as the decision tree classifier (DTC), support vector machine (SVM), k -nearest neighbor (KNN), and random forest (RF) on the test of the GF-1 dataset; OA increased by 32.31%, 13.48%, 9.77%, and 7.72% for the GF-2 dataset. In terms of time consuming, by rough statistic, OBIA-RF spends 223.55 s, SVM spends 403.57 s, KNN spends 86.93 s, and DT spends 0.61 s on average of the GF-1 and GF-2 datasets. Taking the account classification accuracy and running time, the proposed method has good ability of generalization and robustness for complex urban surface classification with high-resolution remotely sensed data.
The various ecological processes of human beings are not only restricted by the landscape pattern on the regional scale but also affect the local and regional landscape together with global climate change. To date, most of the research on ecological security is based on the pressure-state-response (PSR) model, while there were a few studies based on the landscape ecology model approach. In addition, there has been little literature focus on the dynamic change process of ecological security, especially the simulation and prediction of the future development trend of ecological security. The purpose of this research is to establish a landscape ecological security evaluation method based on grid division, be aimed at breaking the inherent drawbacks of the administrative region as a unit mode approach, anticipated to better reflect the landscape ecological security status of the study area. A complex framework was constructed by integrating random forest algorithm, Fishnet model, landscape ecology model, and CA-Markov model. Multitemporal remote sensing data were selected as a data source, and land use maps of the study area were obtained through the random forest machine learning algorithm firstly. And then, the study area is divided into 307 grids of 2 km × 2 km using the Fishnet model. Next, the landscape disturbance index, landscape vulnerability index, and landscape loss index are used on the grid scale to establish a landscape ecological security evaluation model. Finally, ecological security assessment of Zhengzhou city was carried out, and the distribution map of the landscape ecological status in 1986, 1996, 2006, 2016, and predicted for 2026 was obtained. The results of the study showed that, as time goes by, the areas with high ecological safety gradually decrease. It is predicted that by 2026, the ecological security level of Zhengzhou will be dominated by lower ecological security areas. The research results can provide basic information and decision support for government agencies and land use planners to ensure responsible and sustainable development of the urban environment and ecology.
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