结合遥感和统计数据的家畜分布网格化方法研究

李翔华; 黄春林; 侯金亮; 韩伟孝; 冯娅娅; 陈彦四; 王静

doi:10.11873/j.issn.1004-0323.2022.1.0262

閬ユ劅鎶�鏈笌搴旂敤鈥衡�� 2022, Vol. 37 鈥衡�� Issue (1): 262-271.DOI: 10.11873/j.issn.1004-0323.2022.1.0262

鈥� 鑽夊湴閬ユ劅涓撴爮鈥� 涓婁竴绡� 涓嬩竴绡�

缁撳悎閬ユ劅鍜岀粺璁℃暟鎹殑瀹剁暅鍒嗗竷缃戞牸鍖栨柟娉曠爺绌�

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^1.涓浗绉戝闄㈣タ鍖楃敓鎬佺幆澧冭祫婧愮爺绌堕櫌鐢樿們鐪侀仴鎰熼噸鐐瑰疄楠屽锛岀敇鑲� 鍏板窞 730000
^2.涓浗绉戝闄㈠ぇ瀛︼紝鍖椾含 100049
^3.鐢樿們鐪侀鍝佹楠岀爺绌堕櫌锛岀敇鑲� 鍏板窞 730000

鏀剁鏃ユ湡:2020-09-08 淇洖鏃ユ湡:2021-11-17 鍑虹増鏃ユ湡:2022-02-20 鍙戝竷鏃ユ湡:2022-04-08
閫氳浣滆��: 榛勬槬鏋�
浣滆�呯畝浠�:鏉庣繑鍗庯紙1996-锛夛紝濂筹紝娌冲崡鍟嗕笜浜猴紝纭曞＋鐮旂┒鐢�,涓昏浠庝簨閬ユ劅澶ф暟鎹笌鏁版嵁绌洪棿鍖栫爺绌躲�侲?mail: lixianghua@lzb.ac.cn
鍩洪噾璧勫姪:
涓浗绉戝闄㈡垬鐣ユ�у厛瀵肩鎶�涓撻」(A绫�)(XDA19040500);鐢樿們鐪侀噸鐐圭爺鍙戣鍒掗」鐩�(17YF1FA134)

Mapping Grid Livestock Distribution with Remote Sensing and Statistical Data

Xianghua Li^1,²(),Chunlin Huang¹(),Jinliang Hou¹,Weixiao Han^1,²,Yaya Feng^1,²,Yansi Chen^1,²,Jing Wang³

^1.Key Laboratory of Remote Sensing of Gansu Province锛孨orthwest Institute of Eco-Environment and Resources锛孋hinese Academy of Sciences锛孡anzhou 730000锛孋hina
^2.University of Chinese Academy of Sciences锛孊eijing 100049锛孋hina
^3.Gansu Food Inspection and Research Institute锛孡anzhou 730000锛孋hina

Received:2020-09-08 Revised:2021-11-17 Online:2022-02-20 Published:2022-04-08
Contact: Chunlin Huang

鎽樿/Abstract

鎽樿锛�

瀹剁暅鐨勭┖闂村垎甯冨浜庣伯椋熷畨鍏ㄣ�佸啘涓氱ぞ浼氱粡娴庛�佺幆澧冨奖鍝嶈瘎浼板拰浜虹暅鍏辨偅鐥呯殑鐮旂┒绛夋柟闈㈣嚦鍏抽噸瑕併�傞�夊彇鐢樿們鐪佷綔涓哄吀鍨嬬爺绌跺尯锛屼互缇婁负鐮旂┒瀵硅薄锛屽熀浜庨殢鏈烘．鏋楀洖褰掔畻娉曟瀯寤轰簡铻嶅悎閬ユ劅鏁版嵁鍜岀粺璁℃暟鎹殑瀹剁暅鍒嗗竷缃戞牸鍖栦及绠楁ā鍨嬶紝鑾峰緱鐢樿們鐪�1 km脳1 km灏哄害涓婄緤鐨勭┖闂村垎甯冧俊鎭�傜粨鏋滆〃鏄庯細鍩轰簬闅忔満妫灄鍥炲綊鐨勫鐣滃垎甯冪綉鏍煎寲浼扮畻妯″瀷锛岀粨鍚堜簡閬ユ劅鏁版嵁鍜岀粺璁℃暟鎹殑浼樺娍锛屽彲浠ヨ緝鍑嗙‘鍦颁及绠�1 km脳1 km灏哄害涓婂鐣滅殑绌洪棿鍒嗗竷鎯呭喌锛屼及绠楃粨鏋滀笌缁熻鏁版嵁涔嬮棿鐨勭浉鍏崇郴鏁帮紙R锛夎揪鍒�0.88锛屽潎鏂规牴璇樊锛圧MSE锛変负0.24锛岀浉瀵瑰潎鏂规牴璇樊锛圧RMSE锛変负15.1%銆傜敇鑲冪渷鐨勭緤涓昏鍒嗗竷鍦ㄦ渤瑗胯蛋寤婃垐澹佸尯銆佺敇鍗楅珮鍘熻崏鍘熻崏鐢稿尯銆侀粍鍦熼珮鍘熶笜闄靛尯鐨勮タ鍗楅儴浠ュ強榛勫湡楂樺師娌熷鍖虹殑鍖楅儴銆傚缇婄殑绌洪棿鍒嗗竷褰卞搷杈冨ぇ鐨勭幆澧冨洜瀛愪緷娆℃槸锛氳�曞湴鐧惧垎姣斻�佹捣鎷斻�佸湴琛ㄦ俯搴﹀拰鍧″害銆�

鍏抽敭璇�: 闅忔満妫灄鍥炲綊, 绌洪棿闄嶅昂搴�, 瀹剁暅, 閬ユ劅

Abstract:

The livestock鈥檚 distribution across space is essential to the research on food safety锛� agricultural society economy锛� environmental influence assessment and zoonosis. In this study锛� an approximation model of livestock's distribution across space was constructed on the basis of Random Forest 锛圧F锛� regression algorithm to combine remote sensing data and statistical data. In order to test and validate the proposed method锛� statistics for sheep in 87 counties of Gansu Province was collected in 2010 and 11 environmental factors were considered in this scheme. Finally锛� the spatial distribution information of sheep on the scale of 1 km脳1 km in Gansu Province is obtained by the model. As is indicated by the results锛� the grid model of livestock鈥檚 spatial distribution based on the RF regression has included the advantages of both remote sensing data and statistical data. It is able to estimate the spatial distribution situation of sheep on the scale of 1 km脳1 km with certain accuracy. The correlation coefficient 锛�R锛� between estimated results and statistical data reached 0.88锛� the Root Mean Square Error 锛圧MSE锛� was 0.24锛� and the Relative Root Mean Square Error 锛圧RMSE锛� was 15.1%. Sheep in Gansu Province are mainly distributed in the Gobi area of the Hexi Corridor锛� the grassland and meadow area of the Gannan Plateau锛� the southwestern part of the hilly area of the Loess Plateau锛� and the northern part of the gully area of the Loess Plateau. The environmental factors that have a greater impact on the spatial distribution of sheep are锛� percentage of cultivated land锛� altitude锛� surface temperature锛� and slope.

Key words: Random forest regression, Spatial downscaling, Livestock, Remote sensing

涓浘鍒嗙被鍙�:

TP79

鏉庣繑鍗�,榛勬槬鏋�,渚噾浜�,闊╀紵瀛�,鍐▍濞�,闄堝溅鍥�,鐜嬮潤. 缁撳悎閬ユ劅鍜岀粺璁℃暟鎹殑瀹剁暅鍒嗗竷缃戞牸鍖栨柟娉曠爺绌禰J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(1): 262-271.

Xianghua Li,Chunlin Huang,Jinliang Hou,Weixiao Han,Yaya Feng,Yansi Chen,Jing Wang. Mapping Grid Livestock Distribution with Remote Sensing and Statistical Data[J]. Remote Sensing Technology and Application, 2022, 37(1): 262-271.

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[1]	杞︽芳, 鐜嬫捣鑽�, 寰愮幒, 瀛欏磭. PSO-DF:鍩轰簬楂樺厜璋辩殑姘寸ɑ鍙剁墖姘惈閲忎及娴嬫柟娉�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 280-289.
[2]	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.
[3]	钁ｇ鏄�, 钂嬫��, 鏉庡畻鍗�, 闄堟磱, 鐜嬪皬鐕�, 鏉ㄩ洩闈�, 鏉庣珷鎴�, 鍒樹簹. 鍩轰簬Sentinel-1鏃跺簭鏁版嵁鐨勭ɑ铏剧敯閬ユ劅璇嗗埆[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 306-314.
[4]	鏋楁鎬�, 姹皬閽�, 姹ょ传闇�, 鏉庤挋钂�, 鍚寸憺濮�, 榛勫痉鍗�. 鍩轰簬闈㈠悜瀵硅薄CNN鍜孯F鐨勪笉鍚岀┖闂村垎杈ㄧ巼閬ユ劅褰卞儚鍐滀笟澶ф鎻愬彇鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 315-327.
[5]	鐜嬫闇�, 鐜嬮拪, 瀛欎寒, 瀛欐斂, 娈垫ⅵ鐞�, 鐜嬫案鍓�. 鍩轰簬閬ユ劅浣滅墿鍙傝�冩洸绾跨殑榛勫湡楂樺師鑻规灉浜у尯濮嬭姳鏈熺洃娴�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 328-336.
[6]	鍚村彅闃�, 閮戝崥绂�, 姹睙, 鍒樺繝, 涓囩倻, 甯堢邯鍗�. 2022骞存睙瑗跨渷鏋佺骞叉棻瀵规煈姗樼妞嶇粡娴庢崯澶辩殑閬ユ劅璇勪及[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 337-349.
[7]	寮犻獮鏈�, 寮犵户鐪�, 閮濇鐟�, 寮犳湀. 21涓栫邯鍒濇湡涓滃寳鍦板尯鍐滅敯鐗╁�欏姩鎬佸強鍏跺姘斿�欑殑鍝嶅簲[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 350-361.
[8]	鍛ㄥ叴闇�, 鐜嬮娲�, 鏉ㄦ攢. 鍩轰簬鍏夊涓庨浄杈鹃仴鎰熷奖鍍忓崗浣滅殑澶氫簯闆惧尯鍩熷啘浣滅墿淇℃伅鎻愬彇鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 362-372.
[9]	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.
[10]	寮犳収鐞�, 鐜嬪崼鍥�, 鐜嬪缓, 璧垫檽鍥�, 钖勪竴瑙�, 渚壋鍐�. 鍩轰簬閬ユ劅涓嶨IS鎶�鏈殑灞辫タ鐪佺敓鎬佽剢寮辨�ф紨鍙樺垎鏋�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(2): 478-491.
[11]	寮犵亸,寮犲叴璧�,鏉庢寮�,闊╅鎱�,妯婄▼,鏉庤帀,鍙叉,浣曞崜,濮氬墠,鍛ㄩ箯. 闈炰簩娓╁姘斾綋鈥擧FC閬ユ劅鍜岃娴嬬爺绌惰繘灞曠患杩�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 1-10.
[12]	鐜嬩功浼�,鑸掓竻鎬�,椹棴,鑲栧姴妤�,鍛ㄦ枃姝�. 婵�鍏夐浄杈句笌楂樺厜璋辨垚鍍忔妧鏈暟鎹瀺鍚堢爺绌惰繘灞�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 11-23.
[13]	鍏戠传鐠�,鐜嬪嵖,鐜嬫晱,寮犵挓,椤惧�╄崳. 鍩轰簬澶氭簮閬ユ劅鍙婃皵璞℃暟鎹殑娌虫祦闈炲厜瀛︽椿鎬ф按璐ㄥ弬鏁板弽婕旀ā鍨嬬爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 120-133.
[14]	鐜嬩笣涓�, 鍒樺織鏄�, 寮犳櫙, 寮犺繛缈�. 鐏惧搴旀�ュ搷搴斾腑澶氭簮閬ユ劅鏁版嵁鍥介檯鍏变韩鏈嶅姟鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 198-208.
[15]	鍗㈠織鍒�,闄堣姵娣�,琚佽秴,鐢颁害闄�,闄堝己,鏂囩編骞�,灏归敶,鏉ㄥ厜. 閲囩敤I-PSPNet璇箟鍒嗗壊妯″瀷鐨勯珮鍒嗚鲸鐜囬仴鎰熷奖鍍忔煇鐗圭妞嶇墿绉嶆鍦板潡鎻愬彇鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 222-233.

缁撳悎閬ユ劅鍜岀粺璁℃暟鎹殑瀹剁暅鍒嗗竷缃戞牸鍖栨柟娉曠爺绌�

Mapping Grid Livestock Distribution with Remote Sensing and Statistical Data

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寮曠敤鏈枃

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