基于无人机与卫星遥感的草原地上生物量反演研究

李淑贞; 徐大伟; 范凯凯; 陈金强; 佟旭泽; 辛晓平; 王旭

doi:10.11873/j.issn.1004-0323.2022.1.0272

閬ユ劅鎶�鏈笌搴旂敤鈥衡�� 2022, Vol. 37 鈥衡�� Issue (1): 272-278.DOI: 10.11873/j.issn.1004-0323.2022.1.0272

鈥� 鑽夊湴閬ユ劅涓撴爮鈥� 涓婁竴绡�

鍩轰簬鏃犱汉鏈轰笌鍗槦閬ユ劅鐨勮崏鍘熷湴涓婄敓鐗╅噺鍙嶆紨鐮旂┒

鏉庢窇璐�(),寰愬ぇ浼�,鑼冨嚡鍑�,闄堥噾寮�,浣熸棴娉�,杈涙檽骞�,鐜嬫棴()

涓浗鍐滀笟绉戝闄㈠啘涓氳祫婧愪笌鍐滀笟鍖哄垝鐮旂┒鎵�锛屽寳浜� 100081

鏀剁鏃ユ湡:2021-06-15 淇洖鏃ユ湡:2021-12-14 鍑虹増鏃ユ湡:2022-02-20 鍙戝竷鏃ユ湡:2022-04-08
閫氳浣滆��: 鐜嬫棴
浣滆�呯畝浠�:鏉庢窇璐烇紙1996-锛夛紝濂筹紝灞变笢瀵垮厜浜猴紝纭曞＋鐮旂┒鐢燂紝涓昏浠庝簨鍐滀笟璧勬簮涓庣幆澧冮仴鎰熺爺绌躲�侲?mail:82101202278@caas.cn
鍩洪噾璧勫姪:
鍥藉閲嶇偣鐮斿彂璁″垝椤圭洰(2017YFE0104500);涓ぎ绾у叕鐩婃�х鐮旈櫌鎵�鍩烘湰涓氬姟璐逛笓椤�(1610132020028)

Research of Grassland Aboveground Biomass Inversion based on UAV and Satellite Remoting Sensing

Shuzhen Li(),Dawei Xu,Kaikai Fan,Jinqiang Chen,Xuze Tong,Xiaoping Xin,Xu Wang()

Institute of Agricultural Resources and Regional Planning锛孋hinese Academy of Agricultural Sciences锛孊eijing 100081锛孋hina

Received:2021-06-15 Revised:2021-12-14 Online:2022-02-20 Published:2022-04-08
Contact: Xu Wang

鎽樿/Abstract

鎽樿锛�

鑽夊師鐢熺墿閲忔槸璇勪环鑽夊師鐢熸�佺郴缁熷姛鑳界殑閲嶈鍙傛暟銆備负浜嗗揩閫熴�佸噯纭�佹湁鏁堝湴浼扮畻鑽夊師鍦颁笂鐢熺墿閲忥紝浠ュ懠浼﹁礉灏旇崏鍘熶负鐮旂┒鍖猴紝鍩轰簬鏃犱汉鏈哄鍏夎氨褰卞儚鍜屽崼鏄熼仴鎰燂紙Sentinel-2锛夊奖鍍忥紝閫夋嫨GNDVI銆丩CI銆丯DRE銆丯DVI銆丱SAVI銆丒VI绛�6涓琚寚鏁帮紝缁撳悎瀹炴祴鍦颁笂鐢熺墿閲忔暟鎹紝寤虹珛妞嶈鎸囨暟鍥炲綊妯″瀷锛屽苟閲囩敤鐣欎竴娉曚氦鍙夐獙璇佽繘琛岀簿搴﹁瘎浠枫�傜粨鏋滆〃鏄庯細鍩轰簬鏃犱汉鏈哄鍏夎氨褰卞儚鐨凩CI-鐢熺墿閲忓洖褰掓ā鍨嬶紙RRMSE涓�18%锛屾祴閲忓�间笌棰勬祴鍊�R²涓�0.70锛夊拰NDRE-鐢熺墿閲忔ā鍨嬶紙RRMSE涓�18%锛屾祴閲忓�间笌棰勬祴鍊�R²杈惧埌0.71锛夌簿搴﹂珮浜庡叾浠栨琚寚鏁板洖褰掓ā鍨嬶紱鍩轰簬鏃犱汉鏈哄鍏夎氨褰卞儚鐨勭敓鐗╅噺鈥旀琚寚鏁版ā鍨嬶紙RRMSE鍧囦綆浜�22%锛夋ā鎷熺簿搴﹀潎浼樹簬鍩轰簬Sentinel-2褰卞儚鐨勭敓鐗╅噺鈥旀琚寚鏁版ā鍨嬶紙RRMSE鍧囬珮浜�25%锛夛紝鍙互鏇寸簿纭湴鍙嶆紨鑽夊師鍦颁笂鐢熺墿閲忥紝鐮旂┒缁撴灉鍙负鑽夊師鐢熺墿閲忕簿鍑嗗弽婕旀彁渚涚瀛︽柟娉曞拰渚濇嵁銆�

鍏抽敭璇�: 鏃犱汉鏈�, Sentinel?2, 鍦颁笂鐢熺墿閲�, 绾㈣竟, 妞嶈鎸囨暟

Abstract:

Grassland biomass is an important parameter to evaluate the grassland ecosystem function. To estimate the grassland aboveground biomass rapidly锛� accurately and effectively锛� six vegetation indices 锛圙NDVI锛� LCI锛� NDRE锛� NDVI锛� OSAVI and EVI锛� were selected and calculated based on UAV multi-spectral images and satellite remote sensing 锛圫entinel-2锛� images锛� combined with the ground measured biomass data. The vegetation index regression model was established锛� and the precision was verified by the left one method. The results showed that the accuracy of LCI-biomass regression model 锛圧RMSE = 18%锛� the measured and predicted R² = 0.70锛� and NDRE-biomass model 锛圧RMSE = 18%锛� the measured and predicted R² = 0.71锛� based on UAV multi-spectral images was higher than that of other vegetation -biomass models. The biomass-vegetation index models based on UAV multi-spectral images 锛圧RMSE lower than 22%锛� have better simulation accuracy than Sentinel-2 biomass-vegetation index models 锛圧RMSE higher than 25%锛夛紝 which can more accurately retrieve the aboveground biomass of Hulunbuir grassland. The results can provide scientific methods and basis for accurate retrieval of grassland biomass.

Key words: UAV, Sentinel-2, Aboveground biomass, Red edge, Vegetation index

涓浘鍒嗙被鍙�:

S812

鏉庢窇璐�,寰愬ぇ浼�,鑼冨嚡鍑�,闄堥噾寮�,浣熸棴娉�,杈涙檽骞�,鐜嬫棴. 鍩轰簬鏃犱汉鏈轰笌鍗槦閬ユ劅鐨勮崏鍘熷湴涓婄敓鐗╅噺鍙嶆紨鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(1): 272-278.

Shuzhen Li,Dawei Xu,Kaikai Fan,Jinqiang Chen,Xuze Tong,Xiaoping Xin,Xu Wang. Research of Grassland Aboveground Biomass Inversion based on UAV and Satellite Remoting Sensing[J]. Remote Sensing Technology and Application, 2022, 37(1): 272-278.

鍙傝�冩枃鐚�

1	Fang Jingyun锛� He Jinsheng锛� Xiao Yu锛� et al. Spatial cistribution of grassland biomass in China锛籎锛�. Chinese Journal of Plant Ecology锛� 2004锛� 28锛�4锛夛細491-498.
1	璐洪噾鐢燂紝鏂圭簿浜戯紝鏈翠笘榫欙紝绛�.涓浗鑽夊湴妞嶈鐢熺墿閲忓強鍏剁┖闂村垎甯冩牸灞�锛籎锛�.妞嶇墿鐢熸�佸鎶ワ紝 2004锛� 28锛�4锛夛細491-498.
2	Zhao F锛� Xu B锛� Yang X C锛� et al. Remote sensing estimates of grassland aboveground biomass based on MODIS Net Primary Productivity 锛圢PP锛夛細 A case study in the Xilingol grassland of Northern China锛籎锛�. Remote Sensing锛�2014锛� 6锛�5368-5386. DOI锛�10.3390/rs6065368 .
3	Jin Yunxiang锛� Xu Bin锛� Yang Xiuchun锛� et al. Remote sensing dynamic estimation of grass production in Xilinguole锛� Inner Mongolia锛籎锛�. Scientia Sinica锛圴itae锛夛紝 2011锛�41锛�12锛夛細1185-1195.
3	閲戜簯缈旓紝寰愭枌锛屾潹绉�鏄ワ紝绛�.鍐呰挋鍙ら敗鏋楅儹鍕掔洘鑽夊師浜ц崏閲忓姩鎬侀仴鎰熶及绠楋蓟J锛�.涓浗绉戝锛氱敓鍛界瀛︼紝2011锛�41锛�12锛夛細1185-1195.
4	Li Suying锛� Li Xiaobing锛� Ying Ge锛� et al. Vegetation indexes-biomass models for typical semi-arid steppe鈥擜 case study for Xilinhot in Northern China锛籎锛�. Chinese Journal of Plant Ecology锛�2007锛�31锛�1锛夛細23-31.
4	鏉庣礌鑻憋紝鏉庢檽鍏碉紝鑾烘瓕锛岀瓑.鍩轰簬妞嶈鎸囨暟鐨勫吀鍨嬭崏鍘熷尯鐢熺墿閲忔ā鍨嬧�斺�斾互鍐呰挋鍙ら敗鏋楁旦鐗瑰競涓轰緥锛籎锛�.妞嶇墿鐢熸�佸鎶ワ紝2007锛�31锛�1锛夛細23-31.
5	Yan Ruirui锛� Tang Huang锛� Ding Lei锛� et al. Natural mowing grassland resource distribution and biomass estimation based on remote sensing in Hulunber锛籎锛�.Transactions of the Chinese Society of Agricultural Engineering锛� 2017锛�33锛�15锛夛細210-218.
5	闂憺鐟烇紝鍞愭锛屼竵钑撅紝绛�.鍛间鸡璐濆皵澶╃劧鎵撹崏鍦哄垎甯冨強鐢熺墿閲忛仴鎰熶及绠楋蓟J锛�.鍐滀笟宸ョ▼瀛︽姤锛�2017锛�33锛�15锛夛細210-218.
6	Wang Xiaoqin锛� Wang Miaomiao锛� Wang Shaoqiang锛� et al. Extraction of vegetation information from visible unmanned aerial vehicle images锛籎锛�. Transactions of the Chinese Society of Agricultural Engineering锛� 2015锛�31锛�5锛夛細152-159.
6	姹皬閽︼紝鐜嬭嫍鑻楋紝鐜嬬粛寮猴紝绛�.鍩轰簬鍙鍏夋尝娈垫棤浜烘満閬ユ劅鐨勬琚俊鎭彁鍙栵蓟J锛�.鍐滀笟宸ョ▼瀛︽姤锛�2015锛�31锛�5锛夛細152-159.
7	Batistoti J锛� Junior J M锛� L铆tavo锛� et al. Estimating pasture biomass and canopy height in Brazilian Savanna using UAV photogrammetry锛籎锛�. Remote Sensing锛� 2019锛� 11锛�20锛夛細2447-2459.
8	Zhang Zhengjian锛� Li Ainong锛� Bian Jinhu锛� et al. Estimating aboveground biomass of grassland in Zoige by visible vegetation index derived from unmanned aerial vehicle image锛籎锛�. Remote Sensing Technology and Application锛� 2016锛�31锛�1锛夛細51-62.
8	寮犳鍋ワ紝鏉庣埍鍐滐紝杈归噾铏庯紝绛�.鍩轰簬鏃犱汉鏈哄奖鍍忓彲瑙佸厜妞嶈鎸囨暟鐨勮嫢灏旂洊鑽夊湴鍦颁笂鐢熺墿閲忎及绠楃爺绌讹蓟J锛�.閬ユ劅鎶�鏈笌搴旂敤锛�2016锛�31锛�1锛夛細51-62.
9	Sun Shize锛� Wang Chuanjian锛� Yin Xiaojun锛� et al. Estimating aboveground biomass of natural grassland based on multispectral images of unmanned aerial vehicles锛籎锛�. Journal of Remote sensing锛�2018锛�22锛�5锛夛細848-856.
9	瀛欎笘娉斤紝姹紶寤猴紝灏瑰皬鍚涳紝绛�.鏃犱汉鏈哄鍏夎氨褰卞儚鐨勫ぉ鐒惰崏鍦扮敓鐗╅噺浼扮畻锛籎锛�.閬ユ劅瀛︽姤锛�2018锛�22锛�5锛夛細848-856.
10	Zhang Ya锛� Yin Xiaojun锛� Wang Weiqiang锛� et al. Estimation of grassland aboveground biomass using Landsat-8 OLI satellite image in the Northern Hillside of Tianshan Mountain锛籎锛�.Remote Sensing Technology and Application锛�2017锛�32锛�6锛夛細1012-1021.
10	寮犻泤锛� 灏瑰皬鍚涳紝鐜嬩紵寮猴紝绛�. 鍩轰簬Landsat-8 OLI閬ユ劅褰卞儚鐨勫ぉ灞卞寳鍧¤崏鍦板湴涓婄敓鐗╅噺浼扮畻锛籎锛�. 閬ユ劅鎶�鏈笌搴旂敤锛� 2017锛�32锛�6锛夛細1012-1021.
11	Wang Xu锛� Yan Yuchun锛� Yan Ruirui锛� et al. Effect of rainfall on the seasonal variation of soil respiration in Hulunber Meadow Steppe锛籎锛�. Acta Ecologica Sinica锛�2013锛�33锛�18锛夛細5631-5635.
11	鐜嬫棴锛岄棲鐜夋槬锛岄棲鐟炵憺锛岀瓑.闄嶉洦瀵硅崏鍦板湡澹ゅ懠鍚稿鑺傚彉寮傛�х殑褰卞搷锛籎锛�.鐢熸�佸鎶ワ紝2013锛�33锛�18锛夛細5631-5635.
12	Wang Xu锛� Yan Ruirui锛� Deng Yu锛� et al. Effect of grazing on the temperature sensitivity of soil respiration in Hulunber Meadow Steppe锛籎锛�. Environment Science锛�2014锛�35锛�5锛夛細1909-1914.
12	鐜嬫棴锛岄棲鐟炵憺锛岄倱閽帮紝绛�.鏀剧墽瀵瑰懠浼﹁礉灏旇崏鐢歌崏鍘熷湡澹ゅ懠鍚告俯搴︽晱鎰熸�х殑褰卞搷锛籎锛�.鐜绉戝锛�2014锛�35锛�5锛夛細1909-1914.
13	Gao Yan锛� Liang Zeyu锛� Wang Biao锛� et al. UAV and satellite remote sensing images based aboveground biomass inversion in the meadows of Lake Shengjin锛籎锛�. Journal of Lake Sciences锛� 2019锛�31锛�2锛夛細517-528.
13	楂樼嚂锛屾娉芥瘬锛岀帇褰紝绛�.鍩轰簬鏃犱汉鏈哄拰鍗槦閬ユ劅褰卞儚鐨勫崌閲戞箹鑽夋哗妞嶈鍦颁笂鐢熺墿閲忓弽婕旓蓟J锛�.婀栨硦绉戝锛�2019锛�31锛�2锛夛細517-528.
14	Wang Hanghang锛� Wang Jie锛� Cui Yuhuan锛� et al.Remote sensing monitoring on spatial differentiation of suspended sediment concentration in a river-lake system based on Sentinel-2 MSI imaging锛� A case for Shengjin Lake and connected Yangtze River section in Anhui Province锛籎锛�. Environment Science锛� 2020锛�41锛�3锛夛細1207-1216.
14	鐜嬭琛岋紝鐜嬫澃锛屽磾鐜夌幆.鍩轰簬Sentinel-2 MSI褰卞儚鐨勬渤婀栫郴缁熸按浣撴偓娴墿绌洪棿鍒嗗紓閬ユ劅鐩戞祴锛氫互瀹夊窘鐪佸崌閲戞箹涓庤繛鎺ラ暱姹熸涓轰緥锛籎锛�.鐜绉戝锛�2020锛�41锛�3锛夛細1207-1216.
15	Zhang Yannan锛� Niu Jianming锛� Zhang Qing锛� et al. A discussion on applications of vegetation index for estimating aboveground biomass of typical steppe锛籎锛�. Acta Prataculturae Sinica锛� 2012锛�21锛�1锛夛細229-238.
15	寮犺壋妤狅紝鐗涘缓鏄庯紝寮犲簡锛岀瓑.妞嶈鎸囨暟鍦ㄥ吀鍨嬭崏鍘熺敓鐗╅噺閬ユ劅浼版祴搴旂敤涓殑闂鎺㈣锛籎锛�.鑽変笟瀛︽姤锛�2012锛�21锛�1锛夛細229-238.
16	Xie Lan锛� Gao Donghong. The application and comparison of different nonlinear fit methods锛籎锛�.Journal of Mathematics in Practice and Theory锛� 2009锛�39锛�10锛夛細117-121.
16	璋㈠叞锛岄珮涓滅孩.闈炵嚎鎬у洖褰掓柟娉曠殑搴旂敤涓庢瘮杈冿蓟J锛�. 鏁板鐨勫疄璺典笌璁よ瘑锛� 2009锛�39锛�10锛夛細 117-121.
17	Yi Qiuxiang. Remote estimation of cotton LAI using Sentinel-2 multispectral data锛籎锛�. Transactions of the Chinese Society of Agricultural Engineering锛� 2019锛�35锛�16锛夛細189-197.
17	鏄撶棣�.鍩轰簬Sentinel-2澶氬厜璋辨暟鎹殑妫夎姳鍙堕潰绉寚鏁颁及绠楋蓟J锛�.鍐滀笟宸ョ▼瀛︽姤锛�2019锛�35锛�16锛夛細189-197.
18	Filho Guerini锛� Kuplich锛� Quadros. Estimating natural grassland biomass by vegetation indices using Sentinel-2 remote sensing data锛籎锛�. International Journal of Remote Sensing锛�2020锛�41锛�8锛夛細2861-2876. DOI锛�10.1080/01431161.2019. 1697004 .
19	Guo Yunkai锛� Liu Yuling锛� Xu Min锛� et al. Modeling and analysis of red edge index estimated by leaf Area Index in road vegetation锛籎锛�. Science of Surveying and Mapping锛� 2021锛�46锛�1锛夛細93-98.
19	閮簯寮�锛屽垬闆ㄧ幉锛岃鏁忥紝绛�.妞嶈鍙堕潰绉寚鏁颁及绠楃殑绾㈣竟鎸囨暟寤烘ā鍒嗘瀽锛籎锛�.娴嬬粯绉戝锛�2021锛�46锛�1锛夛細93-98.
20	Boochs F锛� Kupfer G锛� Dockter K锛� et al. Shape of the red edge as vitality indicator for plants锛籎锛�. International Journal of Remote Sensing锛�1990锛�11锛�10锛夛細1741-1753. DOI锛� 10.1080/01431169008955127 .
21	Penuelas F J. The red edge position and shape as indicators of plant chlorophyll content锛� biomass and hydric status锛籎锛�. International Journal of Remote Sensing锛�1994锛�15锛�7锛夛細1459-1470. DOI锛� 10.1080/01431169408954177 .
22	Zhao Yingshi. The principle and method of analysis of remote sensing application锛籑锛�. Beijing锛� Science Press锛� 2003.
22	璧佃嫳鏃�.閬ユ劅搴旂敤鍒嗘瀽鍘熺悊涓庢柟娉曪蓟M锛�.鍖椾含锛氱瀛﹀嚭鐗堢ぞ锛�2003.
23	Dong Jianjun. Yield estimation of typical grassland based on multi-source satellite data锛� A case study of Xilin River Basin锛籇锛�.Hohhotl锛� Inner Mongolia University锛�2013.锛昏懀寤哄啗锛屽熀浜庡婧愬崼鏄熸暟鎹殑鍏稿瀷鑽夊師閬ユ劅浼颁骇鐮旂┒鈥斺�斾互閿℃灄娌虫祦鍩熶负渚嬶蓟D锛�.鍛煎拰娴╃壒锛� 鍐呰挋鍙ゅぇ瀛︼紝2013.锛�
24	Liu Hong锛� Guo Wenli锛� Quan Weijun.Climatic division of the types and yields of grassland in Inner Mongolia锛籎锛�. Journal of Applied Meteorological Science锛�2011锛�22锛�3锛夛細329-335.
24	鍒樻椽锛岄儹鏂囧埄锛屾潈缁翠繆. 鍐呰挋鍙よ崏鍦扮被鍨嬩笌鐢熺墿閲忔皵鍊欏尯鍒掞蓟J锛�. 搴旂敤姘旇薄瀛︽姤锛� 2011锛�22锛�3锛夛細329-335.
25	Lai Qiang锛� Li Qingfeng锛� Aoqier Morigen锛� et al. A study on factors affecting moisture content measurement and ratio of forage fresh weight to dry weight锛籎锛�. Chinese Journal of Grassland锛�2008锛� 30锛�4锛夛細73-77.
25	鏉ュ己锛� 鏉庨潚涓帮紝鑾棩鏍规晼鍏跺皵锛岀瓑. 褰卞搷鐗ц崏鍚按閲忔祴瀹氫互鍙婄墽鑽夊共椴滄瘮鐨勪富瑕佸洜绱狅蓟J锛�. 涓浗鑽夊湴瀛︽姤锛� 2008锛� 30锛�4锛夛細73-77.
26	Gao Yan锛� Liang Zeyu锛� Wang Biao锛� et al. UAV and satellite remote sensing images based aboveground biomass inversion in the meadows of Lake Shengjin锛籎锛�. Journal of Lake Science锛�2019锛� 31锛�2锛夛細215-226.
26	楂樼嚂锛屾娉芥瘬锛岀帇褰紝绛�. 鍩轰簬鏃犱汉鏈哄拰鍗槦閬ユ劅褰卞儚鐨勫崌閲戞箹鑽夋哗妞嶈鍦颁笂鐢熺墿閲忓弽婕旓蓟J锛�. 婀栨硦绉戝锛� 2019锛� 31锛�2锛夛細215-226.
27	Zeng Na锛� Ren Xiaoli锛� He Honglin锛� et al.Estimation of grassland aboveground biomass in Three-river Headwaters region based on neural network锛籎锛�. Research of Environmental Sciences锛� 2017锛�30锛�1锛夛細59-66.
27	鏇剧撼锛� 浠诲皬涓斤紝浣曟椽鏋楋紝绛�. 鍩轰簬绁炵粡缃戠粶鐨勪笁姹熸簮鍖鸿崏鍦板湴涓婄敓鐗╅噺浼扮畻锛籎锛�. 鐜绉戝鐮旂┒锛�2017锛�30锛�1锛夛細59-66.

[1]	闄堝鍗�, 寮犵珛绂�, 榛勯暱骞�, 閮庤悕, 搴峰瓭宀�. 鍩轰簬Sentinel鈦�2A褰卞儚鍏夎氨鍜岀汗鐞嗙壒寰佺殑鍐皬楹﹀彾闈㈢Н鎸囨暟浼扮畻妯″瀷鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 290-305.
[2]	闄堢箒, 璐炬槑鏄�, 鐜嬪┃鐟�, 绋嬩附濞�, 浜庣殦, 鏉庢収棰�. 2020骞存境澶у埄浜氭疆闂村甫婊╂秱绌洪棿鍒嗗竷鍒跺浘[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 373-380.
[3]	椴嶄腑鏃�, 鏂芥鼎鍜�, 榛勮��娆�. 鍩轰簬鏂囩尞璁￠噺鐨勬棤浜烘満鍦扮悊瀛﹀簲鐢ㄦ�佸娍鍒嗘瀽[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 413-425.
[4]	楂樻晱,鏉庢絿灞�,鐜嬭秴,钁ｉ煬,闄堢帴,寮犳柟鏂�,鐜嬭儨钑�,鍒樻敼鑺�,鏉庝繆鐢�. 鍩轰簬娉㈡瀹氬埗楂樺儚绱犳棤浜烘満澶氬厜璋辩浉鏈虹殑闄嗘祽姘村簱姘磋川鍙傛暟鍙嶆紨鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 160-169.
[5]	涔屽凹涔�,鍖呯帀榫�,甯冧粊鍥鹃泤,鍥惧竷鏂板反闆呭皵,闄惰禌鍠滈泤鎷夊浘,鍖呯帀娴�,閲戦灏斿痉鏈ㄥ悙. 鍩轰簬鏃犱汉鏈洪珮鍏夎氨閬ユ劅鐨勫吀鍨嬭崏鍘熼��鍖栨寚绀虹璇嗗埆[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 248-258.
[6]	闄堟爲鏂�,鍒樼偝鏉�,鐜嬫捣鐔�,鑻忓媷,鑹鹃亽涓�,鐢版槙. 缁撳悎鍙鍏夋琚寚鏁板拰鍒嗘按宀畻娉曠殑鍗曟湪鏍戝啝淇℃伅鎻愬彇[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 34-44.
[7]	瀹嬪噷瀵�,鍒樺皬鏉�,寮犱粨鐨�,閽熼湝闆�,鍒樺仴,浣欏潳鍕�,鐜嬪竼. 铻嶅悎绌衡�斿ぉ閬ユ劅鏁版嵁鐨勬瘺绔规灄鍙剁豢绱犲惈閲忓弽婕旀ā鍨嬪姣旂爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 67-76.
[8]	鐜嬪В,鏉庡▉,璧靛崼鏉�,璧电浼�,榛勪寒,鏉ㄥ鑺�. 鍩轰簬鍝ㄥ叺-2鏁版嵁鐨勫杸鏂壒楂樺師娣辨按婀栧簱COD_Mn閬ユ劅鍙嶆紨[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 98-109.
[9]	鏉ㄦ檽鑺�,婊″崼涓�,鍒樻槑鏈�,寮犳案褰�,閮戞旦,瀹嬫暚鑼�,搴峰織寮�. 娴欐睙婊ㄦ捣婀垮湴浜掕姳绫宠崏鐢熺墿閲忛仴鎰熶及绠楁ā鍨嬬爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1445-1454.
[10]	闄堢敳璞�,鑳′腑姘�,鍚村嚡. 1982~2015骞存捣鍗楀矝NDVI鏃剁┖鍙樺寲鍙婃皵鍊欓┍鍔ㄥ姏鍒嗘瀽[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1071-1080.
[11]	濮滄澃,浜庢硥娲�,鐗涙尟鍥�,姊佹槬鐜�,楂樼帀鍥�,寮犵幉,寮犲畯绔�. 鍩轰簬Sentinel-2褰卞儚鐨勫崡鍥涙箹鑿硅崏缇よ惤閬ユ劅鎻愬彇鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1192-1202.
[12]	寤栧瓱鍏�,鏉庣寷,瑜氭,鏉庡皯瀹�. 鍩轰簬GIS閭诲煙鍒嗘瀽鐨勬棤浜烘満鍊炬枩褰卞儚闃斿彾鏋楁爲楂樻彁鍙栨柟娉曠爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1203-1214.
[13]	鏉庝簯榫�,鏉庡啗,甯告鐓�. 鍩轰簬涓嶅悓妞嶈鎸囨暟鐨勯噸搴嗗湴鍖虹墿鍊欏弬鏁版彁鍙栧姣旂爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(4): 978-989.
[14]	鍒樿儨濞�,褰唬浜�,闄堜繆鏉�,鑳￠敠搴�,妤煎瓙鏉�,鍐棴绁�,绋嬫仼鎯�. 鍩轰簬Sentinel-2褰卞儚鐨勫啲灏忛害鏀惰幏闈㈢Н娴嬬畻[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(3): 544-557.
[15]	榻愭枃鏍�,閮戝鏄�,浣曢粠鏄�,鍗㈢弽,椤炬檽楣�,鍛ㄨ壋鍏�. 鍩轰簬澶氭椂鐩窼entinel-2褰卞儚鐨勬鑺遍浌鐏炬椂搴忓彉鍖栭仴鎰熺洃娴�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(3): 566-577.

鍩轰簬鏃犱汉鏈轰笌鍗槦閬ユ劅鐨勮崏鍘熷湴涓婄敓鐗╅噺鍙嶆紨鐮旂┒

Research of Grassland Aboveground Biomass Inversion based on UAV and Satellite Remoting Sensing

RichHTML

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