Time series high-resolution leaf area index estimation and change monitoring in the Saihanba area

نویسندگان

چکیده

叶面积指数LAI(Leaf Area Indexï¼‰æ˜¯è°ƒèŠ‚æ¤è¢«å† å±‚ç”Ÿç†è¿‡ç¨‹çš„æœ€é‡è¦çš„ç”Ÿç‰©ç‰©ç†å˜é‡ä¹‹ä¸€ï¼Œé«˜ç©ºé—´åˆ†è¾¨çŽ‡æ—¶é—´åºåˆ—LAIå¯¹äºŽæ¤è¢«ç”Ÿé•¿æ£€æµ‹ã€åœ°è¡¨è¿‡ç¨‹æ¨¡æ‹Ÿä¸ŽåŒºåŸŸå’Œå ¨çƒå˜åŒ–ç ”ç©¶è‡³å ³é‡è¦ï¼Œä½†æ˜¯ç”±äºŽæ•°æ®ç¼ºå¤±å’Œåæ¼”æ–¹æ³•é™åˆ¶ï¼Œç›®å‰è¿˜æ²¡æœ‰æ—¶ç©ºè¿žç»­çš„é«˜åˆ†è¾¨çŽ‡LAIæ•°æ®äº§å“ã€‚æœ¬ç ”ç©¶æå‡ºäº†ä¸€ç§ç”Ÿæˆæ—¶é—´è¿žç»­çš„é«˜ç©ºé—´åˆ†è¾¨çŽ‡LAIæ•°æ®çš„ç®—æ³•ï¼Œé¦–å ˆå¯¹MODIS LAI产品滤波平滑,生成时间序列LAIçš„ä¸ŠåŒ ç»œæ›²çº¿ï¼Œæ ¹æ®ä¸ŠåŒ ç»œæ›²çº¿æä¾›çš„å˜åŒ–ä¿¡æ¯æž„å»ºLAI动态模型。然后利用地面实测的LAI数据与Landsat反射率数据构建LAI反演的BP (Back Propagation)神经网络模型。将反演得到的高分辨率LAI数据作为LAI观测数据,利用集合卡尔曼滤波EnKF(Ensemble Kalman Filter)方法实时更新动态模型,生成时间连续的30 m空间分辨率LAI数据集。基于该算法生成了塞罕坝地区2000年—2018年长时间序列LAI数据集,利用Prophetæ·±åº¦å­¦ä¹ æ¨¡åž‹è¿›è¡Œæ¨¡æ‹Ÿå’Œé¢„æµ‹ï¼Œæ ¹æ®é¢„æµ‹å’ŒåŽŸå§‹LAI差异,利用支持向量机SVM(Support Vector Machine)方法检测植被干扰状况。结果表明:EnKF算法能够生成时空连续的高空间分辨率LAI数据,估算结果与地面测量值一致性较高,R2为0.9498,RMSE为0.1577,在区域尺度上与Landsat LAI参考值较为吻合,R2高于0.87,RMSE低于0.61。Prophet与SVMæ¨¡åž‹æ£€æµ‹åˆ°ç ”ç©¶åŒº2009年,2010年,2013年,2014年, 2015å¹´æ¤è¢«å—å¹²æ‰°è¾ƒä¸ºä¸¥é‡ï¼Œä¸»è¦ç”±äºŽå¹´é™æ°´é‡åå°‘å’Œæž—åŒºä½œä¸šç ä¼é€ æˆï¼Œæ£€æµ‹ç»“æžœä¸Žå½“åœ°é™æ°´é‡ä¸Žç ä¼æ•°æ®å»åˆã€‚æœ¬æ–‡æå‡ºçš„ç®—æ³•å¯ç”¨äºŽå¤§èŒƒå›´é«˜æ—¶ç©ºLAIæ•°æ®åæ¼”å’Œæ¤è¢«å˜åŒ–æ£€æµ‹ï¼Œå¯¹å¡žç½•åä¹ƒè‡³å ¨å›½æž—åŒºè§„åˆ’ç®¡ç†å ·æœ‰é‡è¦çš„å‚è€ƒä»·å€¼ã€‚

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ژورنال

عنوان ژورنال: Journal of remote sensing

سال: 2021

ISSN: ['1007-4619', '2095-9494']

DOI: https://doi.org/10.11834/jrs.20219447