光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
Spectroscopy and Spectral Analysis
2015年
10期
2830-2835
,共6页
大叶黄杨%高光谱%植被指数%滞尘量%回归分析
大葉黃楊%高光譜%植被指數%滯塵量%迴歸分析
대협황양%고광보%식피지수%체진량%회귀분석
Euonymus japonicas%Spectral data%Vegetation index%Amount of dust absorption%Regression analysis
植被指数是表征植被覆盖,生长状况简单有效的度量参数。本文以城市绿化主要植被大叶黄杨为例,研究叶片滞尘对植被指数的影响,并构建植被指数修正模型对植被指数进行修正优化,提高植被指数的测量精度。研究选取北京城区为研究区,采集20个采样点的200个叶片样本,利用电子分析天平、ASD高光谱辐射仪及Win FOLIA叶面积仪,分别获取叶片尘埃量、光谱信息、叶面积等数据。通过对比分析样本叶片除尘前、后光谱特征及NDVI、NDWI、NDNI、NDII、CAI、PRI植被指数分布特征差异,结合单位滞尘量与光谱数据,构建植被指数修正模型,并对修正模型进行精度检验。结果表明:大叶黄杨叶片在除尘前与除尘后的光谱曲线均表现出典型的植被光谱特征,且蓝边、红边均出现在520和705 nm处,然而在350~700,750~1350,1500~1850,1900~2100 nm波段范围内,滞尘对叶片光谱反射率影响显著,同时对植被指数也有较大影响;通过对滞尘量定量的研究分析发现,当尘埃质量增加时,NDVI和PRI植被指数与尘埃量的线性关系变弱,而NDWI ,NDII ,CAI植被指数与尘埃量依然保持明显的线性关系。修正模型NDVI , NDII ,CAI ,PRI精度验证决定系数(R2)分别为0.547,0.430,0.653,0.960,RMSE分别为0.035,0.020,0.112,0.009。研究结果表明对以后利用植被指数进行大面积植被反演、评估时,根据滞尘量影响进行修正优化,提高反演精度有一定参考意义。
植被指數是錶徵植被覆蓋,生長狀況簡單有效的度量參數。本文以城市綠化主要植被大葉黃楊為例,研究葉片滯塵對植被指數的影響,併構建植被指數脩正模型對植被指數進行脩正優化,提高植被指數的測量精度。研究選取北京城區為研究區,採集20箇採樣點的200箇葉片樣本,利用電子分析天平、ASD高光譜輻射儀及Win FOLIA葉麵積儀,分彆穫取葉片塵埃量、光譜信息、葉麵積等數據。通過對比分析樣本葉片除塵前、後光譜特徵及NDVI、NDWI、NDNI、NDII、CAI、PRI植被指數分佈特徵差異,結閤單位滯塵量與光譜數據,構建植被指數脩正模型,併對脩正模型進行精度檢驗。結果錶明:大葉黃楊葉片在除塵前與除塵後的光譜麯線均錶現齣典型的植被光譜特徵,且藍邊、紅邊均齣現在520和705 nm處,然而在350~700,750~1350,1500~1850,1900~2100 nm波段範圍內,滯塵對葉片光譜反射率影響顯著,同時對植被指數也有較大影響;通過對滯塵量定量的研究分析髮現,噹塵埃質量增加時,NDVI和PRI植被指數與塵埃量的線性關繫變弱,而NDWI ,NDII ,CAI植被指數與塵埃量依然保持明顯的線性關繫。脩正模型NDVI , NDII ,CAI ,PRI精度驗證決定繫數(R2)分彆為0.547,0.430,0.653,0.960,RMSE分彆為0.035,0.020,0.112,0.009。研究結果錶明對以後利用植被指數進行大麵積植被反縯、評估時,根據滯塵量影響進行脩正優化,提高反縯精度有一定參攷意義。
식피지수시표정식피복개,생장상황간단유효적도량삼수。본문이성시녹화주요식피대협황양위례,연구협편체진대식피지수적영향,병구건식피지수수정모형대식피지수진행수정우화,제고식피지수적측량정도。연구선취북경성구위연구구,채집20개채양점적200개협편양본,이용전자분석천평、ASD고광보복사의급Win FOLIA협면적의,분별획취협편진애량、광보신식、협면적등수거。통과대비분석양본협편제진전、후광보특정급NDVI、NDWI、NDNI、NDII、CAI、PRI식피지수분포특정차이,결합단위체진량여광보수거,구건식피지수수정모형,병대수정모형진행정도검험。결과표명:대협황양협편재제진전여제진후적광보곡선균표현출전형적식피광보특정,차람변、홍변균출현재520화705 nm처,연이재350~700,750~1350,1500~1850,1900~2100 nm파단범위내,체진대협편광보반사솔영향현저,동시대식피지수야유교대영향;통과대체진량정량적연구분석발현,당진애질량증가시,NDVI화PRI식피지수여진애량적선성관계변약,이NDWI ,NDII ,CAI식피지수여진애량의연보지명현적선성관계。수정모형NDVI , NDII ,CAI ,PRI정도험증결정계수(R2)분별위0.547,0.430,0.653,0.960,RMSE분별위0.035,0.020,0.112,0.009。연구결과표명대이후이용식피지수진행대면적식피반연、평고시,근거체진량영향진행수정우화,제고반연정도유일정삼고의의。
Vegetation indicesarethe simplest and most effective metric parameters representing the features of vegetation cover and growth condition .This paper used Euonymus japonicas Thunb as a study case and collected 200 leaf samples in 20 locations . Using electronic analytical balance and ASD hyperspectral radiometer with Win FOLIA leaf area meter obtainedthe data of the a‐mount of dust ,spectral information and leaf area .Through comparative analysis between dust and clean leaves ,differences of spectral curve and vegetation indices were apparent .Then ,combined with dust weight and spectral data ,dust correction models‐for vegetation indices were built .The analysis results showedthat the spectral curve between clean and dust leaves havetypical characteristics:blue edge and red edge were at 520 and 705 nm ;however ,dust influenced leaf reflectance significantly in range of 350~700 ,750~1 350 ,1 500~1 850 ,1 900~2 100 nm wavelength ,and had a greater impact on vegetation indices .With dust weight increasing ,the linear correlation of dust with NDVI and PRI was non‐significant ,but that with NDWI ,NDII and CAI was still significant .The verification of correction models showedthat coefficient of determination (R2 ) of NDVI ,NDII , CAI and PRI were 0.547 ,0.430 ,0.653 and 0.96 and their root mean square error (RMSE) was 0.035 ,0.020 ,0.112 and 0.009 respectively .Furthermore ,itshowed that applyingdust correction models can improve the accuracy of vegetation indices calculation .