&nbsp;作物病虫害遥感监测和预测预警研究进展

doi:10.11873/j.issn.1004-0323.2019.1.0021

鎽樿/Abstract

鎽樿锛�

鍗卞涓ラ噸鐨勭梾铏鑳佽揩甯稿湪鎴戝浗浣滅墿涓讳骇鍖哄彂鐢燂紝妞嶄繚閮ㄩ棬鐨勭敯闂磋皟鏌ャ�佸疄鍦板彇鏍风瓑娴嬫姤鏂瑰紡宸茬粡鏃犳硶婊¤冻鐩墠绮惧噯銆佹棤鎹熴�侀珮鏁堢殑鐩戞祴棰勮闇�姹傘�傝兘澶熷疄鏃跺姩鎬佺洃娴嬬殑閬ユ劅鎶�鏈墜娈典负蹇�熻幏鍙栧湴琛ㄨ繛缁俊鎭彁渚涗簡鍙兘锛屼篃鏄湭鏉ヤ綔鐗╃梾铏閬ユ劅鐩戞祴棰勬祴鐨勪富瑕佸彂灞曟柟鍚戙�傞�氳繃鎬荤粨銆佸綊绾冲拰鏁寸悊鐩墠浣滅墿鐥呰櫕瀹抽仴鎰熷簲鐢ㄤ腑涓嶅悓鐥呰櫕瀹宠儊杩被鍨嬪尯鍒嗐�佸崟涓�鑳佽揩绋嬪害浼扮畻鍜屼綔鐗╄儊杩娴嬮璀︾殑涓夊ぇ涓昏鏂瑰悜鐨勭爺绌剁幇鐘讹紝闃愯堪浜嗙幇鏈夌爺绌朵腑浣跨敤鐨勭壒寰佹彁鍙栨柟娉曘�佺壒寰侀�夋嫨鏂规硶锛屼互鍙婅儊杩被鍨嬪尯鍒嗐�佺▼搴︿及绠楀拰棰勬祴棰勮鐨勬ā鍨嬬畻娉曪紝骞堕�氳繃鍥藉唴妫�绱㈠钩鍙板涓夊ぇ绮浣滅墿鐥呰櫕瀹崇殑閬ユ劅鐮旂┒搴旂敤鎯呭喌杩涜浜嗙粺璁″垎鏋愩�傚湪姝ゅ熀纭�涓婃帰璁ㄤ綔鐗╃梾铏閬ユ劅鐩戞祴鍜岄娴嬮璀︾幇瀛樼殑闂鍜屾湭鏉ョ殑鍙戝睍瓒嬪娍锛屾帹鍔ㄥ啘涓氬彲鎸佺画鎬х殑闀挎晥浣撳埗鐨勬瀯寤恒��

鍏抽敭璇�: 浣滅墿鐥呰櫕瀹�, 閬ユ劅鐩戞祴, 棰勬祴棰勮, 鏂规硶涓庢ā鍨�

Abstract: Crop diseases and pests are the first natural biological hazards that threaten food production and quality.The investigation and sampling in field of plant protection department can鈥檛 meet demand of the accurate锛宯on-destructive and efficient monitoring and warning.Currently锛宺emote sensing which can monitor dynamically in real time provides the possibility for the rapid acquisition of continuous surface information锛宎nd is also the main development direction monitoring and prediction of crop diseases and pests in the future.Research status of three main directions锛宨ncluding classification of different stresses锛宻everity estimation and stress forecasting锛宎re summarized锛宎nd the methods of feature extraction锛宖eature selection锛宎nd algorithms are expounded.Then锛宼he application of diseases and pests of three major foodsby remote sensing was analyzed by means of domestic retrieval platforms.On this basis锛宼he existing problems and future development trend of monitoring and forecasting of crop diseases and pests by remote sensing are discussed to promotethe long-term mechanism of agricultural sustainable development.

Key words: , Crop pests and diseases, Remote sensing monitoring, Forecasting and early warning, Methods and models

涓浘鍒嗙被鍙�:

S127
S431

椴佸啗鏅�, 瀛欓浄鍒�, 榛勬枃姹�. 浣滅墿鐥呰櫕瀹抽仴鎰熺洃娴嬪拰棰勬祴棰勮鐮旂┒杩涘睍顎僛J]. 閬ユ劅鎶�鏈笌搴旂敤, 2019, 34(1): 21-32.

Lu Junjing, Sun Leigang, Huang Wenjiang. Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing[J]. Remote Sensing Technology and Application, 2019, 34(1): 21-32.

鍙傝�冩枃鐚�

[1]Huo Zhiguo锛孡iu Wancai锛孲hao Zhenrun锛宔t al.On Developing Long Term Meteorological Prediction Research of Crop Pests and Diseases Prevailing in China[J].Journal of Natural Disasters锛�2000锛�9(1)锛�117-121.[闇嶆不鍥斤紝鍒樹竾鎵嶏紝閭垫尟娑︼紝绛�.璇曡寮�灞曚腑鍥藉啘浣滅墿鐥呰櫕瀹冲嵄瀹虫祦琛岀殑闀挎湡姘旇薄棰勬祴鐮旂┒[J].鑷劧鐏惧瀛︽姤锛�2000锛�9(1)锛�117-121.] [2]Huang Wenjiang锛孼hang Jincheng锛孡uo Juhua锛宔t al.Remote Sensing Monitoring and Forecasting of Crop Pests and Diseases[M].Beijing锛歋cience Press锛�2015.[榛勬枃姹燂紝寮犵珵鎴愶紝缃楄強鑺憋紝绛�.浣滅墿鐥呰櫕閬ユ劅鐩戞祴涓庨娴媅M].鍖椾含锛氱瀛﹀嚭鐗堢ぞ锛�2015.] [3]Lu Junjing锛孒uang Wenjiang锛孼hang Jingcheng锛宔t al.Quantitative Identification of Yellow Rust and Powdery Mildew in Winter Wheat based on Wavelet Feature[J].Spectroscopy and Spectral Analysis锛�2016锛�36(6)锛�1854-1858.[椴佸啗鏅紝榛勬枃姹燂紝寮犵珵鎴愶紝绛�.鍩轰簬灏忔尝鐗瑰緛鐨勫皬楹︾櫧绮夌梾涓庢潯閿堢梾鐨勫畾閲忚瘑鍒爺绌禰J].鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛�2016锛�36(6)锛�1854-1858.] [4]Sankaran S锛孧ishra A锛孍hsani R锛宔t al.A Review of Advanced Techniques for Detecting Plant Diseases[J].Computers and Electronics in Agriculture锛�2010锛�72(1)锛�1-13. [5]Yuan Lin锛孼hang Jingcheng锛孼hao Jinlin锛宔t al.Differentiation of Yellow Rust and Powdery Mildew in Winter Wheat and Retrieving of Disease Severity based on Leaf Level Spectral Analysis[J].Spectroscopy and Spectral Analysis锛�2013锛�33(6)锛�1608-1614.[琚佺惓锛屽紶绔炴垚锛岃档鏅嬮櫟锛岀瓑.鍩轰簬鍙剁墖鍏夎氨鍒嗘瀽鐨勫皬楹︾櫧绮夌梾涓庢潯閿堢梾鍖哄垎鍙婄梾鎯呭弽婕旂爺绌禰J].鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛�2013锛�33(6)锛�1608-1614.] [6]Guan Q S锛孒uang W J锛孼hao J L锛宔t al.Quantitative Identification of Yellow Rust锛孭owdery Mildew and Fertilizer-water Stress in Winter Wheat Using In-situ Hyperspectral Data[J].Sensor Letters锛�2014锛�12锛�1-7. [7]Zhao J L锛孼hang D Y锛孡uo J H锛宔t al.A Comparative Study on Monitoring Leaf-scale Wheat Aphids Using Pushbroom Imaging and Non-imaging ASD Field Spectrometers[J].International Journal of Agriculture and Biology锛�2012锛�14(1)锛�136-140. [8]Qiao Hongbo锛孲hi Yue锛孲i Haiping锛宔t al.Monitoring and Classification of Wheat Take-all in Field based on Imaging Spectrometer[J].Transactions of the Chinese Society of Agricultural Engineering.2014锛�3(20)锛�172-178.[涔旀椽娉紝甯堣秺锛屽徃娴峰钩锛岀瓑.鍩轰簬杩戝湴鎴愬儚鍏夎氨鐨勫皬楹﹀叏铓�鐥呯瓑绾х洃娴媅J].鍐滀笟宸ョ▼瀛︽姤锛�2014锛�3(20)锛�172-178.] [9]Jonas F锛孧enz G.Multi-temporal Wheat Disease Detection by Multi-spectral Remote Sensing[J].Precision Agriculture锛�2007锛�8(3)锛�161-172. [10]Yuan Lin.Identification and Differentiation of Wheat Diseasesand Insects with Multi-source and Multi-scale Remote Sensing Data[D].Hangzhou锛歓hejiang University锛�2015.[琚佺惓.灏忛害鐥呰櫕瀹冲灏哄害閬ユ劅璇嗗埆鍜屽尯鍒嗘柟娉曠爺绌禰D].鏉窞锛氭禉姹熷ぇ瀛︼紝2015.] [11]Huang Muyi锛孒uang Wenjiang锛孡iu Liangyun锛宔t al.Spectral Reflectance Feature of Winter Wheat Single Leaf Infected with Stripe Rust and Severity Level Inversion[J].Transactions of the Chinese Society of Agricultural Engineering锛�2004锛�20(1)锛�176-180.[榛勬湪鏄擄紝榛勬枃姹燂紝鍒樿壇浜戯紝绛�.鍐皬楹︽潯閿堢梾鍗曞彾鍏夎氨鐗规�у強涓ラ噸搴﹀弽婕擺J].鍐滀笟宸ョ▼瀛︽姤锛�2004锛�20(1)锛�176-180.] [12]Graeff S锛孡ink J锛孋laupein W.Identification of Powdery Mildew and Take-all Disease in Wheat by Means of Leaf Reflectance Measurements[J].Central European Journal of Biology锛�2006锛�1(2)锛�275-288. [13]Zang Hongting.Monitoring and Evaluation the Spatial and Temporal Dynamic Changesof Corn Armyworm based on Remote Sensing Data[D].Harbin锛歂ortheast Agricultural University锛�2014.[鑷х孩濠�.鐜夌背绮樿櫕鏃剁┖鍔ㄦ�侀仴鎰熺洃娴嬩笌璇勪环[D].鍝堝皵婊細涓滃寳鍐滀笟澶у锛�2014.] [14]Liu Zhanyu.Monitoring the Rice Disease and Insect Stress with Remote Sensing[D].Hangzhou锛歓hejiang University锛�2008.[鍒樺崰瀹�.姘寸ɑ涓昏鐥呰櫕瀹宠儊杩仴鎰熺洃娴嬬爺绌禰D].鏉窞锛氭禉姹熷ぇ瀛︼紝2008.] [15]Yang C M锛孋heng C H.SpectralCharacteristics of Rice Plants Infested by Brown Plant Hoppers[J].Proceedings of the National Science Council Republic of China Part B Life Sciences锛�2001锛�25(3)锛�180-186. [16]Yang C M锛孋heng C H锛孋hen R K.Changes inSpectral Characteristics of Rice Canopy Infested with Brown Plant Hopper and Leaf Folder[J].Crop Science锛�2007锛�47(1)锛�329-335. [17]Jones C D锛孞ones J B锛孡ee W S.Diagnosis of Bacterial Spot Tomato Using Spectral Signatures[J].Computers and Electronics in Agriculture锛�2010锛�74(2)锛�329-335. [18]Jiang Jinbao锛孋hen Yunhao锛孒uang Wenjiang.Using Hyperspectral Derivative Index to Monitor Winter Wheat Disease[J].Spectroscopy and Spectral Analysis锛�2007锛�27(12)锛�2475-2479.[钂嬮噾璞癸紝闄堜簯娴╋紝榛勬枃姹�.鐢ㄩ珮鍏夎氨寰垎鎸囨暟鐩戞祴鍐皬楹︾梾瀹崇殑鐮旂┒[J].鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛�2007锛�27(12)锛�2475-2479.] [19]Huang W J锛孒uang M Y锛孡iu L Y锛宔t al.Inversion of the severity of Winter Wheat Yellow Rust Using Proper Hyper Spectral Index[J].Transactions of the Chinese Society of Agricultural Engineering.2005锛�21(4)锛�97-103. [20]Huang W J锛孡amb D W锛孨iu Z锛宔t al.Identification of Yellow Rust in Wheat Using In-situ Spectral Reflectance Measurements and Airborne Hyperspectral Imaging[J].Precision Agriculture锛�2007锛�8(5)锛�187-197. [21]Lu Junjing锛孒uang Wenjiang锛孞iang Jinbao锛宔t al.Comparison of Wavelet Features and Conventional Spectral Features on Estimating Severity of Stripe Rust in Winter Wheat[J].Journal of Triticeae Crops锛�2015锛�35(10)锛�1456-1461.[椴佸啗鏅紝榛勬枃姹燂紝钂嬮噾璞癸紝绛�.灏忔尝鐗瑰緛涓庝紶缁熷厜璋辩壒寰佷及娴嬪啲灏忛害鏉￠攬鐥呯梾鎯呬弗閲嶅害鐨勫姣旂爺绌禰J].楹︾被浣滅墿瀛︽姤锛�2015锛�35(10)锛�1456-1461.] [22]Sun Jiayi.Sensitivity of Hyperspectral Reflectance to Monitor Rice Pests and the Monitor Methods for Rice Plant Hoppers[D].Nanjing锛歂anjing Agricultural University锛�2013.[瀛欏槈鎬�.姘寸ɑ鍙剁墖楂樺厜璋辫櫕瀹崇殑鏁忔劅鎬у強绋婚铏辩殑涓哄鐩戞祴[D].鍗椾含锛氬崡浜啘涓氬ぇ瀛︼紝2013. [23][JP2]Zhang Jingcheng.Methods for Information Extraction of Wheat [JP]Disease based on Multi-source Remote Sensing Data[D].Hangzhou锛歓hejiang University锛�2012.[寮犵珵鎴�.澶氭簮閬ユ劅鏁版嵁灏忛害鐥呭淇℃伅鎻愬彇鏂规硶鐮旂┒[D].鏉窞锛氭禉姹熷ぇ瀛︼紝2012.] [24]Qiao Hongbo锛孧a Xinming锛孋heng Dengfa锛宔t al.Detecting Infestation of Take-all Disease in Winter Wheat Using TM Image[J].Journal of Triticeae Crops锛�2009锛�29(4)锛�716-720.[涔旂孩娉紝椹柊鏄庯紝绋嬬櫥鍙戯紝绛�.鍩轰簬TM褰卞儚鐨勫皬楹﹀叏铓�鐥呭嵄瀹充俊鎭彁鍙朳J].楹︾被浣滅墿瀛︽姤锛�2009锛�29(4)锛�716-720.] [25]Luo Juhua.Monitoring and Predicting of Aphid based on Multi-source Remote Sensing Data[D].Beijing锛欱eijing Normal University锛�2012.[缃楄強鑺�.鍩轰簬澶氭簮鏁版嵁鐨勫皬楹﹁殰铏仴鎰熺洃娴嬮娴嬬爺绌禰D].鍖椾含锛氬寳浜笀鑼冨ぇ瀛︼紝2012.] [26]Lu Junjing.Remote Sensing Monitoring of Powdery Mildew and Yellow Rust in Winter Wheat based on Multi-source Data[D].Beijing锛欳hina University of Mining & Technology锛�2016.[椴佸啗鏅�.澶氭簮鏁版嵁鍐皬楹︾櫧绮夌梾鍜屾潯閿堢梾閬ユ劅鐩戞祴鐮旂┒[D].鍖椾含锛氫腑鍥界熆涓氬ぇ瀛︼紝2016.] [27]Ma Huiqin锛孒uang Wenjiang锛孞ing Yuanshu锛宔t al.Remote Sensing Monitoring of Wheat Powdery Mildew based on AdaBoost Model Combining mRMR Algorithm[J].Transactions of the Chinese Society of Agricultural Engineering锛�2017锛�33(5)锛�162-169.[椹収鐞达紝榛勬枃姹燂紝鏅厓涔︼紝绛�.鍩轰簬AdaBoost妯″瀷鍜宮RMR绠楁硶鐨勫皬楹︾櫧绮夌梾閬ユ劅鐩戞祴[J].鍐滀笟宸ョ▼瀛︽姤锛�2017锛�33(5)锛�162-169.] [28]Nie Chenwei锛孻uan Lin锛學ang Baotong锛宔t al.Monitoring Wheat Powdery Mildew based on Integrated Remote Sensing and Meteorological Information[J].Acta Phytopathologica Sinica锛�2016锛�46(2)锛�285-288.[鑱傝嚕宸嶏紝琚佺惓锛岀帇淇濋�氾紝绛�.缁煎悎閬ユ劅涓庢皵璞′俊鎭殑灏忛害鐧界矇鐥呯洃娴嬫柟娉昜J].妞嶇墿鐥呯悊瀛︽姤锛�2016锛�46(2)锛�285-288.] [29]Ma Ning锛孧eng Zhijun锛學ang Pei锛宔t al.Research Summary on Forecasting Methods of Crop Pests and Diseases[J].Journal of Heilongjiang Bayi Agricultural University锛�2016锛�28(1):15-18.[椹畞锛屽瓱蹇楀啗锛岀帇鍩癸紝绛�.鍐滀綔鐗╃梾铏棰勬姤鏂规硶鐮旂┒缁艰堪[J].榛戦緳姹熷叓涓�鍐滃灕澶у瀛︽姤锛�2016锛�28(1)锛�15-18.] [30]Scherm H锛孻ang X B.Atmospheric Teleconnection Patterns Associated with Wheat Stripe Rust Disease in North China[J].International Journal of Biometeorology锛孊erlin锛孏ermany锛�1998.42(1)锛�28-33. [31]Maelzer D A锛孼alucki M P.Long Range Forecasts of the Numbers of Helicoverpa Punctigera and H.armigera(Lepidoptera锛歂octuidae) in Australia Using the Southern Oscillation Index and the Sea Surface Temperature[J].Bulletin of Entomological Research.2000锛�90(2)锛�133-146. [32]Huo Zhiguo锛孻e Cailing锛孮ian Shuan锛宔t al.Relationship between Climatic Anomaly and Prevailling of the Wheat Powdery Mildew in China[J].Journal of Natural Disasters锛�2002锛�11(1)锛�85-90.[闇嶆不鍥斤紝鍙跺僵鐜诧紝閽辨爴锛岀瓑.姘斿�欏紓甯镐笌涓浗灏忛害鐧界矇鐥呯伨瀹虫祦琛屽叧绯荤殑鐮旂┒[J].鑷劧鐏惧瀛︽姤锛�2002锛�11(1)锛�85-90.] [33]Yang Hongsheng锛孞i Rong锛學ang Ting.Atmospheric Circulation Background and Long-term Prediction of Grasshopper Occurrence in Xinjiang[J].Chinese Journal of Ecology锛�2008锛�27(2)锛�218-222.[鏉ㄦ椽鍗囷紝瀛ｈ崳锛岀帇濠�.鏂扮枂铦楄櫕鍙戠敓鐨勫ぇ姘旂幆娴佽儗鏅強闀挎湡棰勬祴[J].鐢熸�佸鏉傚織锛�2008锛�27(2)锛�218-222.] [34]Strand J F.Some Agrometeorological Aspects of Pest and Disease Management for the 21顎嬵�坰t顎� Century[J].Agricultural and Forest Meteorology锛�2000锛�103(1-2)锛�73-82. [35]Baker K M锛孠irk W W.Comparative Analysis of Models Integrating Synoptic Forecast Data into Potato Late Blight Risk Estimate Systems[J].Computers and Electronics in Agriculture锛�2007锛�57(1)锛�23-32. [36]Tan W Z锛孡i C W锛孊i C W锛宔t al.A Computer Software-Epitimulator顎嬵摫 for Simulating Temporal Dynamics of Plant Disease Epidemic Progress[J].Agricultural Sciences in China锛�2010锛�9(2)锛�242-248. [37]Zhang Lei.Variation and Regional Dynamic Warning of Crop Disease and Pests under Climate Change[D].Beijing锛欳hinese Academy of Meteorological Sciences锛�2013.[寮犺暰.姘斿�欏彉鍖栬儗鏅笅鍐滀綔鐗╃梾铏鐨勫彉鍖栧強鍖哄煙鍔ㄦ�侀璀︾爺绌禰D].鍖椾含锛氫腑鍥芥皵璞＄瀛︾爺绌堕櫌锛�2013.] [38]Tang Cuicui锛孒uang Wenjiang锛孡uo Juhua锛宔t al.Forecasting Wheat Aphid with Remote Sensing based on Relevance Vector Machine[J].Transactions of the Chinese Society of Agricultural Engineering锛�2015锛�31(6)锛�201-207.[鍞愮繝缈狅紝榛勬枃姹燂紝缃楄強鑺憋紝绛�.鍩轰簬鐩稿叧鍚戦噺鏈虹殑鍐皬楹﹁殰铏仴鎰熼娴媅J].鍐滀笟宸ョ▼瀛︽姤锛�2015锛�31(6)锛�201-207.] [39]Dutta S锛孲ingh S K锛孠hullar M.A Case Study on Forewarning of Yellow Rust Affected Areas on Wheat Crop Using Satellite Data[J].Journal of the Indian Society of Remote Sensing锛�2014锛�42(2)锛�335-342. [40]Ma Huiqin锛孒uang Wenjiang锛孞ing Yuanshu.Wheat Powdery Mildew Forecasting in Filling Stage based on Remote Sensing and Meteorological Data[J].Transactions of the Chinese Society of Agricultural Engineering锛�2016锛�32(9)锛�165-172.[椹収鐞达紝榛勬枃姹燂紝鏅厓涔�.閬ユ劅涓庢皵璞℃暟鎹粨鍚堥娴嬪皬楹︾亴娴嗘湡鐧界矇鐥匸J].鍐滀笟宸ョ▼瀛︽姤锛�2016锛�32(9)锛�165-172.] [41]Delwiche S R锛孠im M S.Hyperspectral Imaging for Detection of Scab in Wheat[C]鈭nvironmental and Industrial Sensing锛欱iological Quality and Precision Agriculture 鈪★紝Proceedings of SPIE锛�2000锛�4203锛�13-20. [42]Moshou D锛孊ravo C锛學est J锛宔t al.Automatic Detection of Yellow Rust in Wheat Using Reflectance Measurements and Neural Networks[J].Computers and Electronics in Agriculture锛�2004锛�44锛�173-188. [43]Liu Liangyun锛孒uang Muyi锛孒uang Wenjiang锛宔t al.Monitoring Stripe Rust Disease of Winter Wheat Using Multi-temporal Hyperspectral Airborne Data[J].Journal of Remote Sensing锛�2004锛�8(3)锛�275-281.[鍒樿壇浜戯紝榛勬湪鏄擄紝榛勬枃姹燂紝绛�.鍒╃敤澶氭椂鐩哥殑楂樺厜璋辫埅绌哄浘鍍忕洃娴嬪啲灏忛害鏉￠攬鐥�.閬ユ劅瀛︽姤锛�2004锛�8(3)锛�275-281.] [44]Liu Z Y锛學u H F锛孒uang J F.Application of Neural Networks to Discriminate Fungal Infection Levels in Rice Panicles Using Hyperspectral Reflectance and Principal Components Analysis[J].Computers and Electronics in Agriculture锛�2010锛�70(2)锛�99-106. [45]Demetriades-Shah T H锛孲teven M D锛孋lark J A.High Resolution Derivative Spectra in Remote Sensing[J].Remote Sensing of Environment锛�1990锛�33(1)锛�55-64. [46][JP2]Gamon J A锛孭enuelas J锛孎ield C B.A Narrow-waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Ef顣攃iency[J].Remote Sensing of Environment锛�1992锛�41锛�35-44.[JP] [47]Naidu R A锛孭erry E M锛孭ierce F J锛宔t al.The Potential of Spectral Reflectance Technique for the Detection of Grapevine Leaf Roll-associated Virus-3 in Two Red-berried Wine Grape Cultivars[J].Computers and Electronics in Agriculture锛�2009锛�66(1)锛�38-45. [48]Huang W J锛孏uan Q S锛孡uo J H锛宔t al.New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing锛�2014锛�7(6)锛�2516-2524. [49]Cheng T锛孯ivard B锛孲anchenz-azofeifa G A.Spectroscopic Determination of Leaf Water Content Using Continuous Wavelet Analysis[J].Remote Sensing of Environment锛�2011锛�115(2)锛�662. [50]Cheng T锛孯ivard B锛孲谩nchez-Azofeifa A锛宔t al.Continuous Wavelet Analysis for the Detection of Green Attack Damage due to Mountain Pine Beetle Infestation[J].Remote Sensing of Environment锛�2010锛�114(4)锛�899-910. [51]Zhang J C锛孻uan L锛孭u R L锛宔t al.Comparison between Wavelet Spectral Features and Conventional Spectral Features in Detecting Yellow Rust for Winter Wheat[J].Computers and Electronics in Agriculture锛�2014锛�100锛�79-87. [52]Shi Y锛孒uang W J锛孼hou XF锛宔t al.Evaluation of Wavelet Spectral Features in Pathological Detection and Discrimination of Yellow Rust and Powdery Mildew in Winter Wheat with Hyperspectral Reflectance Data[J].Journal of AppliedRemote Sensing锛�2017锛�11(2)锛�026025. [53]Shi Y锛孒uang W J锛孏onz谩lez-Moreno P锛宔t al.Wavelet-based Rust Spectral Feature Set(WRSFs)锛欰 Novel Spectral Feature Set based on Continuous Wavelet Transformation for Tracking Progressive Host-Pathogen Interaction of Yellow Rust on Wheat[J].Remote Sensing锛�2018锛�10(4)锛�525. [54]Huang W J锛孡u J J锛孻e H C锛宔t al.Quantitative Identification of Crop Disease and Nitrogen-water Stress in Winter Wheat Using Continuous Wavelet Analysis[J].International Journal of Agricultural and Biological Engineering锛�2018锛�11(2)锛�145-152. [55]Xie Fei.Research and Implementation of Image Texture Feature Extraction and Image Classification System[D].Beijing锛歎niversity of Electronic Science and Technology of China锛�2009.[璋㈣彶.鍥惧儚绾圭悊鐗瑰緛鐨勬彁鍙栧拰鍥惧儚鍒嗙被绯荤粺鐮旂┒鍙婂疄鐜癧D].鍖椾含锛氱數瀛愮鎶�澶у锛�2009.] [56]Zhu Yun.The Detection of Fruit Tree Pests based on Digital Image Processing[D].Zhengzhou锛歂orth China University of Water Resources and Electric Power锛�2012.[鏈变簯.鍩轰簬鏁板瓧鍥惧儚澶勭悊鐨勬灉鏍戠梾铏鏅鸿兘鍖栨娴媅D].閮戝窞锛氬崕鍖楁按鍒╂按鐢靛闄紝2012.] [57]Bu Yadong.Research of Image Texture Feature Extraction[D].Ji鈥檔an锛歋handong Normal University锛�2012.[姝ヤ簹涓�.鍥惧儚绾圭悊鐗瑰緛鎻愬彇鐨勭爺绌禰D].娴庡崡锛氬北涓滃笀鑼冨ぇ瀛︼紝2012.] [58]Guo Qing锛學ang Liwen锛孌ong Fangmin锛宔t al.Identification of Wheat Stripe Rust and Powdery Mildew Using Orientation Coherence Feature[J].Transactions of the Chinese Society of Agricultural Machinery锛�2015锛�46(1)锛�26-34.[閮潚锛岀帇楠婇洴锛岃懀鏂规晱锛岀瓑.鍩轰簬鏂瑰悜涓�鑷存�х壒寰佺殑灏忛害鏉￠攬鐥呬笌鐧界矇鐥呰瘑鍒玔J].鍐滀笟鏈烘瀛︽姤锛�2015锛�46(1)锛�26-34.] [59]Chestmore D锛孊ernard T锛孖nman A J锛宔t al.Image Analysis for the Identification of the Quarantine Pest Tilletia Indica[J].EPPO Bulletin锛�2003锛�3(3)锛�495-499. [60]Ahmad I S锛孯eid J F.Evaluation of Colour Representations for Maize Images[J].Journal of Agricultural Engineering Research锛�1996锛�63锛�185-196. [61]Martin D P锛孯ybicki E P.Microcomputer-based Quantification of Maize Streak Virus Symptom in Zeamays[J].Phtopathology锛�1998锛�88(5)锛�422-427. [62]Shatadal P锛孴an J.Identifying Damaged Soybeans by Color Image Analysis[J].Applied Engineering in Agriculture锛�2003锛�9(1)锛�65-69. [63]Hu Chunhua锛孡i Pingping.Application of Image Processing to Diagnose Cucumbers Short of Mg and N[J].Journal of Jiangsu University锛歂atural Science Edition锛�2004锛�25(1)锛�9-12.[鑳℃槬鍗庯紝鏉庤悕钀�.鍩轰簬鍥惧儚澶勭悊鐨勯粍鐡滅己姘笌缂洪晛鍒ゅ埆鐨勭爺绌禰J].姹熻嫃澶у瀛︽姤锛氳嚜鐒剁瀛︾増锛�2004锛�25(1)锛�9-12.] [64]Ma Xiaodan锛孮i Guangyun.Investigation and Recognition on Diseased Spots of Soybean Laminae based on Neural Network[J].Journal of Heilongjiang August First Land Reclamation University锛�2006锛�18(2)锛�84-87.[椹檽涓癸紝绁佸箍浜�.鍩轰簬绁炵粡缃戠粶鐨勫ぇ璞嗗彾鐗囩梾鏂戠殑璇嗗埆涓庣爺绌禰J].榛戦緳姹熷叓涓�鍐滃灕澶у瀛︽姤锛�2006锛�18(2)锛�84-87.] [65]Lai Junchen.Research on Maize Diseases Intelligent Diagnosis based on Disease Images[D].Shihezi锛歋hihezi University锛�2010.[璧栧啗鑷�.鍩轰簬鐥呯棁鍥惧儚鐨勭帀绫崇梾瀹虫櫤鑳借瘖鏂爺绌禰D].鐭虫渤瀛愶細鐭虫渤瀛愬ぇ瀛︼紝2010.] [66]Wang Keru.Diagnosis of Crop Disease锛孖nsect Pest and Weed based on Image Recognition[D].Beijng锛欳hinese Academy of Agricultural Science锛�2005.[鐜嬪厠濡�.鍩轰簬鍥惧儚璇嗗埆鐨勪綔鐗╃梾铏崏瀹宠瘖鏂爺绌禰D].鍖椾含锛氫腑鍥藉啘涓氱瀛﹂櫌锛�2005.] [67]Zhang Jing锛學ang Shuangxi锛孌ong Xiaozhi锛宔t al.A Study on Method of Extract of Texture Characteristic Value in Image Processing for Plant Disease of Greenhouse[J].Journal of Shenyang Agricultural University锛�2006锛�37(3)锛�282-285.[寮犻潤锛岀帇鍙屽枩锛岃懀鏅撳織锛岀瓑.鍩轰簬娓╁妞嶇墿鍙剁墖绾圭悊鐨勭梾瀹冲浘鍍忓鐞嗗強鐗瑰緛鎻愬�煎彇鏂规硶鐨勭爺绌禰J].娌堥槼鍐滀笟澶у瀛︽姤.2006锛�37(3)锛�282-285.] [68]Qi Xinglan锛孲tudy on Information Extraction Technology of Dendrolimus Punctatus Damage based on SPOT-5 Remote Sensing Images[D].Fuzhou锛欶ujian Agriculture and Forestry University锛�2011.[浜撳叴鍏�.SPOT-5閬ユ劅褰卞儚椹熬鏉炬瘺铏淇℃伅鎻愬彇鎶�鏈爺绌禰D].绂忓窞锛氱寤哄啘鏋楀ぇ瀛︼紝2011.] [69]Xu Guili锛孧ao Hanping锛孡i Pingping.Research on Extraction Leaf Texture Features as Sample of Nutrient Shortage by Percent Histogram of Differentiation[J].Transactions of the Chinese Society of Agricultural Machinery锛�2003锛�34(2)锛�76-79.[寰愯吹鍔涳紝姣涚綍骞筹紝鏉庤悕钀�.宸垎鐧惧垎鐜囩洿鏂瑰浘娉曟彁鍙栫己绱犲彾鐗囩汗鐞嗙壒寰乕J].鍐滀笟鏈烘瀛︽姤锛�2003锛�34(2)锛�76-79.] [70]Patil J K锛孠umar R.Feature Extraction of Diseased Leaf Images[J].Journal of Signal and Image Processing锛�2012锛�3(1)锛�60-63. [71]Bryceson K P.Digitally Processed Satellite Data as A Tool in Detecting Potential Australian Plague Locust Outbreak Areas[J].Journal of Environmental Management锛�1990锛�30锛�191-207. [72]Bryceson K P.TheUse of Landsat MSS Data to Determine the Distribution of Locust Eggbeds in the Riverina Region of New South Wales[J].Australia International Journal of Remote Sensing锛�1989锛�10锛�1749-1762. [73]Bryceson K P锛學right D E.AnAnalysis of the 1984 Locust Plague in Australia Using Multitemporal Landsat Multispectral Data and ASimulation Model of Locust Development[J].Agriculture锛孍cosystems and Environment锛�1986锛�16锛�87-102. [74]Michael C锛孭ierre M锛孍tienne B锛宔t al.Spot Vegetation Contribution to Desert Locust Habitat Monitoring[C]鈭egetation 2000 Conference锛�2017锛孋D-ROM 锛�247-258. [75]Wolter P T锛孴ownsend P A锛孲turtevant B R锛宔t al.Remote Sensing of the Distribution and Abundance of Host Species for Spruce Budworm in Northern Minnesota and Ontario[J].Remote Sensing of Environment锛�2008锛�112锛�3971-3982. [76]Baret F锛孷anderbilt V C锛孲teven M D锛宔t al.Use of Spectral Analogy to Evaluate Canopy Reflectance Sensitivity to Leaf Optical Properties[J].Remote Sensing of Environment锛�1994锛�48(2)锛�253-260. [77]Rouse J W锛孒aas R H锛孲chell J A锛宔t al.Monitoring Vegetation Systems in the Great Plains with ERTS[J].Third ERTS Symposium锛孨ASA SP-351锛孨ASA锛�1973锛�1锛�309-317. [78]Chen J M.Evaluation ofVegetation Indices and A Modified Simple Ratio for Boreal Applications[J].Canadian Journal of Remote Sensing锛�1996锛�22锛�229-242. [79]Gitelson A A锛孧erzlyak M N锛孋hivkunova O B.Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves[J].Photochemistry and Photobiology锛�2001锛�74锛�38-45. [80]Roujean J L锛孊reon F M.Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements[J].Remote Sensing of Environment锛�1995锛�51锛�375-384. [81]Devadas R锛孡amb D W锛孲impfendorfer S锛宔t al.Evaluating Ten Spectral Vegetation Indices for Identifying Rust Infection in Individual Wheat Leaves[J].Precision Agriculture锛�2009锛�10锛�459-470. [82]Rondeaux G锛孲teven M锛孊aret F.Optimization of Soil-adjusted Vegetation Indices[J].Remote Sensing Environment锛�1996锛�55(2)锛�95-107. [83]Kim M S锛孌aughtry C S T锛孋happelle E W锛宔t al.The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynthetically Active Radiation(APAR)[C]鈭roceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing锛孎rance锛歏al d鈥橧sere锛�1994锛�299-306. [84]Haboudane D锛孧iller J R锛孴remblay N锛宔t al.Integrated Narrowband Vegetation Indices for Prediction of Crop Chlorophyll content for Application to Precision Agriculture[J].Remote Sensing of Environment锛�2002锛�81锛�416-426. [85]Merton R锛孒untington J.Early Simulation of the ARIES-1 Satellite Sensor for Multi-temporal Vegetation Research derived from AVIRIS[R].Summaries of the Eight JPL Airborne Earth Science Workshop.Pasadena锛孋A锛欽PL Publication锛�1999锛�299-307. [86]Gong P锛孭u R L锛孒eald R C.Analysis of in Situ Hyperspectral Data for Nutrient Estimation of Giant Sequoia[J].International Journal of Remote Sensing锛�2002锛�23(9)锛�1827-1850. [87]Pu R L锛孏e S锛孠elly N M锛宔t al. Spectral Absorption Features as Indicators of Water Status in Coast Live Oak(Quercus Agrifolia) Leaves[J].International Journal of Remote Sensing锛�2003锛�24(9)锛�1799-1810. [88]Muhammed H H锛孡arsolle A.FeatureVector based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat[J].Biosystems Engineering锛�2003锛�86(2)锛�125-134. [89]Li Bo锛孡iu Zhanyu锛孒uang Jingfeng.Hyperspectral Identification of Rice Diseases and Pests based on Principal Pomponent Analysis and Probabilistic Neural Network[J].Transactions of the Chinese Society of Agricultural Engineering锛�2009锛�25(9)锛�143-147.[鏉庢尝锛屽垬鍗犲畤锛岄粍鏁嘲锛岀瓑.鍩轰簬PCA 鍜孭NN 鐨勬按绋荤梾铏楂樺厜璋辫瘑鍒玔J].鍐滀笟宸ョ▼瀛︽姤锛�2009锛�25(9)锛�143-147.] [90]Costa G锛孨oferini M锛孎iori G锛宔t al.Innovative Application of Non-destructive Techniques for Fruit Quality and Disease Diagnosis[J].Acta Horticulturae锛�2007锛�753(1)锛�275-282. [91]Shen Wenying锛孡i Yingxue锛孎eng Wei锛宔t al.Inversion Model for Severity of Powdery Mildew in Wheat Leaves based on Factor Analysis-BP Neural Network[J].Transactions of the Chinese Society of Agricultural Engineering锛�2015锛�22(31)锛�183-190.[娌堟枃棰栵紝鏉庢槧闆紝鍐紵锛岀瓑.鍩轰簬鍥犲瓙鍒嗘瀽-BP绁炵粡缃戠粶鐨勫皬楹﹀彾鐗囩櫧绮夌梾鍙嶆紨妯″瀷[J].鍐滀笟宸ョ▼瀛︽姤锛�2015锛�22(31)锛�183-190.] [92]Lin Na锛孻ang Wunian.Hyperspectral Remote Sensing Image Feature Extraction based on Kernel Mininum Noise Fraction Transformation[J].Remote Sensing Technology and Application锛�2013锛�28(2)锛�245-251.[鏋楀锛屾潹姝﹀勾.鍩轰簬鏍告渶灏忓櫔澹板垎绂诲彉鎹㈢殑楂樺厜璋遍仴鎰熷奖鍍忕壒寰佹彁鍙栫爺绌禰J].閬ユ劅鎶�鏈笌搴旂敤锛�2013锛�28(2)锛�245-251.] [93]Bai Lin锛孒ui Meng.Classification and Feature Extraction of Hyperspectral Images based on Improved Minimum Noise Fraction Transformation[J].Computer Engineering & Science锛�2015锛�37(7)锛�1344-1348.[鐧界挊锛屾儬钀�.鍩轰簬鏀硅繘鏈�灏忓櫔澹板垎绂诲彉鎹㈢殑鐗瑰緛鎻愬彇涓庡垎绫籟J].璁＄畻鏈哄伐绋嬩笌绉戝锛�2015锛�37(7)锛�1344-1348.] [94]Zhu Yubo.Hyperspectral Monitoring the Damage of Rice by Leaf Folder Cnaphalocrocis Medinalis G顩嘐N顨疎[D].Nanjing锛歂anjing Agricultural University锛�2012.[鏈卞畤娉�.绋荤旱鍗峰彾铻熷嵄瀹虫按绋荤殑楂樺厜璋辩洃娴嬫柟娉曠爺绌禰D].鍗椾含锛氬崡浜啘涓氬ぇ瀛︼紝2012.] [95]Ji Huihua.Early Detection of Rice Disease and Pests Using Spectrum Analysis Technology[D].Hangzhou锛欳hina Jiliang University锛�2013.[瀛ｆ収鍗�.鍩轰簬鍏夎氨鍒嗘瀽鎶�鏈殑姘寸ɑ鐥呰櫕瀹虫棭鏈熸娴嬬爺绌禰D].鏉窞锛氫腑鍥借閲忓闄紝2013.] [96]Mi Yating锛孯esearch on Greenhouse Tomato Disease Diagnosis based on GA-BP Network[D].Harbin锛歂orseast Forest University锛�2016.[绫抽泤濠�.鍩轰簬GA-BP绁炵粡缃戠粶鐨勬俯瀹ょ暘鑼勭梾瀹宠瘖鏂爺绌禰D].鍝堝皵婊細涓滃寳鏋椾笟澶у锛�2016.] [97]Gao Guolong锛孌u Huaqiang锛孒an Ning锛宔t al.Mapping of Moso Bamboo Forest Using Object-based Approach based on the Optimal Features[J].Scientia Silvae Sinicae锛�2016锛�9(52)锛�77-85.[楂樺浗榫欙紝鏉滃崕寮猴紝闊╁嚌锛岀瓑.鍩轰簬鐗瑰緛浼橀�夌殑闈㈠悜瀵硅薄姣涚鏋楀垎甯冧俊鎭彁鍙朳J].鏋椾笟绉戝锛�2016锛�9(52)锛�77-85.] [98][JP2]Xiao Yan锛孞iang Qigang锛學ang Bin锛宔t al.Objectbased Land-use Classification based on Hybrid Feature Selection Method of Combing ReliefF and PSO[J].Transactions of the Chinese Society of Agricultural Engineering锛�2016锛�32(4)锛�211-216.[鑲栬壋锛屽鐞﹀垰锛岀帇鏂岋紝绛�.鍩轰簬ReliefF鍜孭SO娣峰悎鐗瑰緛閫夋嫨鐨勯潰鍚戝璞″湡鍦板埄鐢ㄥ垎绫籟J].鍐滀笟宸ョ▼瀛︽姤锛�2016锛�32(4)锛�211-216.][JP] [99]Wang Lu锛孏ong Guanghong.Multiple Features Remote Sensing Image Classification based on Combining RelieF and mRMR[J].Chinese Journal of Stereology and Image Analysis锛�2014锛�19(3)锛�250-257.[鐜嬮湶锛岄練鍏夌孩.鍩轰簬ReliefF+mRMR鐗瑰緛闄嶇淮绠楁硶鐨勫鐗瑰緛閬ユ劅鍥惧儚鍒嗙被[J].涓浗浣撹瀛︿笌鍥惧儚鍒嗘瀽锛�2014锛�19(3)锛�250-257.][ZK)] [100]Cheng Ximeng锛孲heng Zhanfeng锛孹ing Tingyan锛宔t al.Efficiency and Accuracy of Multispectral Image Classification based on mRMR Feature Selection Method[J].Journal of Geo-information Science锛�2016锛�6(18)锛�815-823.[绋嬪笇钀岋紝娌堝崰閿嬶紝閭㈠环鐐庯紝绛�.鍩轰簬mRMR鐗瑰緛浼橀�夌畻娉曠殑澶氬厜璋遍仴鎰熷奖鍍忓垎绫绘晥鐜囩簿搴﹀垎鏋怺J].鍦扮悊淇℃伅绉戝瀛︽姤锛�2016锛�6(18)锛�815-823.][ZK)] [101]Zhang Yadong.Research on Buildings Extraction of Remote Sensing Image based on Morphology[D].Changchun锛欽ilin University锛�2017.[寮犱簹涓�.鍩轰簬褰㈡�佸鐨勯仴鎰熷奖鍍忔埧灞嬫彁鍙栫爺绌禰D].闀挎槬锛氬悏鏋楀ぇ瀛︼紝2017.] [102]Jiang Y锛孡i C Y.mRMR-based Feature Selection for Classification of Cotton Foreign Matter Using Hyperspectral Imaging[J].Computers and Electronics in Agriculture.2015锛�119锛�191-200. [103]Muhammed H H.Hyperspectral Crop Reflectance Data for Characterizing and Estimating Fungal Disease Severity in Wheat[J].Biosystems Engineering锛�2005锛�91(1)锛�9-20. [104]Wang Jing锛孞ing Yuanshu锛孒uang Wenjiang锛宔t al.Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat[J].Spectroscopy and Spectral Analysis锛�2015锛�35(6)锛�1649-1653.[鐜嬮潤锛屾櫙鍏冧功锛岄粍鏂囨睙锛岀瓑.鍐皬楹︽潯閿堢梾涓ラ噸搴︿笉鍚屼及绠楁柟娉曞姣旂爺绌禰J].鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽.2015锛�35(6)锛�1649-1653.] [105]Shi Y锛孒uang W J锛孻e H C锛宔t al.Partial Least Square Discriminant Analysis based on Normalized Two-stage Vegetation Indices for Mapping Damage from Rice Diseases Using Planet Scope Datasets[J].Sensors锛�2018锛�18(6)锛�1901. [106]Zheng Q锛孒uang W J锛孋ui X M锛宔t al.New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery[J].Sensors锛�2018锛�18(3)锛�868. [107]Shi Jingjing锛孡iu Zhanyu锛孼hang Lili.Hyperspectral Recognition of Rice Damage by Rice Leaf Roller based on Supper Vector Machine[J].Chinese Journal of Rice Science锛�2009锛�23(3)锛�331-334.[鐭虫櫠鏅讹紝鍒樺崰瀹囷紝寮犺帀涓�.鍩轰簬鏀寔鍚戦噺鏈�(SVM)鐨勭ɑ绾靛嵎鍙惰灍鍗卞姘寸ɑ楂樺厜璋遍仴鎰熻瘑鍒玔J].涓浗姘寸ɑ绉戝锛�2009锛�23(3)锛�331-334.] [108]Sun Jun锛孴an Wenjun锛孧ao Hanping锛宔t al.Recognition of Multiple Plant Leaf Diseases based onImproved Convolutional Neural Network[J].Transactions of the Chinese Society of Agricultural Engineering锛�2017锛�33(19)锛�209-215.[瀛欎繆锛岃碍鏂囧啗锛屾瘺缃曞钩锛岀瓑.鍩轰簬鏀硅繘鍗风Н鐩涙櫙缃戠粶鐨勫绉嶆鐗╁彾鐗囩梾瀹宠瘑鍒玔J].鍐滀笟宸ョ▼瀛︽姤锛�2017锛�33(19)锛�209-215.] [109]Zhang Chu.Detection Mechanism and Methodology of Brassicanapus Disease Using Spectroscopy and Spectral Imaging Technologies[D].Hangzhou锛歓hejiang University锛�2016.[寮犲垵.鍩轰簬鍏夎氨涓庡厜璋辨垚鍍忔妧鏈殑娌硅彍鐥呭鐩戞祴鏈虹悊涓庢柟娉曠爺绌禰D].鏉窞锛氭禉姹熷ぇ瀛︼紝2016.] [110]Feng Jie.Multispectral Imaging System for the Plant Diseases and Insect Pests Diagnosis[J].Spectral and Spectral Analysis锛�2009锛�29(4)锛�1008-1012.[鍐磥.鐢ㄤ簬妞嶇墿鐥呰櫕瀹宠瘖鏂殑澶氬厜璋辨垚鍍忕郴缁焄J].鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛�2009锛�29(4)锛�1008-1012.]

浣滅墿鐥呰櫕瀹抽仴鎰熺洃娴嬪拰棰勬祴棰勮鐮旂┒杩涘睍顎�

Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing

PDF (PC)

鍙鍖�

鎽樿/Abstract

寮曠敤鏈枃

浣跨敤鏈枃

鍙傝�冩枃鐚�

鐩稿叧鏂囩珷 15

缂栬緫鎺ㄨ崘

Metrics

鏈枃璇勪环

[1]	鏂囨ⅵ杩�, 寮犱寒浜�, 涓囧崕浼�, 浣欏嚒, 閮呬簩閾�, 鏂戒僵鑽�, 鐜嬫案璐�, 瀛欐櫒鏇�. 璺煙鐢熸�佺郴缁熼仴鎰熺洃娴嬪彂灞曡秼鍔垮垎鏋愪笌灞曟湜[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 470-477.
[2]	鍗忓瓙鏄�,寮犺秴,鍐粛鍏�,寮犲瘜浠�,钄＄剷鏉�,鍞愭晱,瀛旂邯杩�. 妞嶈鐗╁�欓仴鎰熺洃娴嬬爺绌惰繘灞�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(1): 1-14.
[3]	琚佸痉瀹�,寮犲啺鐟�,鍙跺洖鏄�,榛勬枃姹�,閮戠惣,閮畨寤�,娈佃壋鎱�,榛勭強鐟�. 姘寸ɑ鐥呰櫕瀹抽仴鎰熺洃娴嬩笌棰勬祴鐮旂┒杩涘睍[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(1): 97-107.
[4]	榛勭櫥鍐�,寮犺仾,濮氭檽鍐�,鏉ㄦ樉鍗�,鍒樺. 鐭垮北鐜閬ユ劅鐩戞祴鐮旂┒杩涘睍[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(5): 1043-1055.
[5]	闊︿腑鏅�,闈虫捣浜�,椤炬檽楣�,鏉ㄨ嫳鑼�,鐜嬪簹娉�,娼樼憸鏄�. 鍩轰簬澶氭椂鐩稿崗鍚屽彉鍖栨娴嬬殑鑰曞湴鎾傝崚閬ユ劅鐩戞祴[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(3): 539-549.
[6]	鏈卞帤鏂�,缃楀啿,瀹樻捣缈�,寮犳柊涔�,鏉ㄥ槈閼�,瀹嬫ⅵ瀹�,鍒樼剷鍐�. 鍩轰簬闈㈠悜瀵硅薄鐨勫婧愬崼鏄熼仴鎰熷奖鍍忕帀绫冲�掍紡闈㈢Н鎻愬彇[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(3): 599-607.
[7]	鐜嬪┓,閭规花,閭瑰偿宓�,鏉庢矆閼�,閮戝繝. 绉哥鐒氱儳閬ユ劅鐩戞祴杩涘睍鍒嗘瀽涓庡睍鏈�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(2): 279-289.
[8]	閮暱搴�,寮犲闇�,渚簹涓�,鍖℃枃鎱�. 鍩轰簬澶氭簮鏃剁┖淇℃伅鐨勮タ瀹佸拰鎷夎惃杩�70骞存潵鍩庡競鎵╁睍杩囩▼閲嶇幇鍙婂湴琛ㄨ鐩栧彉鍖栧垎鏋�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(2): 342-353.
[9]	濮氭澃楣�,鏉ㄧ搴�,闄堟帰,瀹嬫槬妗�. 鍩轰簬Sentinel-1锛�2鍜孡andsat 8鏃跺簭褰卞儚鐨勯劚闃虫箹婀垮湴杩炵画鍙樺寲鐩戞祴鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2021, 36(4): 760-776.
[10]	鐜嬫窇闈�,璧栦僵鐜�,閮濇枌椋�,椹槑鍥�,闊╂棴鍐�. 瑗垮崡鍦板尯2001~2019骞存．鏋楁崯澶辩壒寰侀仴鎰熺洃娴嬩笌鏃剁┖鍒嗘瀽[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2021, 36(3): 552-563.
[11]	钂嬬幉姊�,宕旀収鐝�,鐜嬪姛闆�,鏉ㄥ缓鍗�,鐜嬪仴,娼樻柟鍗�,鑻忔棴,鏂硅タ鐟�. 绉洩銆佸湡澹ゅ喕铻嶄笌鍦熷￥姘村垎閬ユ劅鐩戞祴鐮旂┒杩涘睍[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(6): 1237-1262.
[12]	鍒樼編,鏉滃浗鏄�,浜庡嚖鑽�,鍖℃枃鎱�. 鍝堝皵婊ㄥ煄涔℃搴﹀缓璁剧敤鍦扮粨鏋勫彉鍖栧強涓嶉�忔按闈㈤仴鎰熺洃娴嬪垎鏋�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(5): 1206-1217.
[13]	鏉庢櫤绀�,鍖℃枃鎱�,寮犳緧. 杩�70 a澶╂触涓诲煄鍖哄煄甯傚湡鍦板埄鐢�/瑕嗙洊鍙樺寲閬ユ劅鐩戞祴涓庢椂绌哄垎鏋�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(3): 527-536.
[14]	瀵囧媷,鐜嬪畞缁�,闄堝畨瀹�,鍒樺嚡. 1993~2016骞村枩椹媺闆呭北瑗挎鏉扮撼甯冩祦鍩熷啺宸濆彉鍖栭仴鎰熺洃娴�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(3): 712-722.
[15]	鑳℃牴鐢燂紝鍚撮棶澶╋紝榛勬枃姹燂紝姊佹爧锛岄粍鏋楃敓. 绮掑瓙缇や紭鍖栫殑鏈�灏忎簩涔樻敮鎸佸悜閲忔満鍦ㄥ皬楹︾櫧绮夌梾鐩戞祴涓殑搴旂敤[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2017, 32(2): 299-304.