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Study on Automatic Extraction Algorithm of Farmland Flood Disaster Information Based on Remote Sensing

WANG Laigang, XU Shaobo, LI Shimin, GUO Yan, CHENG Yongzheng, HE Jia

Journal of Henan Agricultural Sciences 2022, 51 (11): 163-170. DOI: 10.15933/j.cnki.1004-3268.2022.11.019

Abstract （799）

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In order to improve the efficiency of information extraction of farmland flood disaster，an effective method of automatic extraction of water body from remote sensing images was explored.The flood disaster in Xunxian County，Henan Province in late July 2021 was taken as the research object，and the NDWI（normalized difference water index），MNDWI（modified normalized difference water index），MBWI（multi‑band water index） and B12 band of Sentinel‑2 remote sensing data were taken as the multi‑dimensional characteristics before，during and after the disaster，and the multi‑dimensional unsupervised water body automatic extraction method was used to extract the water body area.At the same time，Canny‑Edge‑Otsu automatic water body extraction method was used to segment MBWI，MNDWI，NDWI and the HV polarization band of GF‑3 data respectively to extract farmland flood disaster information，and the accuracies of different data sources and methods to extract farmland flood disaster information were compared and analyzed.The results showed that the multi‑dimensional unsupervised water body automatic extraction method integrated various water body indexes and bands as multi‑dimensional features，and the extraction errors of pre‑disaster and disaster water bodies were 6.99% and 7.45% respectively，which were lower than those of Canny‑Edge‑Otsu automatic water body extraction method. By comparing NDWI，MBWI and MNDWI，MBWI had the smallest extraction error and NDWI had the largest extraction error，but buildings and cloud shadow areas were easily mistaken for water bodies.The error of flood extraction based on GF‑3 after the disaster was 15.57%，which was larger than Sentinel‑2 image.However，GF‑3 remote sensing image was not affected by cloud and rain weather，so it provided a strong data support in emergency monitoring of flood disaster.

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Effects of UAV Flight Height on Prediction Model of Plant Nitrogen Accumulation in Winter Wheat

JING Yuhang, GUO Yan, ZHANG Huifang, RONG Yasi, ZHANG Shaohua, FENG Wei, WANG Laigang, HE Jia, LIU Haijiao, ZHENG Guoqing

Journal of Henan Agricultural Sciences 2022, 51 (2): 147-158. DOI: 10.15933/j.cnki.1004-3268.2022.02.018

Abstract （885）

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UAV has the advantage of obtaining crop information quickly，efficiently and non‑destructively，and is widely used in modern agriculture. However，the flight height of UAV directly affects the efficiency of obtaining crop information. In this study，UAV remote sensing images with different resolutions were obtained at the jointing，flowering and filling stages of winter wheat by setting flight altitudes of 30 m， 60 m and 90 m，to explore and analyze the impact of UAV flight altitude on the prediction model of winter wheat plant nitrogen accumulation. Firstly，six vegetation indices（NDVI，RDVI，RERDVI，GBNDVI，OSAVI，EXG）and four texture features（Green‑mean，Green‑sm，Red‑mean，Red‑var）were screened out by correlation and collinearity analysis between different height of vegetation indices，texture features and nitrogen accumulation of winter wheat. Based on the selected vegetation index and texture characteristics，the prediction models of nitrogen accumulation of winter wheat plant were established with vegetation index，texture feature and vegetation index+texture feature by using PLSR and BPNN methods，and the models were cross‑verified at different heights. The stability of the models was analyzed by R²，RMSE and RPD indices. The results showed that the prediction models established with vegetation index，texture feature and vegetation index+texture feature extracted from 30 m UAV images had the best stability by the two methods. The R ² ，RMSE and RPD ranges of the three models with different information were 0.57—0.89，1.27—4.16 g/m2 and 1.67—3.65，respectively. The stability of BPNN model constructed under the three kinds of modeling information was better than that of PLSR on the whole.The R ² and RPD of the verification model were improved in the range of 0.01—0.39 and 0.05—1.44，respectively. RMSE decreased by 0.08—8.53 g/m ².The order of stability of the prediction models for plant nitrogen accumulation was vegetation index+texture feature>vegetation index>texture feature. The vegetation index，texture feature and vegetation index+texture feature of remote sensing images from three UAV flight heights were combined to predict plant nitrogen accumulation，which could improve the estimation accuracy（ R ² ，RMSE and RPD ranges were 0.89—0.93，1.80—2.03 g/m ² and 3.54—4.03，respectively）.Therefore，when considering the efficiency and accuracy，the flight height of UAV should be increased appropriately for efficient nitrogen accumulation prediction with comprehensive utilization of vegetation index and texture feature.

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Tomato Recognition in Yuanmou Hot Area Based on Object‐Oriented GF‐2 Remote Sensing Data

CHEN Yuehao, HE Guangxiong, LI Jie, SHI Liangtao, FANG Haidong, SHI Zhengtao

Journal of Henan Agricultural Sciences 2021, 50 (12): 170-180. DOI: 10.15933/j.cnki.1004-3268.2021.12.020

Abstract （885）

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In order to accurately grasp the spatial distribution information of tomato planting in Yuanmou hot area，realize the goal of rationally adjusting the agricultural structure and the economic scale development of regional characteristic crops，this paper used GF‐2 as the data source，based on the object‐oriented classification idea，and used the ESP scale parameter evaluation tool to evaluate the remote sensing image on segmentation scale.After setting the optimal segmentation scale parameter，the image object was obtained，and then the spectrum，texture and vegetation index were used to construct a variety of recognition schemes，remote sensing recognition of tomatoes in the Yuanmou hot area was implemented by using different classifiers of maximum likelihood method and support vector machine.The best auxiliary recognition feature combination method for tomato information extraction based on GF‐2 data was explored. The results showed that the multi‐feature combination scheme of normalized vegetation index，ratio vegetation index，gray level co‐occurrence matrix and local binary pattern texture constructed based on GF‐2 remote sensing image data in the maximum likelihood method had the highest recognition accuracy for tomatoes，with an overall accuracy of 97.20% and a Kappa coefficient of 0.91；in the support vector machine，the combination with the highest recognition accuracy for tomatoes was the multi‐feature combination scheme based on normalized vegetation index，ratio vegetation index，and gray degree co‐occurrence matrix texture，with an overall accuracy of 96.44% and a Kappa coefficient of 0.87.The overall accuracy of the maximum likelihood method for tomato recognition was higher than that of the support vector machine.The research results indicate that the combination of multiple auxiliary recognition features constructed based on GF‐2 image data objects can realize the fine recognition of Yuanmou tomato.

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FPAR Estimation of Cotton Breeding Material Based on Unmanned Aerial Vehicle（UAV）Multispectral Images

TANG Zhongjie, WANG Laigang, GUO Yan, ZHANG Yan, ZHANG Hongli, YANG Xiuzhong, HE Jia

Journal of Henan Agricultural Sciences 2021, 50 (11): 162-171. DOI: 10.15933/j.cnki.1004-3268.2021.11.019

Abstract （981）

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Rapid，nondestructive and high‑throughput acquisition of photosynthetically active radiation（PAR）information of cotton breeding materials is of great significance to the breeding and cultivation management of cotton varieties with high light efficiency.In this study，a multispectral image acquisition system was built based on the unmanned aerial vehicle（UAV） carring the Micasense RedEdge‑M multispectral imager to obtain the multispectral images from the canopy of cotton breeding materials and extract the reflectivity characteristic parameters.Firstly，based on the multispectral image of cotton breeding material，five channel reflectivity values were extracted from each FPAR（fraction of photosynthetically active radiation）measurement point，including blue，green，red，red edge and near infrared，to construct multispectral variables.Secondly，the quantitative relationship between different multispectral variables and FPAR was analyzed，and the unitary linear regression models and multiple linear regression models of FPAR were established. Finally，the accuracy of the estimation model was verified based on the measured FPAR.The results showed that the multispectral remote sensing images of cotton breeding materials could quickly and intuitively characterize the phenotypic traits such as leaf color and growth status of plant canopy.There was a good correlation between the multispectral variables of transformed soil adjusted vegetation index（TSAVI），soil adjusted vegetation index（SAVI），perpendicular vegetation index（PVI），ratio vegetation index（RVI），difference vegetation index（DVI），enhanced vegetation index（EVI），normalized difference vegetation index（NDVI），atmospherically resistant vegetation index（ARVI）and the FPAR，and the range of | r| was 0.542—0.932. There was a good estimation effect of the unitary linear regression models of the FPAR based on TSAVI，and the R ² and the SE of the estimated model were 0.867 and 0.115，respectively，and the R ²， RPD and RMSE of the verified model were 0.932，2.468 and 0.119，respectively.

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A Method for Monitoring the Critical Growth Stages of Winter Wheat by Using Near‑earth Remote Sensing

WEI Qingwei, ZHU Liming , WANG Fuzhou

Journal of Henan Agricultural Sciences 2021, 50 (10): 163-171. DOI: 10.15933/j.cnki.1004-3268.2021.10.021

Abstract （824）

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The aim is to explore the effective method for monitoring the critical growth stages of winter wheat.Firstly，the device for measuring normalized vegetation index（SRS‑NDVI）was used to monitor time series normalized difference vegetation index（NDVI）of winter wheat growing season in 2017—2018 and 2018—2019 at Hebi Agrometeorological Experimental Station. Then，the neighborhood difference analysis method was used to reconstruct time series normalized difference vegetation index，and the S‑G filtering method（Savitzky‑golay，S‑G）was used to smooth the noise in normalized difference vegetation index time series.Finally，according to the characteristics of normalized difference vegetation index time series，the generalized dynamic threshold method，curve rate method and extreme value method were used to extract the key growth stages of winter wheat. The results showed that the neighborhood difference analysis method could effectively remove obvious abnormal values in normalized difference vegetation index time series. Besides，the normalized difference vegetation index time series processed by S‑G filtering method was more in line with the normalized difference vegetation index change rule of winter wheat.In addition，the average error of the critical growth stages of winter wheat was 2. 5 days，and the accuracy was significantly higher than that extracted by using satellite remote sensing.

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Simulation of Intelligent Internet of Things System Based on High-Level Packet Tracer

ZHANG Yan, LIU Ting, BAO Zhuoya, WANG Laigang, HE Jia, GUO Yan, ZHANG Hongli, YANG Xiuzhong

Journal of Henan Agricultural Sciences 2021, 50 (6): 163-170. DOI: 10.15933/j.cnki.1004-3268.2021.06.020

Abstract （788）

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In order to compare differences between Sentinel-2and GF-6 WFV imagery in crops identification,based on Sentinel-2 and GF-6 WFV remote sensing data,this study used K nearest neighbor and maximum likelihood classification methods to extract peanut planting area in Yulin Township,Xuchang City,and constructed confusion matrix by ground sample points for accuracy verification.The relative errors of the extracted peanut planting area were compared based on the measured data.The results showed that two classification methods were effective in extracting peanut planting area from two data sources and could meet the actual needs. The mapping accuracy was above 85%,the user accuracy was above 80%,and the relative error was within 10%. Peanut planting area was mainly concentrated in the northwest and southeast regions,and there were a few sporadic distributions in the northeast and southwest regions.By comparison,the object-oriented K nearest neighbor method could better avoid pixel mis-segmentation and leakage-segmentation problems in complex terrain area.K nearest neighbor method was superior to traditional pixel-based maximum likelihood classification in terms of overall accuracy,Kappa coefficient,peanut planting area mapping accuracy,user accuracy,and relative error.In terms of different classification methods of the same data source,the classification accuracy of two data sources using K nearest neighbor method was higher than the maximum likelihood classification.It showed that compared to the pixel-based classification method,K nearest neighbor method could make full use of the spectrum and texture feature,and obtain higher extraction accuracy. In terms of the same method and different data sources,the extraction accuracy of the peanut planting area based on Sentinel-2 by the maximum likelihood method was lower than that of GF-6 WFV,and the extraction accuracy of the peanut planting area based on Sentinel-2 by the K neighbor method was higher than that of GF-6 WFV.Because the spatial resolution of Sentinel-2 data is higher than that of GF-6 WFV,and the detail expression is better,it is more suitable to extract small-scale areas with complex planting structures.

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Drought Monitoring of Spring Maize in Northeast China Based on TVDI———Taking the 2018 for Instance

WANG Yihao, WU Yongfeng, ZHANG Liting, ZHANG Jinshui, LI Chongrui, ZHANG Xiaoxu

Journal of Henan Agricultural Sciences 2020, 49 (3): 167-180. DOI: 10.15933/j.cnki.1004-3268.2020.03.022

Abstract （538）

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In order to explore the difference of sensitivity and accuracy of temperature vegetation drought index(TVDI) calculated by different vegetation indices in monitoring spring maize drought affected by the development process and farmland environment,and to reveal the spatial-temporal pattern in drought and its variation during the growing period of spring maize of northeast China in 2018,TVDI with strong sensitivity and high accuracy was selected to monitor drought of spring maize in the growing season.Based on the reconstruction of normalized difference vegetation index( NDVI),enhanced vegetation index(EVI)and land surface temperature(LST)which was corrected by digital elevation model (DEM),LST-VI wasconstructed,and temperature vegetation drought index(TVDI-N,TVDI-E) was calculated. Besides,the best monitoring period and sensitive translating period of TVDI-N and TVDI-E to monitor drought were determined by the difference of constant-coefficient.What’ s more,according to ground survey,the data monitoring accuracies of TVDI-N and TVDI-E were analyzed that affected by the development stage of spring maize,soil texture,altitude,slope and other farmland environments.The results showed that,at the early growing stage of spring maize,the TVDI-N dry-edge fitting equation constant-coefficient was consistently higher than TVDI-E,the maximum difference was 0.44 while the minimum difference was 0.10,and the average difference was 0.23.The judgment ratio of TVDI-N and TVDI-E monitoring drought grade to the measured drought grade was 100.0%.However, the consistence between the TVDI-N monitored drought grade and the measured drought grade was 33.3 perentage points higher than TVDI-E,which meant that it owned stronger monitoring sensitivity and higher monitoring accuracy in this period.In the middle stage of spring maize growth,although there was no obvious feature space that one dry-edge fitting equation constant-coefficient was consistently higher or lower than another,the judgment ratio of TVDI-N monitoring drought grade was lower than that of TVDI-E about 12.5 percentage points and the uniformity was the same as TVDI-E.Though this stage was a sensitive transition period,however,TVDI-E monitoring accuracy was higher than TVDI-N. At the later stage of spring maize growth,the TVDI-N dry-edge fitting equation constant-coefficient was consistently lower than TVDI-E,and the maximum difference was 0.29 while the minimum difference was 0.13,the average difference was 0.18.The judgment ratio and uniformity of TVDI-E monitored drought grade and measured drought grade were higher than TVDI-N 8.7 percentage points and 39.2 percentage points.That meant,TVDI-E drought monitoring sensitivity and accuracy were higher than TVDI-N in this period.Using different TVDI which had higher monitoring accuracy in each development stage of spring maize to monitor drought in the growing season of spring maize in 2018,the process of occurrence, development and reduction of drought in 2018 and the influence of drought on the development stages of spring maize in the study area were undrstood.Besides,in early stage of spring maize growth judgment ratio reached 100. 0% and the uniformity reached 83.3%.The drought monitoring judgment ratio in middle and later stage of spring maize growth was also obtained.The judgment ratio reached 82.6% and the uniformity reached 78.3%.By analyzing the sensitivity and accuracy of TVDI-N and TVDI-E in drought monitoring at different developmental stages of spring maize,it came to the conclusion that TVDI-N was suitable for drought monitoring at early spring maize growth stage while TVDI-E was suitable for drought monitoring at middle and later spring maize growth stage. Based on the conclusion,a lasting drought monitoring was made in spring maize growing season of northeast China in 2018,which could reveal the spatial and temporal pattern of drought of spring maize and improve the monitoring accuracy.

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