This page has only limited features, please log in for full access.
Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies.
Xiaohui Su; Shurui Ma; Xiaokang Qiu; Jiabin Shi; Xiaodong Zhang; Feixiang Chen. Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy. International Journal of Environmental Research and Public Health 2021, 18, 8000 .
AMA StyleXiaohui Su, Shurui Ma, Xiaokang Qiu, Jiabin Shi, Xiaodong Zhang, Feixiang Chen. Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy. International Journal of Environmental Research and Public Health. 2021; 18 (15):8000.
Chicago/Turabian StyleXiaohui Su; Shurui Ma; Xiaokang Qiu; Jiabin Shi; Xiaodong Zhang; Feixiang Chen. 2021. "Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy." International Journal of Environmental Research and Public Health 18, no. 15: 8000.
Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R
Quan Xiong; Liping Di; Quanlong Feng; Diyou Liu; Wei Liu; XuLi Zan; Lin Zhang; Dehai Zhu; Zhe Liu; Xiaochuang Yao; Xiaodong Zhang. Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network. Remote Sensing 2021, 13, 1512 .
AMA StyleQuan Xiong, Liping Di, Quanlong Feng, Diyou Liu, Wei Liu, XuLi Zan, Lin Zhang, Dehai Zhu, Zhe Liu, Xiaochuang Yao, Xiaodong Zhang. Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network. Remote Sensing. 2021; 13 (8):1512.
Chicago/Turabian StyleQuan Xiong; Liping Di; Quanlong Feng; Diyou Liu; Wei Liu; XuLi Zan; Lin Zhang; Dehai Zhu; Zhe Liu; Xiaochuang Yao; Xiaodong Zhang. 2021. "Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network." Remote Sensing 13, no. 8: 1512.
Typhoon disaster information is characterized by multiple sources, complexity and diversity, and different users of this content have different individual concerns. The expression methods of typhoon disaster information considered in previous research have been relatively simple, which cannot meet the personalized service needs of different users. In this paper, according to the diverse content needs of different users in distinct stages of a typhoon disaster, a typhoon disaster information expression method with a multi-user, multi-stage, multi-channel and multi-element combined mode is investigated. First, the audience and disaster stages are divided via demand analysis, and the demand content is summarized according to the users, stages, and release channels. Similar information is then integrated into the same theme, and it is also determined how information is expressed. Then, the analytic hierarchy process (AHP) is used to filter out the important information in each theme. The theme template is then designed according to the characteristics of particular release channels. Finally, a prototype system is developed, and Typhoon Lekima, which impacted China in 2019, is considered as a real case for analysis. The results show that the proposed method can effectively support different users to obtain disaster characteristics at distinct stages of typhoon disasters, evaluate disaster conditions, assist scientific decision-making, and enhance public awareness of risk prevention.
Cong Xiao; Xiaodong Zhang; Ziyao Xing; Keke Han; Zhe Liu; Junming Liu. Investigation of the Expression Method of Theme-Typhoon Disaster Information. ISPRS International Journal of Geo-Information 2021, 10, 109 .
AMA StyleCong Xiao, Xiaodong Zhang, Ziyao Xing, Keke Han, Zhe Liu, Junming Liu. Investigation of the Expression Method of Theme-Typhoon Disaster Information. ISPRS International Journal of Geo-Information. 2021; 10 (3):109.
Chicago/Turabian StyleCong Xiao; Xiaodong Zhang; Ziyao Xing; Keke Han; Zhe Liu; Junming Liu. 2021. "Investigation of the Expression Method of Theme-Typhoon Disaster Information." ISPRS International Journal of Geo-Information 10, no. 3: 109.
The eastern Helan Mountains are one of the most important plantings and production bases for high-quality grapes in China. In winter, grape root is prone to freezing damage owing to natural phenomenon such as early and late frost, and methods to assess disaster risk at the grape parcel level is one of the current research hotspots. However, there are two problems at present; first is the lack of grape parcel maps and second is that scale of disaster risk assessment is limited to the level of administrative divisions and grid systems. Therefore, according to the perennial characteristics of the grapes and clear texture of the open-air grape parcels based on public data sets (Sentinel-2A/B) and high-quality winery address information, combined with Google Earth engine (GEE) platform, Google Earth, and Ovitalmap, this study designs a collection scheme of open-air grape parcel samples and non-grape parcel samples, analyzes the environmental characteristics of the distribution of high-quality wineries, constructs a classification feature database in three aspects (time, terrain, and vegetation), uses the random forest classification method to classify open-air grape parcels in the eastern Helan Mountains in 2019, and conducts a late frost disaster risk assessment of the open-air grape parcel. The results show that: (1) The high-quality wineries are mainly distributed in the elevation of 1106–1240 m, with the average slope of 0.82°, the organic matter content of 10.79–14.27 g/kg, the P content of 24.87–37.58 mg/kg, the K content of 116–166 mg/kg, the pH 8.38–8.55, N content of 0.65–0.98 g/kg, annual average active accumulated temperature ≥10 °C of 3490–3590 °C, annual average rainfall of 189–201 mm, the annual average sunshine duration of 7.3–7.6 h, annual average air temperature of 8.8–9.4 °C, annual average temperature difference of 12.7–13.2 °C, annual average frost-free period at 263–270 d and annual average relative air Humidity of 50–54%. (2) The overall classification accuracy of open-air grape parcels reached 98.96% and the verification accuracy of ground truth parcel samples reached 87.89%. The open-air grape parcels in the eastern Helan Mountains are mainly distributed in Helan County, Xixia District, Yongning County, and Qingtongxia City. (3) The highest risk of late frost was found in Xixia District and the lowest risk in Yongning County. Helan County was higher than Qingtongxia City in the medium risk of late frost. The typical small area high risk includes Liangtian Town, the experimental field of Ningxia Academy of Agricultural and Forestry Sciences in Zhenbeibao Town, Yuhuang Winery in Ganchengzi Village, and the State-run Warm Spring Farm in Helan County. Thus, the sample collection and augmentation scheme are feasible, the open-air grape classification accuracy is high and its result is consistent with field investigation, which can provide accurate parcel-level disaster risk assessment and provide decision support for agricultural departments and farmers.
Wei Liu; Xiaodong Zhang; Fei He; Quan Xiong; XuLi Zan; Zhe Liu; Dexuan Sha; Chaowei Yang; Shaoming Li; Yuanyuan Zhao. Open-air grape classification and its application in parcel-level risk assessment of late frost in the eastern Helan Mountains. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 174, 132 -150.
AMA StyleWei Liu, Xiaodong Zhang, Fei He, Quan Xiong, XuLi Zan, Zhe Liu, Dexuan Sha, Chaowei Yang, Shaoming Li, Yuanyuan Zhao. Open-air grape classification and its application in parcel-level risk assessment of late frost in the eastern Helan Mountains. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 174 ():132-150.
Chicago/Turabian StyleWei Liu; Xiaodong Zhang; Fei He; Quan Xiong; XuLi Zan; Zhe Liu; Dexuan Sha; Chaowei Yang; Shaoming Li; Yuanyuan Zhao. 2021. "Open-air grape classification and its application in parcel-level risk assessment of late frost in the eastern Helan Mountains." ISPRS Journal of Photogrammetry and Remote Sensing 174, no. : 132-150.
The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is consequently costly in terms of time and money. In order to reduce the need for sample size, this paper proposes an unsupervised classification method based on principal components isometric binning (PCIB). In particular, principal component analysis (PCA) dimensionality reduction is applied to the classification features, followed by the division of the top k principal components into equidistant bins. Bins of the same category are subsequently merged as a class label. Multitemporal Gaofen 1 satellite (GF-1) remote sensing images were collected over the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China in order to map crop types in 2016 and 2017. Our proposed method was compared with commonly used classifiers (random forest, K-means and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA)). Results demonstrate PCIB and random forest to have the highest classification accuracies, reaching 82% in 2016 in the southwest of Hulin City. In Luobei County in 2016, the accuracies of PCIB and random forest were determined as 81% and 82%, respectively. It can be concluded that the overall accuracy of our proposed method meets the basic requirements of classification accuracy. Despite exhibiting a lower accuracy than that of random forest, PCIB does not require a large field sample size, thus making it more suitable for large-scale crop mapping.
Zhe Ma; Zhe Liu; Yuanyuan Zhao; Lin Zhang; Diyou Liu; Tianwei Ren; Xiaodong Zhang; Shaoming Li. An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. ISPRS International Journal of Geo-Information 2020, 9, 648 .
AMA StyleZhe Ma, Zhe Liu, Yuanyuan Zhao, Lin Zhang, Diyou Liu, Tianwei Ren, Xiaodong Zhang, Shaoming Li. An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. ISPRS International Journal of Geo-Information. 2020; 9 (11):648.
Chicago/Turabian StyleZhe Ma; Zhe Liu; Yuanyuan Zhao; Lin Zhang; Diyou Liu; Tianwei Ren; Xiaodong Zhang; Shaoming Li. 2020. "An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning." ISPRS International Journal of Geo-Information 9, no. 11: 648.
Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase.
Quan Xiong; Xiaodong Zhang; Wei Liu; Sijing Ye; Zhenbo Du; Diyou Liu; Dehai Zhu; Zhe Liu; Xiaochuang Yao. An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase. ISPRS International Journal of Geo-Information 2020, 9, 625 .
AMA StyleQuan Xiong, Xiaodong Zhang, Wei Liu, Sijing Ye, Zhenbo Du, Diyou Liu, Dehai Zhu, Zhe Liu, Xiaochuang Yao. An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase. ISPRS International Journal of Geo-Information. 2020; 9 (11):625.
Chicago/Turabian StyleQuan Xiong; Xiaodong Zhang; Wei Liu; Sijing Ye; Zhenbo Du; Diyou Liu; Dehai Zhu; Zhe Liu; Xiaochuang Yao. 2020. "An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase." ISPRS International Journal of Geo-Information 9, no. 11: 625.
The tassel development status and its branch number in maize flowering stage are the key phenotypic traits to determine the growth process, pollen quantity of different maize varieties, and detasseling arrangement for seed maize production fields. Rapid and accurate detection of tassels is of great significance for maize breeding and seed production. However, due to the complex planting environment in the field, such as unsynchronized growth stage and tassels vary in size and shape caused by varieties, the detection of maize tassel remains challenging problem, and the existing methods also cannot distinguish the early tassels. In this study, based on the time series unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we proposed an algorithm for automatic detection of maize tassels which is suitable for complex scenes by using random forest (RF) and VGG16. First, the RF was used to segment UAV images into tassel regions and non-tassel regions, and then extracted the potential tassel region proposals by morphological method; afterwards, false positives were removed through VGG16 network with the ratio of training set to validation set was 7:3. To demonstrate the performance of the proposed method, 50 plots were selected from UAV images randomly. The precision, recall rate and F1-score were 0.904, 0.979 and 0.94 respectively; 50 plots were divided into early, middle and late tasseling stages according to the proportion of tasseling plants and the morphology of tassels. The result of tassels detection was late tasseling stage > middle tasseling stage > early tasseling stage, and the corresponding F1-score were 0.962, 0.914 and 0.863, respectively. It was found that the model error mainly comes from the recognition of leaves vein and reflective leaves as tassels. Finally, to show the morphological characteristics of tassel directly, we proposed an endpoint detection method based on the tassel skeleton, and further extracted the tassel branch number. The method proposed in this paper can well detect tassels of different development stages, and support large scale tassels detection and branch number extraction.
XuLi Zan; Xinlu Zhang; Ziyao Xing; Wei Liu; Xiaodong Zhang; Wei Su; Zhe Liu; Yuanyuan Zhao; Shaoming Li. Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sensing 2020, 12, 3049 .
AMA StyleXuLi Zan, Xinlu Zhang, Ziyao Xing, Wei Liu, Xiaodong Zhang, Wei Su, Zhe Liu, Yuanyuan Zhao, Shaoming Li. Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sensing. 2020; 12 (18):3049.
Chicago/Turabian StyleXuLi Zan; Xinlu Zhang; Ziyao Xing; Wei Liu; Xiaodong Zhang; Wei Su; Zhe Liu; Yuanyuan Zhao; Shaoming Li. 2020. "Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16." Remote Sensing 12, no. 18: 3049.
Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.
Tianwei Ren; Zhe Liu; Lin Zhang; Diyou Liu; Xiaojie Xi; Yanghui Kang; Yuanyuan Zhao; Chao Zhang; Shaoming Li; Xiaodong Zhang. Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images. Remote Sensing 2020, 12, 2140 .
AMA StyleTianwei Ren, Zhe Liu, Lin Zhang, Diyou Liu, Xiaojie Xi, Yanghui Kang, Yuanyuan Zhao, Chao Zhang, Shaoming Li, Xiaodong Zhang. Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images. Remote Sensing. 2020; 12 (13):2140.
Chicago/Turabian StyleTianwei Ren; Zhe Liu; Lin Zhang; Diyou Liu; Xiaojie Xi; Yanghui Kang; Yuanyuan Zhao; Chao Zhang; Shaoming Li; Xiaodong Zhang. 2020. "Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images." Remote Sensing 12, no. 13: 2140.
Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.
Quan Xiong; Yuan Wang; Diyou Liu; Sijing Ye; Zhenbo Du; Wei Liu; Jianxi Huang; Wei Su; Dehai Zhu; Xiaochuang Yao; Xiaodong Zhang. A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images. Remote Sensing 2020, 12, 450 .
AMA StyleQuan Xiong, Yuan Wang, Diyou Liu, Sijing Ye, Zhenbo Du, Wei Liu, Jianxi Huang, Wei Su, Dehai Zhu, Xiaochuang Yao, Xiaodong Zhang. A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images. Remote Sensing. 2020; 12 (3):450.
Chicago/Turabian StyleQuan Xiong; Yuan Wang; Diyou Liu; Sijing Ye; Zhenbo Du; Wei Liu; Jianxi Huang; Wei Su; Dehai Zhu; Xiaochuang Yao; Xiaodong Zhang. 2020. "A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images." Remote Sensing 12, no. 3: 450.
Seed maize and common maize plots have different planting patterns and variety types. Identification of seed maize is the basis for seed maize growth monitoring, seed quality and common maize seed supply. In this paper, a random forest (RF) classifier is used to develop an approach for seed maize fields’ identification, using the time series vegetation indexes (VIs) calculated from multispectral data acquired from Landsat 8 and Gaofen 1 satellite (GF-1), field sample data, and texture features of Gaofen 2 satellite (GF-2) panchromatic data. Huocheng and Hutubi County in the Xinjiang Uygur Autonomous Region of China were chosen as study area. The results show that RF performs well with the combination of six VIs (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), triangle vegetation index (TVI), ratio vegetation index (RVI), normalized difference water index (NDWI) and difference vegetation index (DVI)) and texture features based on a grey-level co-occurrence matrix. The classification based on “spectrum + texture” information has higher overall, user and producer accuracies than that of spectral information alone. Using the “spectrum + texture” method, the overall accuracy of classification in Huocheng County is 95.90%, the Kappa coefficient is 0.92, and the producer accuracy for seed maize fields is 93.91%. The overall accuracy of the classification in Hutubi County is 97.79%, the Kappa coefficient is 0.95, and the producer accuracy for seed maize fields is 97.65%. Therefore, RF classifier inputted with high-resolution remote-sensing image features can distinguish two kinds of planting patterns (seed and common) and varieties types (inbred and hybrid) of maize and can be used to identify and map a wide range of seed maize fields. However, this method requires a large amount of sample data, so how to effectively use and improve it in areas lacking samples needs further research.
Lin Zhang; Zhe Liu; Tianwei Ren; Diyou Liu; Zhe Ma; Liang Tong; Chao Zhang; Tianying Zhou; Xiaodong Zhang; Shaoming Li. Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sensing 2020, 12, 362 .
AMA StyleLin Zhang, Zhe Liu, Tianwei Ren, Diyou Liu, Zhe Ma, Liang Tong, Chao Zhang, Tianying Zhou, Xiaodong Zhang, Shaoming Li. Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sensing. 2020; 12 (3):362.
Chicago/Turabian StyleLin Zhang; Zhe Liu; Tianwei Ren; Diyou Liu; Zhe Ma; Liang Tong; Chao Zhang; Tianying Zhou; Xiaodong Zhang; Shaoming Li. 2020. "Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier." Remote Sensing 12, no. 3: 362.
Currently, high-temperature risk assessments of crops at the regional scale are usually conducted by comparing the observed air temperature at ground stations or via the remote sensing inversion of canopy temperature (such as MODIS (moderate-resolution imaging spectroradiometer) land surface temperature (LST)) with the threshold temperature of the crop. Since this threshold is based on the absolute temperature value, it is difficult to account for changes in environmental conditions and crop canopy information between different regions and different years in the evaluation model. In this study, MODIS LST products were used to establish an evaluation model (spatiotemporal deviation mean (STDM)) and a classification method to determine maize-growing areas at risk of high temperatures at the regional scale. The study area was the Huang-Huai-Hai River plain of China where maize is grown and high temperatures occur frequently. The spatiotemporal distribution of the high-temperature risk of summer maize was determined in the study area from 2003 to 2018. The results demonstrate the applicability of the model at the regional scale. The distribution of high-temperature risk in the Huang-Huai-Hai region was consistent with the actual temperature measurements. The temperatures in the northwestern, southwestern, and southern parts were relatively high and the area was classified as a stable zone. Shijiazhuang, Jiaozuo, Weinan, Xi’an, and Xianyang city were located in a zone of increasing high temperatures. The regions with a stable high-temperature risk were Xiangfan, Yuncheng, and Luoyang city. Areas of decreasing high temperatures were Handan, Xingtai, Bozhou, Fuyang, Nanyang, Linfen, and Pingdingshan city. Areas that need to focus on preventing high-temperature risks include Luoyang, Yuncheng, Xianyang, Weinan, and Xi’an city. This study provides a new method for the detailed evaluation of regional high-temperature risk and data support.
Xinlei Hu; Zuliang Zhao; Lin Zhang; Zhe Liu; Shaoming Li; Xiaodong Zhang. A High-Temperature Risk Assessment Model for Maize Based on MODIS LST. Sustainability 2019, 11, 6601 .
AMA StyleXinlei Hu, Zuliang Zhao, Lin Zhang, Zhe Liu, Shaoming Li, Xiaodong Zhang. A High-Temperature Risk Assessment Model for Maize Based on MODIS LST. Sustainability. 2019; 11 (23):6601.
Chicago/Turabian StyleXinlei Hu; Zuliang Zhao; Lin Zhang; Zhe Liu; Shaoming Li; Xiaodong Zhang. 2019. "A High-Temperature Risk Assessment Model for Maize Based on MODIS LST." Sustainability 11, no. 23: 6601.
Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1–0.2 and 0.0–0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions.
Wei Su; Zhongping Sun; Wen-Hua Chen; Xiaodong Zhang; Chan Yao; Jiayu Wu; Jianxi Huang; Dehai Zhu. Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images. Remote Sensing 2019, 11, 2409 .
AMA StyleWei Su, Zhongping Sun, Wen-Hua Chen, Xiaodong Zhang, Chan Yao, Jiayu Wu, Jianxi Huang, Dehai Zhu. Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images. Remote Sensing. 2019; 11 (20):2409.
Chicago/Turabian StyleWei Su; Zhongping Sun; Wen-Hua Chen; Xiaodong Zhang; Chan Yao; Jiayu Wu; Jianxi Huang; Dehai Zhu. 2019. "Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images." Remote Sensing 11, no. 20: 2409.
Accurate, year-by-year crop distribution information is a key element in agricultural production regulation and global change governance. However, due to the high sampling costs and insufficient use of historical samples, a supervised classifying method for sampling every year is unsustainable for mapping crop types over time. Therefore, this paper proposes a method for the generation and screening of new samples for 2018 based on historical crop samples, and then it builds a crop mapping model for that current season. Pixels with the same crop type in the historical year (2013–2017) were extracted as potential samples, and their spectral features and spatial information in the current year (2018) were used to generate new samples based on clustering screening. The research result shows that when the clustering number is different, the number and structure of new generated sample also changes. The sample structure generated in Luobei County was not balanced, with the ‘other crop’ representing less than 3.97%, but the structure of southwest Hulin City was more balanced. Based on the newly generated samples and the ground reference data of classified year, the classification models were constructed. The average classification accuracies of Luobei County in 2018 based on new generated samples and field samples were 69.35% and 77.59%, respectively, while those of southwest Hulin City were 80.44% and 82.94%, respectively. Combined with historical samples and the spectral information of the current year, this study proposes a method to generate new samples. It can overcome the problem of crop samples only being collected in the field due to the difficulty of visual interpretation, effectively improve the use of historical data, and also provide a new idea for sustainable crop mapping in many regions lacking seasonal field samples.
Lin Zhang; Zhe Liu; Diyou Liu; Quan Xiong; Ning Yang; Tianwei Ren; Chao Zhang; Xiaodong Zhang; Shaoming Li. Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability 2019, 11, 5052 .
AMA StyleLin Zhang, Zhe Liu, Diyou Liu, Quan Xiong, Ning Yang, Tianwei Ren, Chao Zhang, Xiaodong Zhang, Shaoming Li. Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability. 2019; 11 (18):5052.
Chicago/Turabian StyleLin Zhang; Zhe Liu; Diyou Liu; Quan Xiong; Ning Yang; Tianwei Ren; Chao Zhang; Xiaodong Zhang; Shaoming Li. 2019. "Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China." Sustainability 11, no. 18: 5052.
Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity.
Ziyao Xing; Su; Junming Liu; Xiaodong Zhang. Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake. ISPRS International Journal of Geo-Information 2019, 8, 359 .
AMA StyleZiyao Xing, Su, Junming Liu, Xiaodong Zhang. Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake. ISPRS International Journal of Geo-Information. 2019; 8 (8):359.
Chicago/Turabian StyleZiyao Xing; Su; Junming Liu; Xiaodong Zhang. 2019. "Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake." ISPRS International Journal of Geo-Information 8, no. 8: 359.
An appropriate layout of crop multi-environment trial (MET) sites is imperative for evaluating new crop varieties’ performance in terms of agronomic traits and stress tolerance, and this information is used to determine the utilization value and suitable promotion region of new varieties. Actually, traditional maize test sites have been selected according to the experience of breeding experts, which leads to the strong subjective and unscientific conclusions regarding sites, as well as test results that are not representative of the target population of environments (TPE). Therefore, in this study, we proposed a new method for MET sites layout. Meteorological data, maize growth period data, and county-level maize planting area data were collected for the spatiotemporal classification of a given maize planting region to analyze change rules in the environmental category of each minimum research unit within the study period. If the occurrence frequency of its final attribution category reaches a certain threshold (50%), this minimum research unit is classified as a typical environment region; otherwise, it is classified as an atypical environment region. Then, the number of test sites in each environmental category is allocated by spatial stratified sampling. At last, we establish the optimal test sites layout and a reliability measurement (test adequacy) methods. The practicability of this method was proved by taking the Three Northeastern Provinces of China as the study area. The result shows that there should be 112 test sites in the study area, the distribution of the test sites is uniform, and the environmental representation is high. Test adequacy analysis of the test sites reveals that most of the environmental categories have a test adequacy that reaches 1 in each test period. The method proposed in this paper provides support for the scientific layout of crop varieties test sites and helps to improve the representative and reliability of variety test results while optimizing resources.
XuLi Zan; Zuliang Zhao; Wei Liu; Xiaodong Zhang; Zhe Liu; Shaoming Li; Dehai Zhu. The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment. Sustainability 2019, 11, 3741 .
AMA StyleXuLi Zan, Zuliang Zhao, Wei Liu, Xiaodong Zhang, Zhe Liu, Shaoming Li, Dehai Zhu. The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment. Sustainability. 2019; 11 (13):3741.
Chicago/Turabian StyleXuLi Zan; Zuliang Zhao; Wei Liu; Xiaodong Zhang; Zhe Liu; Shaoming Li; Dehai Zhu. 2019. "The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment." Sustainability 11, no. 13: 3741.
Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.
Wen Zhuo; Jianxi Huang; Li Li; Xiaodong Zhang; Hongyuan Ma; Xinran Gao; Hai Huang; Baodong Xu; Xiangming Xiao. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sensing 2019, 11, 1618 .
AMA StyleWen Zhuo, Jianxi Huang, Li Li, Xiaodong Zhang, Hongyuan Ma, Xinran Gao, Hai Huang, Baodong Xu, Xiangming Xiao. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sensing. 2019; 11 (13):1618.
Chicago/Turabian StyleWen Zhuo; Jianxi Huang; Li Li; Xiaodong Zhang; Hongyuan Ma; Xinran Gao; Hai Huang; Baodong Xu; Xiangming Xiao. 2019. "Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation." Remote Sensing 11, no. 13: 1618.
Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.
Mingzheng Zhang; Dehai Zhu; Wei Su; Jianxi Huang; Xiaodong Zhang; Zhe Liu. Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring. Remote Sensing 2019, 11, 1266 .
AMA StyleMingzheng Zhang, Dehai Zhu, Wei Su, Jianxi Huang, Xiaodong Zhang, Zhe Liu. Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring. Remote Sensing. 2019; 11 (11):1266.
Chicago/Turabian StyleMingzheng Zhang; Dehai Zhu; Wei Su; Jianxi Huang; Xiaodong Zhang; Zhe Liu. 2019. "Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring." Remote Sensing 11, no. 11: 1266.
Variety regional tests based on multiple environments play a critical role in understanding the high yield and adaptability of new crop varieties. However, the current approach mainly depends on experience from breeding experts and is difficulty to promote because of inconsistency between testing and actual situation. We propose a spatial layout method based on the existing systematic regional test network. First, the method of spatial clustering was used to cluster the planting environment. Then, we used spatial stratified sampling to determine the minimum number of test sites in each type of environment. Finally, combined with the factors such as the convenience of transportation and the planting area, we used spatial balance sampling to generate the layout of multi-environment test sites. We present a case study for maize in Jilin Province and show the utility of the method with an accuracy of about 94.5%. The experimental results showed that 66.7% of sites are located in the same county and the unbalanced layout of original sites is improved. Furthermore, we conclude that the set of operational technical ideas for carrying out the layout of multi-environment test sites based on crop varieties in this paper can be applied to future research.
Zuliang Zhao; Liu Zhe; Xiaodong Zhang; XuLi Zan; Xiaochuang Yao; Sijia Wang; Sijing Ye; Shaoming Li; Dehai Zhu. Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province. Sustainability 2018, 10, 1424 .
AMA StyleZuliang Zhao, Liu Zhe, Xiaodong Zhang, XuLi Zan, Xiaochuang Yao, Sijia Wang, Sijing Ye, Shaoming Li, Dehai Zhu. Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province. Sustainability. 2018; 10 (5):1424.
Chicago/Turabian StyleZuliang Zhao; Liu Zhe; Xiaodong Zhang; XuLi Zan; Xiaochuang Yao; Sijia Wang; Sijing Ye; Shaoming Li; Dehai Zhu. 2018. "Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province." Sustainability 10, no. 5: 1424.
Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an intact corn ear allows us to select corn varieties with seeds that have high dehydration speeds in the mature period. We designed a sensor using a ring electrode pair for nondestructive measurement of the kernel moisture in a corn ear based on a high-frequency detection circuit. Through experiments using the effective scope of the electrodes’ electric field, we confirmed that the moisture in the corn cob has little effect on corn kernel moisture measurement. Before the sensor was applied in practice, we investigated temperature and conductivity effects on the output impedance. Results showed that the temperature was linearly related to the output impedance (both real and imaginary parts) of the measurement electrodes and the detection circuit’s output voltage. However, the conductivity has a non-monotonic dependence on the output impedance (both real and imaginary parts) of the measurement electrodes and the output voltage of the high-frequency detection circuit. Therefore, we reduced the effect of conductivity on the measurement results through measurement frequency selection. Corn moisture measurement results showed a quadric regression between corn ear moisture and the imaginary part of the output impedance, and there is also a quadric regression between corn kernel moisture and the high-frequency detection circuit output voltage at 100 MHz. In this study, two corn breeds were measured using our sensor and gave R2 values for the quadric regression equation of 0.7853 and 0.8496.
Han-Lin Zhang; Qin Ma; Li-Feng Fan; Peng-Fei Zhao; Jian-Xu Wang; Xiao-Dong Zhang; De-Hai Zhu; Lan Huang; Dong-Jie Zhao; Zhong-Yi Wang. Nondestructive In Situ Measurement Method for Kernel Moisture Content in Corn Ear. Sensors 2016, 16, 2196 .
AMA StyleHan-Lin Zhang, Qin Ma, Li-Feng Fan, Peng-Fei Zhao, Jian-Xu Wang, Xiao-Dong Zhang, De-Hai Zhu, Lan Huang, Dong-Jie Zhao, Zhong-Yi Wang. Nondestructive In Situ Measurement Method for Kernel Moisture Content in Corn Ear. Sensors. 2016; 16 (12):2196.
Chicago/Turabian StyleHan-Lin Zhang; Qin Ma; Li-Feng Fan; Peng-Fei Zhao; Jian-Xu Wang; Xiao-Dong Zhang; De-Hai Zhu; Lan Huang; Dong-Jie Zhao; Zhong-Yi Wang. 2016. "Nondestructive In Situ Measurement Method for Kernel Moisture Content in Corn Ear." Sensors 16, no. 12: 2196.
Lodging is one of the major problems in maize production which causes severe yield loss every year all over the world. In the present study, the lodging suitability of different maize varieties in target growing environments was investigated based on geographical information science. A total of 401 maize planting counties in northeast China and northern China were selected as study areas. The mean and standard deviation of environment accumulated temperature in vegetative stage of maize were calculated from raw temperature data obtained from 167 meteorology stations in these areas. The variety lodging resistance was determined based on the data of national regional variety trials for maize, and the environment lodging stress was measured using field survey data on lodging. Probability analysis based on the calculated values of environment accumulated temperature in vegetative stage of maize was utilized to determine whether a maize variety can be physically mature in a planting county, and lodging suitability of the variety was evaluated with geographical information science combining variety lodging resistance and local environment lodging stress together. A new maize variety NH1101 was taken as an example to illustrate the modeling and calculating procedures. The result shows that, from southwest to northeast of the study areas, the overall suitability trend changes from nonsuitable to suitable, then to not very suitable. And it is demonstrated that the lodging suitability is not only related to the variety resistance but also to the local environment stress.
Chunqiao Mi; Xiaodong Zhang; Shaoming Li; Jianyu Yang; Dehai Zhu. Evaluation of maize variety suitability on lodging in target environments based on GIS. Annals of GIS 2011, 17, 265 -270.
AMA StyleChunqiao Mi, Xiaodong Zhang, Shaoming Li, Jianyu Yang, Dehai Zhu. Evaluation of maize variety suitability on lodging in target environments based on GIS. Annals of GIS. 2011; 17 (4):265-270.
Chicago/Turabian StyleChunqiao Mi; Xiaodong Zhang; Shaoming Li; Jianyu Yang; Dehai Zhu. 2011. "Evaluation of maize variety suitability on lodging in target environments based on GIS." Annals of GIS 17, no. 4: 265-270.