本文選題：植被蓋度 + 數(shù)字相片　； 參考：《浙江農(nóng)業(yè)學(xué)報(bào)》2017年06期

【摘要】：選取6種基于RGB通道信息的植被指數(shù)(VEG、CIVE、EXG、EXGR、NGRDI、COM),借助自主開發(fā)的手機(jī)APP對(duì)6種方法展開對(duì)比研究,從草綠度、天氣條件、蓋度3個(gè)角度分析估算誤差的變化規(guī)律,并從閾值隨光照變化角度評(píng)估每種方法的穩(wěn)定性。研究表明,6種方法估算精度均高于90%,其中,COM方法最高,達(dá)到95.41%,NGRDI方法估算精度最低,為92.87%。每種方法對(duì)深綠色草的估算誤差均小于對(duì)黃綠色草,在陰天條件下(云量≥70%)的估算誤差小于晴天條件下(云量≤10%)。蓋度增加時(shí),VEG、CIVE、EXG、COM方法估算誤差增大,EXGR、NGRDI方法無(wú)明顯變化規(guī)律。同日內(nèi)不同時(shí)次,隨著太陽(yáng)高度角、光照強(qiáng)度的變化,6種方法閾值無(wú)明顯變化(閾值波動(dòng)≤0.02)。不同天氣條件下,VEG、CIVE方法的閾值基本無(wú)變化(閾值波動(dòng)≤0.01),其余方法變化明顯(閾值波動(dòng)≥0.03)。綜上,6種方法均可滿足在手機(jī)平臺(tái)中應(yīng)用的要求,COM方法精度最高,NGRDI方法精度最低。VEG、CIVE方法閾值設(shè)定無(wú)須考慮光照變化影響,較其他方法具有更好的通用性。
[Abstract]:Six kinds of vegetation index based on RGB channel information were selected to analyze the variation of estimation error from three angles of grass green degree, weather condition and coverage degree, with the help of self-developed mobile phone APP. The stability of each method was evaluated from the point of view of threshold changing with light. The results show that the estimation accuracy of the six methods is higher than 90, among which the com method is the highest, and the precision of NGRDI method is the lowest, 92.87. The estimation error of each method for dark green grass is lower than that for yellowish green grass, and the estimation error for dark green grass is less than that for sunny day (cloud amount 鈮，

本文編號(hào)：1976009