基于直方图与像元分解模型的森林覆盖度遥感估算

doi:10.11873/j.issn.1004-0323.2019.1.0136

鎽樿/Abstract

鎽樿锛� 妫灄瑕嗙洊搴︽槸鑳藉鍕炬弿鍑烘灄鍒嗚竟鐣岀殑妫灄瑕嗙洊鐜囷紝瀹氶噺鍖栫殑瑕嗙洊搴︿俊鎭彲浣撶幇鍏舵按骞冲昂搴︾殑鏃剁┖鍒嗗紓鐗规�с�傚儚鍏冨垎瑙ｆā鍨嬪湪瑕嗙洊搴﹂仴鎰熶及绠椾腑寰楀埌浜嗗箍娉涘簲鐢紝浣嗕粛鐒舵湁寰堝闂锛屽寰堥毦鎵惧埌涓�绉嶆爲鍐犺鐩栧害鐨勭函鍏夎氨绔厓锛屼粠鑰岄毦浠ラ珮绮惧害鍦颁及绠楁爲鍐犺鐩栧害銆備负姝わ紝鍩轰簬鍍忓厓鍒嗚В妯″瀷锛岀粨鍚堜娇鐢ㄥ湡鍦板埄鐢ㄥ拰鍦熷￥绫诲瀷鏁版嵁锛屾彁鍑哄埄鐢ㄧ洿鏂瑰浘娉曠‘瀹氭ā鍨嬩腑涓嶅悓绫诲瀷妞嶈鈥斺�斿湡澹ょ鍏冨弬鏁帮紝瀵瑰尯鍩熷昂搴︽．鏋楄鐩栧害杩涜浼扮畻锛屽苟鍒╃敤涓夊场搴撳尯鐨勫巻鍙查噹澶�161涓牱鐐圭殑瀹炴祴瑕嗙洊搴︽暟鎹繘琛岄獙璇侊紝鍙戠幇R²杈惧埌0.74~0.85锛岃绠楃粨鏋滄瘮杈冩弧鎰忋�傝鏂规硶灏嗕负鍖哄煙灏哄害楂樺垎杈ㄧ巼妫灄瑕嗙洊搴︾殑閬ユ劅浼扮畻鎻愪緵鍊熼壌銆�

鍏抽敭璇�: 妫灄瑕嗙洊搴�, 鍍忓厓鍒嗚В妯″瀷, 鐩存柟鍥炬硶, 涓夊场搴撳尯, 閬ユ劅

Abstract: Forest coverage is the percentage of forest cover for hooking out the forest stand boundary锛宎nd quantitative coverage information can be used for describing the temporal and spatial variability of vegetation in the horizontal scale.Pixel decomposition model has been widely used in remote sensing estimation of vegetation coverage.However锛宼here are still many problems.For example锛宨t is difficult to find a pure spectrum of tree canopy coverage to estimate the crown coverage with high accuracy in forest vegetation applications.In this paper锛宑ombining land use and soil type data锛宨t is proposed to determine the endmember parameters of the different vegetation-soil types by using the histogram method based on pixel decomposition model for estimating the regional scale forest coverage in the Three Gorges.And the results were verified using the 161 samples of field data in the Three Gorges Reservoir area锛孯²was found to be 0.742 4~0.853 6锛宼he estimated results are satisfactory.This method will provide a reference for remote sensing estimation of high-resolution forest coverage at regional scale.

Key words: Forest coverage, Pixel decomposition model, Histogram, Three Gorges, Remote sensing

涓浘鍒嗙被鍙�:

TP79

钁ｇ珛鏂�. 鍩轰簬鐩存柟鍥句笌鍍忓厓鍒嗚В妯″瀷鐨勬．鏋楄鐩栧害閬ユ劅浼扮畻顎僛J]. 閬ユ劅鎶�鏈笌搴旂敤, 2019, 34(1): 136-145.

Dong Lixin. Remote Sensing Estimation of Forest Coverage based on Histogram and Pixel Decomposition Model[J]. Remote Sensing Technology and Application, 2019, 34(1): 136-145.

鍙傝�冩枃鐚�

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[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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.

鍩轰簬鐩存柟鍥句笌鍍忓厓鍒嗚В妯″瀷鐨勬．鏋楄鐩栧害閬ユ劅浼扮畻顎�

Remote Sensing Estimation of Forest Coverage based on Histogram and Pixel Decomposition Model

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寮曠敤鏈枃

浣跨敤鏈枃

鍙傝�冩枃鐚�

鐩稿叧鏂囩珷 15

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