基于综合混合数据和随机森林回归的中国寒温带森林类型和树种组分制图

遥感学报 Pub Date : 2023-01-01 DOI:10.11834/jrs.20233103
Mengyu Wang, Feng Zhao, Yong Pang, Ran Meng, Wen Jia, Chao Yue
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引用次数: 0

摘要

我是一名学生,我想在这里和大家分享一下我的学习经验,希望对大家有所帮助。炖汤:炖汤的时候要注意炖煮的时间,炖煮的时候要注意炖煮的温度,炖煮的时候要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间,炖煮的时间要注意炖煮的时间。Landsat2020ソçLandsatå½±±åï¼å¾åå®å®¶å²æå°å°å°åºéå¶æåéå¶æ35ãƒãƒãƒãƒãƒãƒãƒç度ç"æãç"æ表æ表æï¼1ï¼å¯¹äºæ£®æç±"åè¦ç度åææ¼ï¼åºäºçé¿å£Landsat¢æ®µåæ¤è¢"ææ°ï¼½å½ä¸åèä½ææ°ä"¥å缨帽åæ¢ç³"æ°ï¼çä¸çå¼ç¹å¾ä¼°ç®çç精度æé"ï¼éå¶ä¸ç度估ç®R¯2=0.03:0.04:0.07:2= 0.炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮
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Mapping forest type and tree species fractions of China’s cold-temperate forest based on synthetically mixed data and random forest regression
å¯’æ¸©å¸¦æ£®æž—æ˜¯é™†åœ°ä¸Šåˆ†å¸ƒé¢ç§¯æœ€å¹¿çš„æ£®æž—ç”Ÿæ€ç³»ç»Ÿï¼Œå ·æœ‰é‡è¦çš„ç”Ÿæ€å’Œç¤¾ä¼šç»æµŽä»·å€¼ã€‚å®šé‡åˆ»ç”»é•¿æ—¶åºå¯’æ¸©å¸¦æ£®æž—ç±»åž‹å’Œæ ‘ç§è¦†ç›–ä¿¡æ¯å¯¹äºŽé‡åŒ–å ¶ç”Ÿæ€ç³»ç»ŸæœåŠ¡åŠŸèƒ½ä»¥åŠåˆ¶å®šæ£®æž—ç®¡ç†æ”¿ç­–å ·æœ‰é‡è¦æ„ä¹‰ã€‚ç„¶è€Œå—å®žæµ‹è¦†ç›–åº¦æ•°æ®ç¼ºä¹å’Œå¤šå ‰è°±å½±åƒå ‰è°±ä¿¡æ¯æœ‰é™çš„é™åˆ¶ï¼ŒçŽ°æœ‰ç ”ç©¶è¾ƒå°‘æŽ¢è®¨ä¸­åˆ†è¾¨çŽ‡å¤šå ‰è°±æ˜Ÿè½½æ•°æ®ï¼ˆå¦‚Landsatå«æ˜Ÿï¼‰å¯¹ä¸­å›½å¯’æ¸©å¸¦æ£®æž—ç±»åž‹è¦†ç›–åº¦å’Œæ ‘ç§è¦†ç›–åº¦è¿›è¡Œé•¿æ—¶åºåæ¼”çš„å¯è¡Œæ€§ï¼Œå¹¶ä¸”å¯¹äºŽé¥æ„Ÿå½±åƒèŽ·å–æ—¶é—´é¢‘çŽ‡ï¼ˆå•æ—¶ç›¸ã€å¤šæ—¶ç›¸ï¼‰å¯¹åæ¼”ç²¾åº¦çš„å½±å“ä»ç¼ºä¹å®šé‡è¯„ä¼°ã€‚ä¸ºæ­¤ï¼Œæœ¬æ–‡åˆ©ç”¨äººå·¥åˆæˆæ ·æœ¬å’Œéšæœºæ£®æž—å›žå½’æ¨¡åž‹å¯¹é»‘é¾™æ±Ÿçœå­Ÿå®¶å²—æž—åœºçš„æ£®æž—ç±»åž‹å’Œæ ‘ç§è¦†ç›–åº¦åˆ†åˆ«è¿›è¡Œäº†åæ¼”ã€‚å¹¶å°†æ¨¡åž‹åº”ç”¨è‡³1986-2020年的Landsat影像,得到孟家岗林场阔叶林和针叶林35年的覆盖度结果。结果表明:(1)对于森林类型覆盖度反演,基于生长季Landsat波段和植被指数(归一化耕作指数以及缨帽变换系数)的中值特征估算的精度最高,阔叶林覆盖度估算R¯2= 0.76,针叶林覆盖度估算R¯2= 0.71;(2ï¼‰å¯¹äºŽæ ‘ç§è¦†ç›–åº¦åæ¼”ï¼ŒåŸºäºŽå¤šæ—¶ç›¸Landsat波段和植被指数的精度最高,落叶松覆盖度估算R¯2= 0.40,红松覆盖度估算R¯2= 0.23,樟子松覆盖度估算R¯2= 0.61;(3ï¼‰å¢žåŠ å½±åƒèŽ·å–æ—¶é—´å¯†åº¦å¯¹äºŽæ£®æž—ç±»åž‹è¦†ç›–åº¦åæ¼”ç²¾åº¦çš„æé«˜æ²¡æœ‰æ˜¾è‘—è´¡çŒ®ï¼ˆé˜”å¶æž—âˆ†R¯2= 0.01,针叶林∆R¯2= -0.03ï¼‰ï¼Œä½†å¯¹æé«˜æ ‘ç§è¦†ç›–åº¦çš„åæ¼”ç²¾åº¦å¸®åŠ©è¾ƒå¤§ï¼ˆè½å¶æ¾âˆ†R¯2= 0.04,红松∆R¯2= 0.07,樟子松∆R¯2= 0.27ï¼‰ã€‚æœ¬æ–‡ç»“åˆäººå·¥åˆæˆæ ·æœ¬å’Œéšæœºæ£®æž—å›žå½’æ¨¡åž‹çš„ä¼˜åŠ¿ï¼Œå®šé‡è¯„ä¼°äº†ä¸åŒæ—¶é—´é¢‘çŽ‡çš„æ—¶åºå ‰è°±ç‰¹å¾å¯¹æ£®æž—ç±»åž‹å’Œæ ‘ç§è¦†ç›–åº¦åæ¼”çš„ç²¾åº¦ï¼Œä¸ºä¸­å›½åŒ—æ–¹æ£®æž—ä»¥åŠå ¨çƒå¯’æ¸©å¸¦æ£®æž—ç±»åž‹å’Œæ ‘ç§è¦†ç›–åº¦çš„å¤§å°ºåº¦é•¿æ—¶åºä¼°ç®—æä¾›äº†æ€è·¯ã€‚
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来源期刊
遥感学报
遥感学报 Social Sciences-Geography, Planning and Development
CiteScore
3.60
自引率
0.00%
发文量
3200
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期刊最新文献
Combining solar-induced chlorophyll fluorescence and optical vegetation indices to better understand plant phenological responses to global change Simulating potential tree height for beech-maple-birch forests in northeastern United States on Google Earth Engine Globe230k: A benchmark dense-pixel annotation dataset for global land cover mapping Urban renewal mapping: A case study in Beijing from 2000 to 2020 Improved fine-scale tropical forest cover mapping for Southeast Asia using Planet-NICFI and Sentinel-1 imagery
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