基于SAIL模型的多角度多光谱遥感叶面积指数反演

随着多角度传感器的陆续出现及植被遥感传输机理研究的深入,多角度遥感逐渐成为地表信息反演的热点问题.以SAIL冠层反射率模型为基础,通过联合多角度和多光谱数据,可以从物理机理角度进行植被叶面积指数(LAI)反演的应用研究.首先通过计算得到多角度多光谱遥感影像的角度信息,并经6S模型纠正后得到多光谱多角度植被冠层反射率数据.然后将PROSPECT模型模拟出的植被叶片反射率和透过率,以及多角度观测数据、LAI和其它实测数据输入SAIL模型,模拟得到了多角度多光谱冠层反射率,进而建立多角度多光谱冠层反射率与LAI的查找表.最后,将影像的多角度多光谱冠层反射率与查找表进行匹配,实现植被LAI的反演.最后对反演结果进行了验证和分析,结果表明反演精度较高,误差均在合理范围之内.     

基于Ｌｏｔｋａ Ｖｏｌｔｅｒｒａ方程的基因调控网络的稳定性分析

在2007年7—8月棉花生长旺季,将冠层按高度分多层,通过简单的试验方法确定了棉冠内叶片丛聚指数（clumping index,Ω）,以此区分计算出田间各层逐时阳叶（受光叶）和阴叶（被遮荫叶）叶面积指数的动态值。在8月23日用LI-6400R便携式光合测量系统分多层分别测定阴、阳叶单张叶片净光合作用的日变化,结合阴、阳叶面积权重,探讨利用Ω区分阴阳叶之后对整个冠层日总光合作用的影响。试验结果表明：（1）花铃期棉花冠层内Ω约为0.68;（2）利用Ω计算得到8月23日冠层日间（09：00—18：00）总净光合作用日平均值大约为20.3μmol·m-2·s-1,其中阳叶贡献约占总量的72%,阴叶约占总量的28%;（3）上层叶片贡献约占总量的75%,中层叶片约占总量的22%,下层叶片约占总量的3%。     

北方玉米冠层光合有效辐射垂直分布及影响因子分析

玉米冠层内光合有效辐射（PAR）的大小直接影响冠层内叶片的光合作用,进而影响玉米净第一性生产力或作物产量的准确评估。为弄清玉米冠层内光合有效辐射的分布规律及其影响因子,基于锦州玉米农田生态系统于2006年生育期的光合有效辐射观测数据和叶面积指数动态观测数据,对玉米冠层光合有效辐射的垂直分布特征及其影响因子进行了分析。结果表明：玉米冠层内不同垂直层次叶片的PAR分布随生育期变化显著,与叶面积指数呈显著的负相关（R2=0.89）;玉米冠层光合有效辐射的消光系数K值在生育期呈动态变化,约为0.76,且表现为苗期较大、生育后期较小。分析表明,在进行光合有效辐射及与此密切相关的光合作用模拟时,应考虑消光系数的动态变化。     

HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析

基于地面实测和PROSAIL模型模拟数据,研究了新型传感器HJ-1ACCD与TM数据一致性问题,分析了传感器天顶角和光谱相应函数差异的影响,对比两种传感器数据估算草地LAI和鲜生物量的效果,得出以下结论:(1)HJ-1ACCD和TM反射率数据的拟合系数R2在0.7322和0.9205左右,在反射率较小时,两种传感器数据一致性较好;随着反射率增大,HJ-1ACCD数值逐渐高于TM。总体而言,在可见光和近红外波段,两种传感器较为接近,其中红波段最接近。(2)两种传感器的NDVI数据一致性非常高,且受传感器天顶角和光谱响应函数影响作用较小(相对误差约为0.34%—0.53%),而反射率的相对差别在3.34%—9.54%。(3)传感器天顶角较光谱响应函数对反射率影响更大。(4)基于HJ-1ACCD反射率数据估算草地LAI和鲜生物量效果较好,其中以CCD2传感器估算效果最好。     

基于TM数据的植被覆盖度反演

本文首先对TM影像进行了几何纠正、辐射校正、大气校正;然后根据混合像元的结构特征,利用TM数据从植被指数(NDVI)中采用“等密度模型”和“非密度模型”提取了宜昌南部地区的植被覆盖度。在用“非密度模型”反演植被覆盖度的过程中,叶面积指数(LAI)是一个必要的参数,本文提出了一种改进的借助可见光波段和近红外波段反射值来提取叶面积指数(LAI)的方法。通过和MODIS数据反演结果比较表明:“非密度模型”的估算精度要高于“等密度模型”;利用“等密度模型”和“非密度模型”反演植被覆盖度是可行。     

基于地面高光谱数据的东亚飞蝗危害程度监测

20世纪80年代以来东亚飞蝗在中国再度猖獗,及时、准确地监测东亚飞蝗的危害程度,对于东亚飞蝗的有效防治有重要意义。本研究以河北省黄骅市为研究区,利用地面高光谱数据,分析和比较了正常生长芦苇和受蝗虫危害芦苇的冠层反射光谱和高光谱特征参数的差异,并建立了高光谱特征参数与芦苇叶面积指数（LAI）的关系模型。结果表明,其中的虫害光谱指数（DSI）最适用于反映研究区芦苇受蝗虫危害的程度。在此基础上,利用DSI对研究区蝗虫的危害程度进行了划分,即：DSI〉62.856未受危害;41.254≤DSI≤59.496轻度危害;DSI〈41.254严重危害。     

Evaluation of potential crop productivity based on remote sensing and agro-ecological zones around the world

Human diets strongly rely on wheat, maize, rice and soybean; research on the potential crop productivity of these four main crops could provide the basis for increasing global crop yields. The evaluation model of realistic potential crop productivity based on remote sensing and agro-ecological zones was proposed in this study to provide reliable reference data for world food security. The statistical data on these four main crops yields were obtained from the FAO. The model was used to investigate the potential production of four staple crops in the world. The distributions of the realistic potential productivity of four staple crops (winter wheat, maize, rice and soybean) were produced. In the main producing countries of the four staple crops, statistical analysis was conducted on the realistic potential productivity (RPP) of the four staple crops, the highest productivity (HP) during the period 1983–2011 and the gap between RPP and HP.     

Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida,US

The leaf area index (LAI) of plant canopies is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. Remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the characteristics of high spatial/spectral resolution of the WorldView-2 (WV2) sensor, it is of significance that the textural information extracted from WV2 multispectral (MS) bands will be first time used in estimating and mapping forest LAI. In this study, LAI mapping accuracies would be compared from (a) spatial resolutions between 2-m WV2 MS data and 30-m Landsat TM imagery, (b) the nature of variables between spectrum-based features and texture-based features, and (c) sensors between TM and WV2. Therefore spectral/textural features (SFs) were first selected and tested; then a canonical correlation analysis was performed with different data sets of SFs and LAI measurement; and finally linear regression models were used to predict and map forest LAI with canonical variables calculated from image data. The experimental results demonstrate that for estimating and mapping forest LAI, (i) using high resolution data (WV2) is better than using relatively low resolution data (TM); (ii) extracted from the same WV2 data, texture-based features have higher capability than that of spectrum-based features; (iii) a combination of spectrum-based features with texture-based features could lead to even higher accuracy of mapping forest LAI than their either one separately; and (iv) WV2 sensor outperforms TM sensor significantly. However, we need to address the possible overfitting phenomenon that might be brought in by using more input variables to develop models. In addition, the experimental results also indicate that the red-edge band in WV2 was the worst on estimating LAI among WV2 MS bands and the WV2 MS bands in the visible range had a much higher correlation with ground measured LAI than that red-edge and NIR bands did.     

Application of 3D triangulations of airborne laser scanning data to estimate boreal forest leaf area index

We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAI_e). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAI_e. The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAI_e and canopy volume for large areas. The LAI_e from triangulations was validated with field measured LAI_e and compared with a reference LAI_e calculated from ALS data using logarithmic model based on Beer’s law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAI_e. We investigated the influence species and season on the triangulated LAI_e and demonstrated the relationship between triangulated LAI_e and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAI_e from triangulations and field measured values agreed the most using the highest pulse density data (RMSE = 0.63, the coefficient of determination (R²) = 0.53). Yet, the LAI_e calculated using ACI-index agreed better with the field measured LAI_e (RMSE = 0.53 and R² = 0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAI_e only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.     

面向LAI 反演的参数化SAILH 模型

建立了一种基于植被冠层辐射传输模型SAILH 的参数化模型。该模型首先对SAILH 模型中用到的9 个 中间变量的计算过程进行简化, 然后用一个明确的表达式计算光照冠层的单次散射贡献。分别用模拟数据和2008 年黑河地区星-机-地同步实验中获取的地面测量数据对该参数化模型的反演精度和效率进行了评价。评价结果表明 该参数化模型能在保证反演精度的基础上极大的提高反演效率; 利用模拟数据进行的模型稳定性评价表明, 参数 化模型的稳定性优于SAILH 模型。