Search

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil salinization in the Yellow River Delta is a difficult and miscellaneous disease to restrict the development of agri‐ cultural economy, and further hinders agricultural production. To explore the retrieval of soil salt content from remote sensing images under the condition of no vegetation coverage, the typical area of the Yellow River Delta was taken as the study area to obtain the hyperspectral of surface features, the multispectral of UAVs and the soil salt content of sample points. Three represen‐ tative experimental areas with flat terrain and obvious soil salinization characteristics were set up in the study area, and 90 sam‐ ples were collected in total. By optimizing the sensitive spectral parameters, machine learning algorithms of partial least squares regression (PLSR) and random forest (RF) for inversion of soil salt content were used in the study area. The results showed that: (1) Hyperspectral band of 1972 nm had the highest sensitivity to soil salt content, with correlation r of -0.31. The optimized spectral parameters of shortwave infrared can improve the accuracy of estimating soil salt content. (2) RF model optimized by two different data sources had better stability than PLSR model. RF model performed well in terms of generalization ability and balance error, but it had some over-fitting problems. (3) RF model based on ground feature hyperspectral (R² =0.54, verified RMSE=3.30 g/kg) was superior to the random forest model based on UAV multispectral (R² =0.54, verified RMSE=3.35 g/kg). The combination of image texture features improved the estimation accuracy of multispectral model, but the verification accuracy was still lower than that of hyperspectral model. (4) Soil salt content based on UAV multi-spectral imageries and RF model was mapped in the study area. This study demonstrates that the level of soil salinization in the Yellow River Delta region is significantly different in geographical location. The cultivated land in the study area is mainly light and moderate salinized soil with has certain restrictions on crop cultivation. Areas with low soil salt content are suitable for planting crops in low salinity fields, and farmland with high soil salt content is suitable for planting crops with high salinity tolerance. This study constructed and compared the soil salinity inversion models of the Yellow River Delta from two different sources of data, optimized them based on the advantages of each data source, explored the inversion of soil salinity content without vegetation coverage, and can provide a reference for more accurate inversion of land salinization. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The wide use of plastic greenhouses in the word has brought economic benefits, but also caused many environmental problems. Accurate and timely acquisition of spatial distribution information of plastic greenhouse is of great significance to agricultural production and soil management. The use of plastic greenhouse has changed the structure of soil, thus changing the surface spectral characteristics of the soil. Because of the particularity of the plastic film material, the reflection spectrum has strong directionality and uncertainty, so it is difficult to identify plastic greenhouse accurately only depending on the reflectance spectrum characteristics of ground objects. In this paper, Kunming City, Yunnan Province is taken as the research area, using GF-2 image as a single data source, the multi-scale segmentation method is used to obtain the image object efficiently and accurately. According to the detailed spatial information of plastic greenhouse, the applicability of three image object-based texture extraction algorithms, namely GLCM(gray-level co-occurrence matrix), LBP(local binary pattern) and PSI(pixel shape index) for plastic greenhouse identification is analysed and tested. In addition, different texture features are combined with spectral features and NDVI index to form different classification schemes, to explore which one would be the best combination of texture features for identification of plastic greenhouses. In order to explore the robustness of the method, different texture feature combination schemes are applied in the study areas. The results of SVM (support vector machine) classifier are evaluated by confusion matrix. The results show that the overall combination schemes of the two research areas with different landscape patterns have the same trend. The combination of spectral features and NDVI index can accurately identify the scattered waters in each of the two study areas. For plastic greenhouses and impervious surfaces with similar reflectance spectra, adding texture features can make up for limitation of spectral characteristics and improve the overall accuracy. The phenomena of homologous or homologous spectra in spectral features of high spatial resolution image can effectively improve the discrimination between plastic greenhouse and impervious surface. Texture features can significantly improve the identification accuracy of plastic greenhouse following the object-based image classification frame. In the classification scheme of plastic greenhouses with different spatial distribution structures, the LBP (local binary pattern) texture algorithm has the best recognition accuracy, the overall accuracy of study area A is 96.85%, Kappa coefficient is 0.95, and that of study area B is 95.58% and 0.94. Landscape fragment analysis (landscape fragmentation index area mean index, aggregation indices) of the two different study areas showed that the plastic greenhouses in study area A are more fragmented than study area B (area mean indices are 3.39 hm² and 1.37 hm², aggregation indices are 80.64 and 72.98 for plastic greenhouses in study area A and B, respectively). The results of fragmentation are consistent with those of classification, and the accuracy of landscape classification with more space fragments is lower (the highest overall classification accuracy for study area A and B are 98.13% and 96.13%, respectively, the PA(producer accuracy) and UA(user accuracy) are 96.47% and 97.93% for study area A, and 90.67%, 99.68% for study area B). The results show that texture features based on image objects can improve the recognition accuracy of small-scale plastic greenhouse. This is of great significance to the accurate mapping of the distribution of plastic greenhouses. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Prataculturae Sinica is the property of Acta Prataculturae Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, it's still difficult to entirely replace the artificial visual interpretation for the computer automatic classification, which is used to extract land cover types' information from the remote sensing imagery, because the automatic method needs more efforts to improve the precision of the classification results. Furthermore, this problem has become the key joint of the automatic classification extraction. How to extract land cover types' information in western area of Jilin, is one of the important problems, and the confused land cover types needs to be distinguished. The aim of this study is to deepen the application of remote sensing classification method that is used to extract land cover information automatically and quickly from the satellite imagery. The western area of Jilin is selected as the main research area. A new total solution to extract land cover information, based on the spatial variation theory, has been designed for the convenient automatic classification with the remote sensing technology. The remote sensing classification scheme is carried out by coding the R language algorithm and operating the remote sensing software ERDAS platform. The land cover types in Zhenlai County in the western area of Jilin, have been extracted and monitored through the combined utilization of 4 indices, including semivariance value of normalized difference vegetation index (NDVI) dataset, local variance of image texture, modified soil-adjusted vegetation index and normalized difference water index, which have significant meaning for the land cover types in the transition zone between cropping area and nomadic area. These variances have definite physical meaning (including vegetation, water, and soil drought conditions), so that the phenological information was used to build a multi-dimensional feature space classification data set. The results indicated that: 1) A total of 11 land cover types are extracted, using the multi-temporal remote sensing information to build a multidimensional classification characteristics data set based on the Landsat 8 data. The overall classification accuracy of the algorithm is 95.50%; the Kappa coefficient of classification is 0.9504. The automatic extracting approach implemented obtains a comparatively ideal classification result; 2) The introduction of 3 characteristic variables of the classification in the scheme significantly improves the separability of the confused land cover types. Considering the vegetation classification, the vegetation growth information has practical life-activity significance, and is a real-time dynamic method for the vegetation change monitoring; 3) Improving the land cover classification accuracy is not to introduce more characteristic parameters of the classification, but to effectively combine multiple appropriate classification variables. The new method can broaden the application vision and the scope of the ecological remote sensing investigation of surface vegetation. Moreover, the introduction of new variables not only makes the macro monitoring more convenient, but also improves the accuracy of classification of remote sensing interpretation. It's noted that the extracted classification has obvious regional feature, and the regional feature is consistent with the farming cultivation characteristics on the Northeast Plain. In a word, the results can provide a credible approach and valuable example for extracting and monitoring land cover type in farming-pastoral transitional zone. It is feasible to use the spatial variation theory to extract and monitor land cover type by combining the several evaluation indices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)