林业科学
林業科學
임업과학
Scientia Silvae Sinicae
2015年
9期
24-34
,共11页
周靖靖%赵忠%刘金良%赵君%赵青侠%刘俊
週靖靖%趙忠%劉金良%趙君%趙青俠%劉俊
주정정%조충%류금량%조군%조청협%류준
有效叶面积指数%光谱 -纹理%纹理%高分辨率影像%刺槐林
有效葉麵積指數%光譜 -紋理%紋理%高分辨率影像%刺槐林
유효협면적지수%광보 -문리%문리%고분변솔영상%자괴림
effective leaf area index ( LAIe )%spectral-textural information%texture%high resolution imagery%black locust plantation
【目的】合理利用高分辨率影像空间信息可提高森林参数的估算精度,本研究在前人基础上进一步细化,探索高分辨率影像的光谱与空间信息在提高森林有效叶面积指数( LAIe)估算精度上的组合规律,以期为高分辨率影像对森林参数的估算和森林健康评价研究提供参考和基础数据。【方法】以黄土高原渭北地区刺槐人工林为研究对象,野外测定76块刺槐人工林样地的 LAIe,并分别提取高分辨率快鸟影像全色数据的7种纹理指数(角二阶矩阵 ASM、同质性 HOM、相关性 COR、对比度 CON、非相似度 DIS、变化量 VAR、熵 ENT)和多光谱数据的7种光谱信息(近红外波段b4、土壤调节植被指数SAVI、修正的土壤植被指数MSAVI、非线性植被指数NLI、改进型土壤大气修正植被指数 EVI、差值植被指数 DVI、归一化植被指数 NDVI),通过栅格运算得到光谱-纹理组合参数,利用一元线性回归模型、二次多项式模型、乘幂模型和指数模型分别建立光谱-纹理组合参数、纹理参数与刺槐人工林LAIe的关系方程,计算比较光谱-纹理与纹理参数对刺槐人工林 LAIe 的估算精度和均方根误差(RMSE),揭示Quickbird影像光谱信息与纹理信息在提高森林 LAIe估算精度上的组合规律。【结果】ASM,HOM,COR 与任意植被指数结合后,估算精度均比相应纹理指数高; CON,DIS和 VAR与部分植被指数结合后,估算精度比相应纹理指数高;相反,ENT 与任意植被指数结合,光谱-纹理组合参数的估算精度均小于纹理指数。二次多项式模型和指数模型对 LAIe估算的决定系数略高于一元线性回归模型和乘幂模型。【结论】利用高分辨率影像的纹理信息和光谱信息估算刺槐人工林 LAIe时,将空间信息加入光谱信息,可有效估算森林 LAIe 且能够得到较高的森林 LAIe估算精度;但并非任意纹理指数与植被指数结合对森林 LAIe的估算精度均高于纹理指数,且估算模型对精度有一定影响。本文的研究表明,综合利用高分辨率影像的空间信息和光谱信息,并选择合适的光谱-纹理组合参数和估算方程,有利于区域尺度森林参数的精确估计和反演。
【目的】閤理利用高分辨率影像空間信息可提高森林參數的估算精度,本研究在前人基礎上進一步細化,探索高分辨率影像的光譜與空間信息在提高森林有效葉麵積指數( LAIe)估算精度上的組閤規律,以期為高分辨率影像對森林參數的估算和森林健康評價研究提供參攷和基礎數據。【方法】以黃土高原渭北地區刺槐人工林為研究對象,野外測定76塊刺槐人工林樣地的 LAIe,併分彆提取高分辨率快鳥影像全色數據的7種紋理指數(角二階矩陣 ASM、同質性 HOM、相關性 COR、對比度 CON、非相似度 DIS、變化量 VAR、熵 ENT)和多光譜數據的7種光譜信息(近紅外波段b4、土壤調節植被指數SAVI、脩正的土壤植被指數MSAVI、非線性植被指數NLI、改進型土壤大氣脩正植被指數 EVI、差值植被指數 DVI、歸一化植被指數 NDVI),通過柵格運算得到光譜-紋理組閤參數,利用一元線性迴歸模型、二次多項式模型、乘冪模型和指數模型分彆建立光譜-紋理組閤參數、紋理參數與刺槐人工林LAIe的關繫方程,計算比較光譜-紋理與紋理參數對刺槐人工林 LAIe 的估算精度和均方根誤差(RMSE),揭示Quickbird影像光譜信息與紋理信息在提高森林 LAIe估算精度上的組閤規律。【結果】ASM,HOM,COR 與任意植被指數結閤後,估算精度均比相應紋理指數高; CON,DIS和 VAR與部分植被指數結閤後,估算精度比相應紋理指數高;相反,ENT 與任意植被指數結閤,光譜-紋理組閤參數的估算精度均小于紋理指數。二次多項式模型和指數模型對 LAIe估算的決定繫數略高于一元線性迴歸模型和乘冪模型。【結論】利用高分辨率影像的紋理信息和光譜信息估算刺槐人工林 LAIe時,將空間信息加入光譜信息,可有效估算森林 LAIe 且能夠得到較高的森林 LAIe估算精度;但併非任意紋理指數與植被指數結閤對森林 LAIe的估算精度均高于紋理指數,且估算模型對精度有一定影響。本文的研究錶明,綜閤利用高分辨率影像的空間信息和光譜信息,併選擇閤適的光譜-紋理組閤參數和估算方程,有利于區域呎度森林參數的精確估計和反縯。
【목적】합리이용고분변솔영상공간신식가제고삼림삼수적고산정도,본연구재전인기출상진일보세화,탐색고분변솔영상적광보여공간신식재제고삼림유효협면적지수( LAIe)고산정도상적조합규률,이기위고분변솔영상대삼림삼수적고산화삼림건강평개연구제공삼고화기출수거。【방법】이황토고원위북지구자괴인공림위연구대상,야외측정76괴자괴인공림양지적 LAIe,병분별제취고분변솔쾌조영상전색수거적7충문리지수(각이계구진 ASM、동질성 HOM、상관성 COR、대비도 CON、비상사도 DIS、변화량 VAR、적 ENT)화다광보수거적7충광보신식(근홍외파단b4、토양조절식피지수SAVI、수정적토양식피지수MSAVI、비선성식피지수NLI、개진형토양대기수정식피지수 EVI、차치식피지수 DVI、귀일화식피지수 NDVI),통과책격운산득도광보-문리조합삼수,이용일원선성회귀모형、이차다항식모형、승멱모형화지수모형분별건립광보-문리조합삼수、문리삼수여자괴인공림LAIe적관계방정,계산비교광보-문리여문리삼수대자괴인공림 LAIe 적고산정도화균방근오차(RMSE),게시Quickbird영상광보신식여문리신식재제고삼림 LAIe고산정도상적조합규률。【결과】ASM,HOM,COR 여임의식피지수결합후,고산정도균비상응문리지수고; CON,DIS화 VAR여부분식피지수결합후,고산정도비상응문리지수고;상반,ENT 여임의식피지수결합,광보-문리조합삼수적고산정도균소우문리지수。이차다항식모형화지수모형대 LAIe고산적결정계수략고우일원선성회귀모형화승멱모형。【결론】이용고분변솔영상적문리신식화광보신식고산자괴인공림 LAIe시,장공간신식가입광보신식,가유효고산삼림 LAIe 차능구득도교고적삼림 LAIe고산정도;단병비임의문리지수여식피지수결합대삼림 LAIe적고산정도균고우문리지수,차고산모형대정도유일정영향。본문적연구표명,종합이용고분변솔영상적공간신식화광보신식,병선택합괄적광보-문리조합삼수화고산방정,유리우구역척도삼림삼수적정학고계화반연。
[Objective]The spatial information of high resolution remote sensing image can improve the estimation accuracy of forestry parameters. This study precisely explored the combinational rule of spectral and spatial information with high resolution remote sensing in order to improve the effective leaf area index ( LAIe) based on the existing research. Obtained results can be provide evidence and data for estimation of forestry parameters and assessments of forestry health.[Method]The black locust ( Robinia pseudoacacia) plantations located in Weibei area of Loess Plateau were chosen as research objects. The LAIe values of 76 plots were measured. We also extracted seven textural parameters of panchromatic data including ASM,HOM,COR,CON,DIS,VAR,ENT and seven spectral parameters of multi-spectral image including b4,SAVI,MSAVI,NLI,EVI,DVI,NDVI from Quickbird imagey with high resolution. The combined spectral-textural indices of Quickbird imagery were obtained using method of raster operation. Four different techniques, including simple linear regression model, quadratic regression model, power model and exponential model, were developed to describe the relationship between image parameters and field measurements of LAIe. The predicted accuracy of combined spectral-textural index and sole texture parameter was compared to reveal the role of combined spectral index and texture parameters used for LAIe retrieval. [Result]The LAIe estimation accuracy was improved when ASM,COR and HOM were combined with SVIs. To a certain extent,the accuracy of SVIs to estimate LAIe was improved with the combination of CON,DIS,VAR and SVIs. The combination of HOM,ASM and COR with SVIs gained the higher r2 than those achieved using HOM,ASM or COR alone. The performances of CON,DIS and VAR were improved when combining with partly SVIs. The combination of Entropy data with SVIs invariably yielded adjusted r2 values that were lower than those achieved using ENT alone. Quadratic regression model and exponential model exhibited higher r2 values than power model and simple linear regression model slightly.[Conclusion]The combination of spectral and special information can improve the accuracy of LAIe estimation effectively when the high-resolution image was used to invert LAIe of black locust plantations. However,not all combined spectral and textural information can obtained higher accuracy comparing to the solely textural information. The model types influenced the accuracy of LAIe estimation slightly. Our results showed that comprehensive use of spatial and spectral information and appropriate selection of model was beneficial to accurate estimation and inversion of forestry parameters.