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Zhang C.,China Agricultural University | Liu J.,China Agricultural University | Chen Y.,China Agricultural University | Lu Y.,China Agricultural University | And 4 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2017

In order to objectively monitor and evaluate the effectiveness of land consolidation, a new method to identify filed patch data was proposed, which was based on remote sensing imagery and taking arable land polygon data as constraints. The field patch data identified by using this method not only had regional characteristics, but also considered ownership information to some extent. On this basis, evaluation index system in field and regional levels was respectively built for cultivated field patch fragmentation's degree. Patch area, regularity, compactness and connectivity were chosen as evaluation indexes in field level and mean area, density of field patch, LAI, LSI and PAFRAC were chosen as evaluation indexes in regional level. And then the indexes in two levels were made to be dimensionless respectively. The method was applied to Guangxi hilly areas and Hainan plains. The results of experiment showed that after consolidation, the majority of comprehensive evaluation index in field level of Guangxi region was improved from 50~70 to 70~90, and that in Hainan plains was improved from 70~90 to 90~100. The comprehensive evaluation index in regional level of Guangxi hilly areas was increased from 41 to 83 after land consolidation, and that in Hainan plains was increased from 63 to 92. The proposed method for identifying field patch can support the monitor and evaluation of land consolidation projection aimed at land fragmentation, no matter study areas located in hilly areas or plain areas. Hence, the research result can provide support for remote sensing monitor and evaluation in land consolidation areas and remote sensing discerning of well-facilitated farmland. © 2017, Chinese Society of Agricultural Machinery. All right reserved.


Zhang C.,China Agricultural University | Zhang C.,Key Laboratory for Agricultural Land Quality | Qiao M.,China Agricultural University | Yun W.,Key Laboratory for Agricultural Land Quality | And 6 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2017

The cultivated land is the basis of food security and the foundation of state. In order to regulate the cultivated land in China synthetically and three-dimensionally, according to Chinese national conditions, trinity comprehensive regulatory system about the quantity, quality and ecology of the cultivated land was studied from the perspective of system theory and public administration. Through literature analysis method combined with cutting-edge technology, the ecological regulation theory was improved based on the current relatively mature theory of cultivated land quantity, quality supervision system, the innovation of ecological regulation theory was researched, the index of cultivated land quantity, quality and ecology was constructed, and then a trinity comprehensive index system was built on this basis. Through data acquisition system of remote sensing technology, the internet of things and the internet to real-timely acquire multi-source data, and through cleansing and integration, to build cloud database that is a supervision technology system can be promoted and easy to replicate in the test points all over the country. Researching regulatory index rapid computing technology, the trinity comprehensive regulatory system was set up based on multi-source data, distributed service-oriented cultivated land. The cultivated land trinity supervision platform was constructed, it would make idea into a finished product, and provided service for government depatrments and regulatory decisions. The trinity supervision system of the cultivated land can provide technical support for the regulation and maintenance of the cultivated land in China. © 2017, Chinese Society of Agricultural Machinery. All right reserved.


Yang Y.,China Agricultural University | Yang Y.,Key Laboratory for Agricultural Land Quality | Shi Y.,China Agricultural University | Sun T.,China Agricultural University | And 3 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

Development and layout of the reserved cultivated land resources directly affects the sustainable utilization of land resources and protection of ecological environment. Based on the results of the second round survey of reserved cultivated land resources and targeted at the special environment in Tibet, an exploitation suitability evaluation model of reserved cultivatable land resources in Tibet was presented based on the natural and social benefits. Taking the township level administrative region in Tibet as a unit, clustering variables included each township comprehensive suitability index, continuously concentrated index and yield potential index of reserved cultivatable land resources. Considering the spatial neighbor relationship, the spatial "K" luster analysis was used by tree edge removal (SKATER) algorithm based on graph theory. The cluster analysis was used to determine the exploitation combination of reserved cultivated land resources in Tibet. The results showed that the exploitation suitability of reserved cultivated land resources included three grades, which were the most suitability area, the medium suitability area and the barely suitability area. The area of them accounts for 27.98%, 49.09% and 22.93% of total area of the study area, respectively. According to the model of exploitation combination, the townships with reserved cultivated land resources were divided into three groups as the recently major development area, medium-term moderate development area and long-term development area, which maintained neighbor relationships between the objects, as well as the integrity of the district territories. Furthermore, the results provided reference for the development partition of reserved cultivated land resources for local governments, and it also provided scientific basis for agricultural economy. © 2016, Chinese Society of Agricultural Machinery. All right reserved.


Yang Y.,China Agricultural University | Wang X.,China Agricultural University | Meng D.,China Agricultural University | Sun T.,China Agricultural University | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

The cultivated land quality grading results have played an important role in the protection of cultivated land, therefore, research on the test method of cultivated land quality grading results has important practical significance. The main purpose of this study was to prove the rationality of the cultivated land quality gradating results by testing cultivated land natural quality index. This paper presented an improved model, and it was established with inverse distance weighting (IDW) matrix which was amended by cultivated land area, then suspected outliers of the improved model and the original IDW model were extacted. In order to determine the accuracy of suspected outliers, a further study was carried out to establish a standard for judging and extracting suspected outliers, which included analyzing the standard deviation of cultivated land natural quality index of cultivated land unit, and selecting a distance which is greater than the threshold distance of biggest Moran index as a buffer radius. This new method was illustrated by using Ningcheng County of Inner Mongolia as a case study, the standard deviation was 78 and the buffer radius was 600 m were obtained. The results indicated that the method using standard to judge suspected outliers can accurately judge determined outliers and exclude most non-deterministic outliers. Besides, the improved model was better than the original IDW model on extracting the determined outliers unit in the same threshold distance, which illustrated that both the area and the distance were important indicators of the quality of cultivated land. This method can provide a reference for testing the cultivated land quality gradating results at county level, and also provide a new way for the application of spatial autocorrelation analysis in related fields. © 2016, Chinese Society of Agricultural Machinery. All right reserved.


Zhang C.,China Agricultural University | Li Z.,China Agricultural University | Li P.,China Agricultural University | Yang J.,China Agricultural University | And 3 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2015

Urban-rural land use plan is the foundation of healthily and orderly sustainable development of urbanization and the monitoring of plan implementation is considered as the guarantee. The association of remote sensing and GIS is one of rapid and effective monitoring method for urban-rural land use plan implementation which strongly strengthens the dynamic management of land use plan implementation. We used high spatial resolution remote sensing imagery-WorldView-2 with resolution of 0.5 m and the object-oriented image analysis method to achieve the classification. The features and thresholds were determined with CART decision tree in object-oriented rule classification. On the basis of classification results, the completion rate of land plan for each plan patch was computed with the monitoring and evaluation of land use plan implementation. Finally, a subdistrict of Fangshan District, Beijing City was taken as the study area to illustrate the method. The results showed that the final overall accuracy of classification was 0.89 and Kappa coefficient was 0.87. The proposed classification algorithm can meet the basic needs of urban-rural land use plan monitoring. The implementation of land use plan in northeast study area is better than that in the west. The public green land and water area need to be investigated and monitored further as the key objects, at the same time, the density of second type residential building is a little high, while the green landrate is low. © 2015, Chinese Society of Agricultural Machinery. All right reserved.


Liu Z.,China Agricultural University | Li Z.,China Agricultural University | Zhang Y.,China Agricultural University | Zhang C.,China Agricultural University | And 4 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2015

To address the issue of distinguishing seed maize from grain maize with remote sensing, a method of multi-temporal OLI/Landsat-8 remote sensing images combined with GeoEye-1 high-resolution texture was proposed. Utilizing the phenological phase differences of all classes from multi-temporal OLI/Landsat-8 images, the C5.0 decision tree classification algorithm was applied to the constructed EVI time-series. According to the texture difference between seed maize and grain maize, thresholds were set to identify seed maize by using GeoEye-1 high-resolution texture information. Finally, Linze County of Zhangye City in Gansu Province was taken as a study area to test the method. The results showed that the overall classification accuracy of multi-temporal OLI/Landsat-8 was 86.31% and the Kappa coefficient was 0.81, the user accuracy of maize identification was 88.39% and the mapping accuracy was 95.35%, which can meet the demand of further identification of seed maize. In contrast, when combined with texture information from high-resolution images, the user accuracy of seed maize was 86.37% and the mapping accuracy was 83.02%, which were higher than those of exclusive OLI/Landsat-8 data source. The conclusion is, this method can play a technical role in monitoring seed field over large range fast and accurately with remote sensing technology, enforcing seed market supervision and improving the authorities' response time to the market. ©, 2015, Chinese Society of Agricultural Machinery. All right reserved.


Zhao D.,China Agricultural University | Du M.,China Agricultural University | Yang J.,China Agricultural University | Yang J.,Key Laboratory for Agricultural Land Quality | And 4 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

In order to explore the influence of land use change and driving forces in the process of urban development, this paper took Shunyi District, Beijing as an example and analyzed the dynamic changes of land use types in the study area. Meanwhile, the direction and degree of the transformation of land use based on Markov model with three remote sensing images in 2000, 2005 and 2010 were analyzed. Combined with GIS analysis function, the influence of various land use driving forces on land use types was discussed, and the factors of land use transformation were standardized by using fuzzy calculation and intergrated by using weighted linear combination method. CA-Markov model was used to simulate the distribution of land use in 2010. The accuracy of the proposed simulation results obtained was as high as 81.41%, the Kappa index was 0.776 9. The feasibility and accuracy of the proposed method were proved. Based on the patterns of land use in 2010, the land use status of the land in 2020 was predicted and land use changes from 2010 to 2020 were analyzed. In addition to grassland and forest, the area of cultivated land and water continues maintaining the trend of reducing, but the trend is slowing down. And construction land is still increasing. This study provides scientific support for the planning and decision making of land use in the present and future. © 2016, Chinese Society of Agricultural Machinery. All right reserved.


Du M.,China Agricultural University | Zhao D.,China Agricultural University | Yang J.,China Agricultural University | Yang J.,Key Laboratory for Agricultural Land Quality | And 2 more authors.
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | Year: 2015

In order to provide a more reasonable and scientific accordance for projection and management of urban land utilization in the future based on the land use evolution simulation, CA-Markov integrated model was applied to simulate land use distribution of Haidian district in 2008 based on three periods land use data of 1996, 2002 and 2008. In the paper, method of combining CA-Markov model and multi-criteria evaluation was adopted to construct the celluar quantity and spatial location transformation regulations. In addition, some influence factors for land utilization transformation were taken into account, such as natural environment, economical and social development, agricultural production and so on. And three kinds of cellular neighborhood sets of different size were constructed separately in the process of simulation. Based on the result of consistency test, the reseach method used in this paper is proved to be highly feasible and the size of cellular neighbour space has a significant effect on the results accuracy. The land use evolution simulation results show that the city intensive phenomenon wound be severe, and construction lands expand rapidly, which would take up more area of cultivated land and garden plot. Therefore, it is urgent to promote the sustainable development of the urban land utilization. © 2015, SinoMaps Press. All right reserved.


Zhang C.,China Agricultural University | Zhang C.,Key Laboratory for Agricultural Land Quality | Zhang H.,China Agricultural University | Yang J.,China Agricultural University | And 4 more authors.
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | Year: 2015

Understanding the spatial distribution characteristics and influence mechanism of arable land quality is significant to make the scientific decision for the protection of arable land. In this paper, we explored the spatial structural characteristics and agglomerate regulation of arable land quality by taking the arable land quality index as the spatial variable, analyzed the reasons of the differences in the studied area with the use of variation coefficient, bivariate correlate analysis and partial correlation methods. The main results of the study were as follows: Firstly, the arable land quality exhibited a significant spatial autocorrelation to some extent of spatial distribution in Daxing district. Besides, the High-High type of arable land quality which is the positive spatial autocorrelation emerged as the cluster and had a powerful agglomeration, while the Low-Low type arable land had a wide distribution and was of low spatial autocorrelation. The negative spatial autocorrelation which included the High-Low type and Low-High type barely had a concentrate region, and most of them distributed dispersedly with barely outstanding regular pattern. Soil organic matter, surface soil and soil texture profile configuration are the dominant influential factors that affect the spatial distribution pattern of arable land quality, but the influence degree of the factors are different. © 2015, SinoMaps Press. All right reserved.


Yang J.,China Agricultural University | Yang J.,Key Laboratory for Agricultural Land Quality | Tang S.,China Agricultural University | Tang S.,Key Laboratory for Agricultural Land Quality | And 8 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

China, an agricultural country, has a large population but not enough cultivated land. Until 2011, the cultivated land per capita was 1.38 mu (0.09 ha), only 40% of the world average, and it is getting worse with industrialization and urbanization. The next task for the Ministry of Land and Resources: Dynamic monitoring of cultivated land classification in which a number of counties will be sampled; in each county, a sample-based monitoring network would be established that reflects the distribution and its tendency of cultivated land classification in county area and estimates of non-sampled locations. Due to the correlation among samples, traditional methods such as simple random sampling, stratified sampling, and systematic sampling are insufficient to achieve the goal. Therefore, in this paper we introduced a spatial sampling method based on the Kriging estimation error. For our case, natural classifications of cultivated land identified from the last Land Resource Survey and Cultivated Land Evaluation are regarded as the true value and classifications of non-sampled cultivated lands would be predicted by interpolating the sample data. Finally, RMSE (root-mean-square error) of Kriging interpolation is redefined to measure the performance of the network. To be specific, five steps are needed for the monitoring network. First, the optimal sample size is determined by analyzing the variation trend between the number and the accuracy of samples. Then, set up the basic monitoring network using square grids. The suitable grid size can be chosen by comparing the grid sizes and the corresponding RMSEs from the Kriging interpolation of the samples data. Because some centers of grids do not overlap the area of cultivated land, the third step is to add some points near the centers of grids to create the global monitoring network. These points are selected from centroids of cultivated land spots which are closest to the centers and inside the searching circles around the centers by a loop algorithm. The fourth step is a procedure of densification, which is needed to build Thiessen polygons through global sampling points. Then, add the point of maximum Kriging estimation error inside polygons whose RMSEs are relatively high to the network only if it makes the global RMSE smaller. This procedure stops when the count of sampling points reaches the optimal sample size. The final step is to replace several monitoring points by standard plots to reduce the sampling cost. Finally, estimate the population mean of cultivated land classification through Kriging interpolation. Experiments in Beijing Daxing district that compared this method to traditional sampling methods in cost (count of sampling points), estimation accuracy (measured by RMSE), and prediction accuracy of the population mean illustrate that the estimation accuracy of this method is higher than simple random sampling, stratified sampling, or traditional grids when the number of sampling points is 48. Besides, the prediction accuracy of population mean stays in an accurate level with the relative error of 0.07%. Therefore, this method can meet the needs of monitoring the classification of cultivated land in county area.

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