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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. Source

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. Source

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. Source

Yang J.,China Agricultural University | Yang J.,Key Laboratory for Agricultural Land Quality | Yue Y.,China Agricultural University | Yue Y.,Key Laboratory for Agricultural Land Quality | And 5 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

M Monitoring points in country area are the foundation to reflect changes of cultivated land quality, which directly affect the result of farmland grading and its accuracy. Through the monitoring network for cultivated land quality in county area, the distribution and changing trend of the cultivated land quality can be reflected. Besides, the quality of non-sampled locations should also be estimated with the data of sampling points. Due to the correlation among spatial samples, the traditional methods such as simple random sampling, stratified sampling and systematic sampling are inefficient to accomplish the task above. Thus, we propose a new spatial sampling and optimizing method based on the spatial simulated annealing (SSA). This paper presents a pre-processing method to determine the number of sampling points, including preprocessing the data of cultivated land quality before sampling, exploring the spatial correlation and spatial distribution pattern of cultivated land quality, and computing the appropriate quantity of sampling points by analyzing the change trend of sampling number and sampling precision, and on this basis we propose the extended spatial simulated annealing method to optimize spatial sampling design for obtaining the minimal Kriging variance. The main steps for computing the optimal sampling design can now be summarized as follows: 1) calculate the semi-variogram of cultivated land quality and determine the parameters of ordinary Kriging interpolation; 2) identify the quantity of samples, choose a set of cultivated land map spots randomly as an initial design, and compute the associated fitness function; 3) given one design, construct a candidate new sampling design by random perturbation; 4) compute the fitness function for the new design, and if it is smaller than or equal to that for the original design, accept the original design, or else accept the new design with an acceptance probability. If the new design is accepted, the estimated point (j) is returned to zero, or else increased by 1; 5) if j is smaller than or equal to a threshold value of continuous rejections, increase i (representing monitoring point) by 1, or else stop the iteration and current design is the best. Designs by simulated annealing that reduce the average Kriging standard error are always accepted, and designs that worsen the interpolation effect are accepted with a certain probability, which decreases to zero as iterations proceed. However, there are integrated factors such as soil organic matter content, topsoil texture, profile pattern, salinization which affect arable land quality change over time and space, and are taken as potential change factors to detect potential change areas. Under the guidance of expert knowledge, the sampling points are set up through spatial simulated annealing algorithm and adjusted based on potential change areas, rivers, roads and abnormal monitoring points. We illustrate this new method using Daxing District, Beijing City as a case study. Spatial overlay analysis of potential change factors and geostatistics method of GIS are employed to test this method. The spatial variability of cultivated land quality is simulated using natural quality indices and a specified number of network locations is defined which can be used to adequately predict the quality of cultivated land. The experimental results of Daxing District, Beijing City show that 55 monitoring reference sample units are finally deployed, and the average ordinary Kriging standard error with this method is 131.78, which is smaller than the simple random sampling (134.97) and stratified sampling (134.93) when the quantity of samples is the same. Besides, sampling accuracy and cost are both considered and reach a certain balance in this method. This method is suited for counties which have carried out several surveys of cultivated land quality, or counties whose grading factors have certain changes. Besides, it is also suitable for counties which have some prior knowledge but never have conducted a survey of cultivated land quality. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved. Source

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. Source

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