Mapping forest type and tree species fractions of China’s cold-temperate forest based on synthetically mixed data and random forest regression

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

Abstract

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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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
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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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
期刊介绍： The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China. As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.
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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