Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

Modeling spatially non-stationary land use/cover change in the lower Connecticut River Basin by combining geographically weighted logistic regression and the CA-Markov model

Simulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation.     

我国海洋功能区划的回顾性评价和新一轮编制建议

为完善我国海洋功能区划制度,提高海洋功能区划的编制和实施水平,促进其在我国海洋空间规划乃至国土空间规划中发挥应有作用,文章对我国现行海洋功能区划的实施情况进行回顾性评价,并对新一轮海洋功能区划的编制提出建议。研究结果表明:我国海洋功能区划法律地位高、管控范围广,现行海洋功能区划实施以来,通过建立定量目标管控体系、协调保障行业用海和保护海洋生态环境,对我国海洋经济和海洋生态环境的可持续发展做出巨大贡献;针对实施过程中存在的问题,新一轮海洋功能区划的编制应扩大海洋保护区的规模和种类、提高前瞻性和动态适应性、加强公众实质性参与以及完善技术体系。     

德州市临盘采油区地面沉降监测

济南市的鹊山上设立了3块岩层水准标石的原点组。使用美国Tri mble Di Ni 12电子水准仪和条码式铟钢水准标尺施测一等精密水准82.2 km,二等精密水准30.8 km,联测各类水准点35个。对观测结果进行统计与分析表明,在临盘采油区(以后仓西北采油四队LY18为中心)形成了一个较明显的沉降区域,年沉降量为54.3 mm。建议在其周边增埋地面观测标石,进行加密观测,以掌握其变化规律。     

Advances in understanding of the mechanism for generation of earthquake thermal precursors detected by satellites

Stresses building up during an earthquake preparation phase also manifest themselves in the form of a so called increased land surface temperature (LST) leading to a thermal precursor prior to the earthquake event. This phenomenon has now been validated by our observations of short-term thermal anomalies detected by infrared satellite sensors for several recent past earthquakes around the world. The rise in infrared radiance temperature was seen to vary between 5 and 12 °C for different earthquakes. We discuss in this paper different explanations for the generation of such anomalies that have been offered. Emission of gases due to the opening and closure of micropores upon induced stresses and also the participation of ground water have been propounded as a possible cause for generation of thermal anomalies. Seismo-ionosphere coupling, by which gases like radon move to the earth–atmosphere interface and cause air ionization thus bringing about a change in air temperature, relative humidity, etc., has been put forth by some workers. A mechanism of low frequency electromagnetic emission was tested and experimented by scientists with rock masses in stressed conditions as those that exist at tectonic locations. The workers proposed the positive hole pair theory, which received support from several scientific groups. Positive holes (sites of electron deficiency) are activated in stressed rocks from pre-existing yet dormant positive hole pairs (PHPs) and their recombination at rock–air interface leads to a LST rise. A combination of remote sensing detection of rock mechanics behavior with a perception of chemistry and geophysics has been applied to propose the remote sensing rock mechanics theory. Remote sensing detections of such anomalies confirm so far proposed lab theories for such a hotly debated field as earthquake precursor study by providing unbiased observations with consistency in time and space distribution.     

贵州省国土资源信息化建设模式研究

本文在深入调研贵州省国土资源信息化建设现状基础上,回顾了发展历程,系统总结了贵州省在推进国土资源信息化建设方面的主要思路和做法,阐述了建设现状和取得的主要成效,以贵州省国土资源信息化建设模式为借鉴,提出了几个值得深入思考和探讨的问题。     

乡镇土地利用规划管理信息系统设计与实现

针对当前乡镇土地利用规划中存在的问题,运用GIS的数据管理和空间分析等功能,结合规划区域的实际状况,开发了一个面向乡镇的土地利用规划管理信息系统,提高了土地管理的工作效率和信息化水平,能够实现乡镇土地的合理利用。     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.     

大黄鱼病原副溶血弧菌拮抗菌的筛选

用点种法从大黄鱼肠道优势菌群中筛选出104株病原性副溶血弧菌的拮抗菌,进一步研究拮抗效果最好的11株拮抗菌的抗菌谱和生长曲线,其中NAC平板上筛选的4株拮抗菌的抗菌范围较广,锰营养琼脂培养基上筛选的3株拮抗菌具有生长优势。用BIOLOG板测定了抗菌谱较广、有一定生长优势3株拮抗菌(R22,Y58和J312)的碳源利用情况,结果表明,3株拮抗菌的碳源谱都比较广,都能利用糖类、有机酸和氨基酸等,而且与鱼类营养的竞争较小。16S rRNA基因同源性分析的结果显示与R22,Y58,J312同源性达99%以上的菌株分别来自弧菌属、芽孢杆菌属和假单胞菌属。进一步对R22,J312和Y58三株拮抗菌进行生化鉴定,结果显示R22为溶藻弧菌,J312为荧光假单胞菌,但Y58的种属未能得到确认。所得到的3株拮抗菌R22,Y58,J312在拮抗病原菌、抗菌谱、生长特性和营养利用等方面都初步满足益生菌的筛选条件,有待进一步检测其安全性和实用效果以便能够成为能实用的益生菌。