基于无人机遥感监测滩涂湿地入侵种互花米草植被覆盖度

为了实现对沿海滩涂湿地资源与生态环境的有效管理与利用,需要对区域内的入侵种互花米草进行高精度的监测与分析.本文以福建三沙湾为试验区,以低空无人机获取的可见光和多光谱影像为数据源,对互花米草植被覆盖度进行监测与分析,通过NDVI指数模型获取了多光谱影像的植被覆盖度信息,以可见光影像为参考进行了精度检验.结果表明: 影像区互花米草植被覆盖度以40%～60%和60%～80%的中高和高等级覆盖度为主.NDVI模型估算值与真实值之间的均方根误差RMSE为0.06,决定系数R²为0.92,两者具有较好的一致性.     

影响黄土高原地物光谱反射率的非均匀因子及反照率参数化研究

通过应用高光谱反射仪进行各种植被覆盖度地物的同期观测,分析不同地物光谱反射率和宽波段反照率的差异,得出:除太阳高度角的影响外,植被的不同生育期及生长状况决定的叶绿素、细胞构造和含水量等要素都会影响植物光谱反射率;基于归一化植被指数( NDVI)、归一化植被水分指数(NDWI)、土壤体积含水量以及参考对象的光谱曲线建立了植物光谱反射率的估算模型,能较好地反映地物光谱反射率特征;基于地物波谱反射率估算得到的全波段反照率误差在0.02范围内,可以作为反照率遥感反演和转换的依据;该方法也为高光谱遥感在反照率等陆面过程参数尺度耦合和转换过程中应用奠定了基础.     

基于LSMM和改进的FCM提取城市植被覆盖度——以北京市海淀区为例

植被是城市生态系统的重要组成部分,及时获取植被覆盖信息对城市生态环境监测具有重要意义。利用中分辨率Landsat TM遥感数据,采用线性光谱分解模型(LSMM)开展城市植被覆盖度提取;同时,通过改进训练样本选择方式,在最小噪声变换(MNF)、像元纯净指数分析(PPI)、N维可视化分析基础上得到端元样本,再运用模糊C-均值(FCM)获取植被覆盖度;最后以高分辨率SPOT5遥感数据对两种方式的提取结果进行精度检验。结果显示,基于LSMM和改进的FCM提取的城市植被覆盖度与检验数据相关系数分别为0.8252和0.9381,后者可以较好地处理其他要素的非线性影响,因而具有较高精度。     

五种TM影像大气校正模型在植被遥感中的应用

基于2005年7月18日广州市东北部和惠州市北部的TM影像,以表观反射率模型为参照,从植被反射率光谱、地物反射率统计特征、规一化植被指数三方面对4种黑体减法模型和6S模型在植被遥感中的应用进行了评价.结果表明：黑体减法模型DOS4获得了精度较高的植被反射率,其地物反射率与规一化植被指数的信息量最大,适用于研究区的植被遥感研究.对于不同区域的植被遥感研究需要进行具体的比较分析,才能选择到合适的大气校正模型.     

多尺度遥感数据协同的干旱地区植被覆盖度提取

纯植被像元获取是植被覆盖信息遥感反演的必要环节。干旱地区植被分布零散稀疏,使用中、低分辨率遥感数据提取植被覆盖度时,难以获取纯植被像元,致使植被覆盖度提取精度较低。针对上述问题,本文提出一种基于多尺度遥感数据协同的干旱区植被覆盖度反演方法。该方法利用空间分辨率较高的Landsat-8 OLI数据确定纯植被像元,考虑到不同传感器之间的光谱差异,使用实测地物光谱数据进行光谱转换,代替中等分辨率MODIS数据的纯植被像元,应用于像元二分模型,选择典型的干旱区新疆阜康市为研究区,进行植被覆盖度反演实验,最后使用无人机航拍影像对反演结果进行精度验证。结果表明,植被覆盖度反演结果精度较高,与实测值间存在较高的相关性(R2=0.75),均方根误差较低(RMSE=0.10)。该方法能够有效提高干旱区植被覆盖度反演精度,可为利用中低分辨率数据研究干旱地区生态环境变化提供一种新思路。     

基于中分辨率TM数据的湿地水生植被提取

利用湿地水生植被生长旺盛、光谱反射较强、光谱信息比较丰富的8月份中分辨率Landsat TM和ETM+多光谱遥感影像,采用面向对象的分类方法,进行野鸭湖湿地水生植被的提取。研究表明:在提取过程中,通过对原始影像进行主成分变换和穗帽变换,将主要信息与噪声分离,不仅减小了数据冗余和波段间的相关性,而且增大了影像上湿地水生植被与其他地物类型光谱和空间信息的差异性,并结合野外水生植被光谱特征分析,选择归一化植被指数NDVI与归一化水体指数NDWI辅助分类,构建特征波段或波段组合,然后,确定适当的隶属度函数和阈值范围,构建分类决策树,完成湿地水生植被的自动分类,提高了影像分割与面向对象分类的精度,取得了较为理想的湿地水生植被提取结果。2002年和2008年两景影像的总体分类精度分别达到86.5%和85.44%,表明中分辨率TM影像可以满足湿地水生植被提取的需要,又因为其具有较高的波谱分辨率、极为丰富的信息量、相对较低的价格、长时间序列,可以作为近20a湿地水生植被提取和动态变化监测的主要数据源。     

滩涂湿地入侵种互花米草植被覆盖度的高空间分辨率遥感估算

互花米草是沿海滩涂生态系统的重要入侵物种,其分布状况和覆盖度是湿地生态研究的重要参数和基础。以宁德三沙湾(三都澳)滩涂湿地为研究区,以SPOT6 6 m空间分辨率卫星影像为数据源,对互花米草分布和植被覆盖度进行研究,并与同期10 cm空间分辨率无人机影像进行比较验证。结果表明,影像覆盖区域内互花米草总面积为20.19 km~2,其中蕉城区互花米草分布较广,面积为9.63 km~2,占研究区互花米草总面积的47.70%。互花米草植被覆盖度整体上以40%-60%和60%-80%的中、高覆盖度分布为主,其分布面积分别为5.44 km~2和4.95 km~2,占互花米草总分布面积的26.92%和24.52%,而40%以下的低覆盖度和80%以上较高覆盖度分布相对较少。SPOT6遥感影像估算得到的互花米草植被覆盖度具有较好的精度,与无人机影像值之间的均方根误差RMSE为0.117,线性回归决定系数R~2为0.918,可用于滩涂湿地植被覆盖度分析。     

黄河三角洲滨海湿地潮沟分布与植被覆盖度的关系

基于黄河三角洲滨海湿地2005、2010与2017年3个时期的Landsat TM/OLI影像,利用遥感和地理信息技术,对黄河三角洲滨海湿地的潮沟与植被覆盖度的分布格局与动态变化进行了分析,并运用网格搜索法,对研究区的潮沟与植被覆盖度进行相关分析。结果表明:(1) 2005—2017年黄河三角洲滨海湿地植被覆盖度不断提高,低植被覆盖度的面积减少了233.73 km2,高植被覆盖度的面积增加了165.85 km2;(2) 2005—2017年黄河三角洲滨海湿地的潮沟长度和面积不断增加,频数也在增加,其中2017年滨海湿地东南部的潮沟长度达到216.13 km,面积为22.23 km2,长度和面积分别比2005年增加了36.91%和49%;(3) 2005—2017年黄河三角洲潮沟分布与植被覆盖度呈负相关,其中2010年、2017年的潮沟与植被覆盖度呈显著相关(P0.05),这说明黄河三角洲的潮沟的空间分布与区域植被的长势密切相关。     

基于高光谱混合像元分解的干旱地区稀疏植被覆盖度估测

以Hyperion高光谱影像为数据源,选取流沙、假戈壁(影像端元)及荒漠植被(实测光谱端元)3种端元,利用非受限及全受限的混合像元分解对甘肃省民勤绿洲-荒漠过渡带的稀疏植被覆盖度进行了估测.结果表明:全受限混合像元分解得到的荒漠植被分量准确地代表了地表真实稀疏植被覆盖情况,两者之间的偏差不超过5%、均方根误差RMSE为3.0681;而非受限的混合像元分解结果则明显小于地面实测植被覆盖度,两者之间虽具有一定相关性,但相关性不高(R2=0.5855);与McGwire等的相关研究相比,全受限混合像元分解对稀疏植被覆盖度的估测具有更高的精度及可靠性,具有广阔的应用前景.     

乌梁素海湿地芦苇最大羧化速率的高光谱遥感

湿地植被生产力和固碳潜力的研究是全球碳循环和全球变化的热点研究问题。湿地植被的光合能力能够指示其生长的健康状态。最大羧化速率是重要的植被光合参数之一,对精确模拟湿地植被光合作用和气体交换模型中的固碳过程具有重要的作用。以内蒙古乌梁素海湖泊湿地为研究区,进行了芦苇叶片光合参数和光谱的测量。芦苇叶片最大羧化速率(V_(cmax))数值是基于Farquhar光合作用模型,从光合测量获取的A-C_i曲线计算并校正到25℃得到的。分别基于bootstrap PLSR模型、单波段和高光谱植被指数(包括简单比值指数SR和归一化差值指数ND),构建湿地芦苇叶片最大羧化速率(V_(cmax))估算模型。基于高光谱遥感图像HJ-1A HSI,采用ND高光谱指数中具有较高V_(cmax)估算精度的入选波段702和756 nm,获取研究区湿地芦苇最大羧化速率空间分布图。研究结果表明,湿地植被光谱特征和高光谱植被指数,可用于估算湿地芦苇V_(cmax),其中最高精度产生于基于bootstrap PLSR模型的建模方法(R~2=0.87,RMSECV=3.90,RPD=2.72),ND高光谱指数的V_(cmax)估算精度高于SR高光谱指数的估算精度;从获取的V_(cmax)空间分布图上提取估算值,其与测量值对比,存在较好的相关性(R~2=0.80,RMSE=4.74)。     

Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model

Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R² of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.     

Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico

Predicting broad-scale patterns of biodiversity is challenging, particularly in ecosystems where traditional methods of quantifying habitat structure fail to capture subtle but potentially important variation within habitat types. With the unprecedented rate at which global biodiversity is declining, there is a strong need for improvement in methods for discerning broad-scale differences in habitat quality. Here, we test the importance of habitat structure (i.e. fine-scale spatial variability in plant growth forms) and plant productivity (i.e. amount of green biomass) for predicting avian biodiversity. We used image texture (i.e. a surrogate for habitat structure) and vegetation indices (i.e., surrogates for plant productivity) derived from Landsat Thematic Mapper (TM) data for predicting bird species richness patterns in the northern Chihuahuan Desert of New Mexico. Bird species richness was summarized for forty-two 108 ha plots in the McGregor Range of Fort Bliss Military Reserve between 1996 and 1998. Six Landsat TM bands and the normalized difference vegetation index (NDVI) were used to calculate first-order and second-order image textures measures. The relationship between bird species richness versus image texture and productivity (mean NDVI) was assessed using Bayesian model averaging. The predictive ability of the models was evaluated using leave-one-out cross-validation. Texture of NDVI predicted bird species richness better than texture of individual Landsat TM bands and accounted for up to 82.3% of the variability in species richness. Combining habitat structure and productivity measures accounted for up to 87.4% of the variability in bird species richness. Our results highlight that texture measures from Landsat TM imagery were useful for predicting patterns of bird species richness in semi-arid ecosystems and that image texture is a promising tool when assessing broad-scale patterns of biodiversity using remotely sensed data.     

Monitoring coastal wetland communities in north-eastern NSW using ASTER and Landsat satellite data

The coastal wetland communities of north-eastern New South Wales (NSW) Australia exist in a subtropical climate with high biodiversity and are affected by anthropogenic and natural stressors such as urbanization and climate change. The aim of the research is to map and monitor the coastal wetland communities in north eastern NSW using satellite data. Advanced Spaceborne Thermal Emission and Reflectance Radiometer, Landsat ETM+ and Landsat TM satellite imagery of November 2003, June 2001 and September 1989 respectively were used to identify and monitor the wetland communities. Supervised classification was performed using the maximum likelihood standard algorithm. Normalized Difference Vegetation Index was produced and the health of the wetland vegetation was evaluated. The wetland maps present significant changes in the coastal wetland communities in the months of September 1989, June 2001 and November 2003. This information could be used by coastal wetland managers in order to enhance the management of these ecosystems.     

Assessing species density and abundance of tropical trees from remotely sensed data and geostatistics

Question: What relationships exist between remotely sensed measurements and field observations of species density and abundance of tree species? Can these relationships and spatial interpolation approaches be used to improve the accuracy of prediction of species density and abundance of tree species? Location: Quintana Roo, Yucatan peninsula, Mexico. Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model‐averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions. Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases. Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation.     

基于光谱特征变量的湿地典型植物生态类型识别方法——以北京野鸭湖湿地为例

光谱特征变量的选择对于湿地植被识别的精度和效率有着直接的影响作用.以华北地区典型的淡水湿地——野鸭湖湿地为研究区,采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植物的冠层光谱.以野外高光谱数据为基础,首先利用一阶导数与包络线去除的方法,分析和对比不同植物生态类型的光谱特征,选定了用于识别植物生态类型的光谱特征变量,选定的8个光谱特征变量为红边位置WP_r、红边幅值Dr、绿峰位置WP_g、绿峰幅值Rg、510 nm附近的吸收深度DEP-510和吸收面积AREA-510、675 nm附近的吸收深度DEP-675和吸收面积AREA-675.其中,7种植物生态类型的一阶导数光谱特征差异较小,吸收特征差异性相对较大.除WP_r和WP _g外,沉水植物Rg和Dr平均值最低,湿生植物的Rg平均值最高,达到0.164,栽培植物的Dr平均值最高,达到0.012.7种植物生态类型在675 nm附近的DEP-675和AREA-675均高于510 nm附近的DEP-510与AREA-510,除去栽培植物,随着水分梯度的变化,其他6种植物生态类型的吸收深度和吸收面积都表现出先升高后降低的趋势.然后利用单因素方差分析(One-way ANOVA)验证了所选光谱特征变量的区分度,在P≤0.01的置信水平下,选取的8个光谱特征变量都能够较好的区分7种植物生态类型,区分度的最小值为13,最大值为18,并且吸收特征参数的区分度优于一阶导数参数.最后应用非线性的反向传播人工神经网络(BP-ANN)与线性判别分析(FLDA)的类型识别方法,利用选定的8个光谱特征变量进行湿地植物生态类型识别,取得了较好的识别精度,两种方法的总分类精度分别达到85.5％和87.98％.单因素方差分析(One-way ANOVA)和不同分类器的分类精度表明,所选的8个光谱特征变量具有一定的普适性和可靠性.     

高原河谷城市植被时空变化及其影响因素——以青海省西宁市为例

高原型河谷城市具有特殊的自然地理与气候特征,生态环境脆弱,城市化引起的生态环境问题日益突出。植被作为其生态系统的载体,响应更加敏感。深入研究高原型河谷城市的植被变化及其影响因素,对促进西部大开发及城市化健康发展,建立良好的城市人居、生态环境具有重要的现实意义。西宁市作为典型的高原型河谷城市,植被覆盖在城市化与退耕还林(草)政策共同作用下变化明显。基于植被-不透水表面-土壤(V—I—S)模型,以西宁市城市规划区1995年与2009年两期landsat TM影像为数据源,利用线性光谱混合模型进行混合像元分解,获取研究区植被覆盖度的空间分布。通过整体分析、转移矩阵分析,格网分析等技术手段,研究植被时空变化特征并分别探讨川道与丘陵植被变化的影响因素。结果表明:研究期内,西宁城市规划区平均植被覆盖度维持在30%左右。2009年与1995年相比植被覆盖度出现下降,植被覆盖空间差异略有减小。在数量上,基本无覆盖、中覆盖、高覆盖等级呈增加趋势,低覆盖、全覆盖呈下降趋势。研究区西北部及西南部丘陵区植被覆盖整体趋于好转,主要由中低丰度植被等级变化而来,原因在于2000-2005年湟中县累计退耕还林(草)54.91km~2,累计造林247.98km~2,使研究区西北部植被覆盖等级提高,表明西宁市退耕还林(草)工程对于改善植被覆盖效果明显。同时丘陵区植被变化与气候影响趋势相同,表明其植被变化可能也受气候变化影响。城市扩展方向及强度对其周边植被覆盖的影响突出。市区快速扩张及农业退化使川道内中高丰度植被覆盖整体退化趋势明显。主要原因在于2000年后西宁进入快速发展期,城市用地规模迅速增大,川道内城市周边大量中高覆盖等级植被转变为基本无覆盖等级,造成植被退化。川道内城市区域植被变化与气候影响趋势相反,表明本文研究结果可能低估了城市化对川道内植被变化的影响幅度,相比气候影响,人为活动的影响更加强烈。研究区内植被覆盖等级的变化趋势为植被覆盖较差的等级(基本无覆盖和低覆盖)向高一级别发展,得益于退耕还林(草)工程;中等级别以上的覆盖等级出现一定程度的退化,尤其是位于川道中受到城市化干扰的区域植被退化问题尤为突出,需对这些区域采取植被保育措施,避免植被覆盖高等级区域受到城市化影响造成不可逆转的退化。     

Decadal‐scale aspen changes: evidence in remote sensing and tree ring data

Question: (1) Which remote sensing classification most successfully identify aspen using multitemporal Landsat 5 TM images and airborne lidar data? (2) How has aspen distribution changed in southwestern Idaho? (3) Are topographic variables and conifer encroachment correlated with aspen changes? Location: Reynolds Creek Experimental Watershed in southwestern Idaho, USA. Methods: Multi‐temporal Landsat 5 TM and lidar data were used individually and fused together. The best classification model was compared with a 1965 aspen map and tree ring data. Conifer encroachment was examined via image‐based change detection and field mapping. Lidar‐derived topographic variables were correlated with aspen change patterns using quantile regression models. Results: The best Landsat 5 TM classification was a normalized difference vegetation index (NDVI)‐based approach with 92% overall accuracy. The lidar classification of tree presence/absence performed with 100% overall accuracy. Fusing the lidar classification with various Landsat 5 TM classifications improved overall accuracies 3 to 6%. Among the fusion models, the NDVI‐lidar fusion performed best with 96% overall accuracy. Change detection indicated 69% decline in aspen cover, but 179% increase in aspen cover in other areas of the watershed. Conifers have completely replaced 17% of the aspen, while 93% of the remaining aspen stands have young Douglas‐fir and western juniper trees underneath the aspen canopy. Aspen significantly decreased (P‐values <0.05) with increasing elevation (up to 2150 m) and decreasing slope. Conclusions: Landsat 5 TM data used with a NDVI‐based approach provide an accurate method to classify aspen distribution. Landsat 5 TM classifications can be further improved via fusion with lidar data. Aspen change patterns are spatially variable: while aspen is drastically declining in some parts of this watershed, aspen is increasing in other areas.