基于SPOT5遥感影像丰宁县植被地上生物量估测研究

利用SPOT5遥感影像数据和同期获得的野外调查样地数据,基于按植被类型分类估测的方法,研究了河北省丰宁满族自治县植被地上生物量的遥感估测技术。研究结果显示,SPOT5影像的4个波段反射率和中红外植被指数(VI3)结合建立的多元回归模型,可用于森林生物量的遥感估测,估测的R2值达0.540,说明中红外波段信息提高森林生物量的估测精度有一定作用;通过分析样地生物量与多种植被指数的相关性发现,基于比值植被指数(RVI)的指数回归模型是灌丛生物量估测的最佳模型,估测的R2值达0.711,基于归一化植被指数(NDVI)的简单线性回归模型为估测草地生物量的最佳模型,R2值达0.790。利用2008年的全覆盖SPOT5影像,获得了丰宁县2008年植被地上生物量分布图,除农田植被外,全县地上生物总量为3.706×107 t,单位面积生物量平均为51.223t/hm2,其中,森林植被总生物量为3.578×107 t,灌丛植被总生物量为1.048×106 t,草地植被总生物量为2.277×105 t。     

应用遥感影像纹理特征提取线性体的马尔柯夫随机场模型

分析了遥感影像纹理的统计特征,利用马尔柯夫随机场能够合理地描述图像纹理的随机特征,建立了纹理特征马尔柯夫随机场模型,并且对该模型在提取线性体方面作了初步探讨。研究表明该方法在遥感图像线性体信息提取方面有着广阔的应用前景。     

应用遥感影像纹理特征提取线性体的马尔柯夫随机场模型

分析了遥感影像纹理的统计特征,利用马尔柯夫随机场能够合理地描述图像纹理的随机特征,建立了纹理特征马尔柯夫随机场模型,并且对该模型在提取线性体方面作了初步探讨。研究表明该方法在遥感图像线性体信息提取方面有着广阔的应用前景。     

退牧还草工程项目遥感分析与效益评价——以四川省阿坝县为例

根据对卫星遥感影像的判读解译,探讨了利用3S技术（遥感（RS）、全球定位系统（GPS）、地理信息系统（GIS）技术）监测四川省阿坝县的退牧还草工程现状。通过陆地卫星TM遥感影像数据和同期野外调查数据,分析了植被指数与草地植被生物量之间的相关关系,建立了不同植被指数与草地生物量之间的一元线性回归模型和非线性回归模型。结果表明,利用遥感卫星的植被指数可以较好反映牧草植被群落变化和不同草原类型的牧草产草量差异。在全年放牧草地中,地上总生物量、植被总覆盖度、植被平均高度等指标均低于围栏内的草地。因此,利用“3S”技术可以对全县草原地上生物量进行遥感估测并对草原基况做出评价,客观反映退牧还草工程实施后效果。同时,为推动高空间分辨率卫星影像在我国草业和生态环境建设中的应用打下了坚实基础。     

基于哨兵-2数据的喀斯特高原深水湖库CODMn遥感反演

利用哨兵-2数据及多种方法反演喀斯特高原深水湖库的高锰酸盐指数(COD_Mn),对于区域水环境管理和丰富水质反演理论具有重要意义。以贵阳市红枫湖与百花湖为研究区,基于Sentinel-2 MSI影像和COD_Mn浓度数据,使用随机森林回归(RFR)、支持向量回归方法(SVR)、高斯过程回归(GPR),构建COD_Mn反演模型,获得2018～2020年不同时期的COD_Mn空间分布。结果表明：(1)RFR模型估算精度最高,验证集RMSE为0.222 mg·L^-1,MAPE为5.84%,R2为0.841;(2)红枫湖COD_Mn浓度变化呈现上游高于下游、春季高于夏季的时空分布特征。百花湖除了上游,整体湖区COD_Mn浓度较低且随时间变化不大。研究揭示了RFR模型与Sentinel-2数据在喀斯特高原深水湖库COD_Mn浓度反演具有良好的适用性。     

湖南省大通湖浮游植物群落结构及其与环境因子关系

湖南省大通湖是洞庭湖区典型的浅水养殖湖泊，为合理地开发利用其自然资源，保护生态环境，本研究于2013年4月至2014年3月对湖南省大通湖浮游生物群落结构及环境因子进行了调查研究，并通过典范对应分析(CCA)，探讨浮游生物群落结构与环境因子之间的关系，以期为其渔业可持续利用提供参考。水深、水温、透明度、pH、总氮、硝酸盐氮、总磷、可溶性活性磷以及叶绿素a为水体理化因子的主要调查参数。调查期间共发现浮游植物7门80种，其中绿藻门26种，硅藻门21种，蓝藻门19种，裸藻门6种，黄藻门4种，隐藻门3种，甲藻门1种。浮游植物全年优势种为多形裸藻(Euglena polymorphya)，卵形隐藻 (Cryptomonas ovate)，尖尾蓝隐藻(Chroomonas acuta)，链状小环藻(Syclotellacatenata)，小球藻(Chlorella vulgaris)、圆形衣藻(Chlamydomonas orbicularis)以及四尾栅藻(Scenedesmus quadricauda)。浮游植物密度和生物量年平均值分别为6.58±0.66×104ind.?L-1 和0.16±0.05 mg?L-1，均在2013年11-12月相对较高；浮游植物现存量空间上均呈现南高北低的分布趋势。浮游植物多样性指数(H)、丰富度指数(D)和均匀度指数(J)分别在0.72-2.08、0.38-0.75和0.31-0.84。浮游植物多样性指数表明大通湖水体呈富营养状态。CCA结果表明水温、总磷以及pH是影响大通湖浮游植物群落结构的主要环境因子。     

大通湖浮游植物群落结构与环境因子关系

湖南省大通湖是洞庭湖区典型的浅水养殖湖泊,为合理地开发利用其自然资源,保护生态环境,于2013年4月至2014年3月对大通湖浮游生物群落结构进行了调查研究,并通过典范对应分析(CCA),探讨浮游植物群落结构与环境因子之间的关系。调查期间共发现浮游植物7门80种,全年优势种为多形裸藻Euglena polymorphya、卵形隐藻Cryptomonas ovate、尖尾蓝隐藻Chroomonas acuta、链状小环藻Syclotella catenata、小球藻Chlorella vulgaris、圆形衣藻Chlamydomonas orbicularis及四尾栅藻Scenedesmus quadricauda。浮游植物密度和生物量年平均值分别为6.58±0.66×10~4 ind./L和0.16±0.05 mg/L,且空间上均呈现南高北低的分布趋势。浮游植物多样性指数及丰富度指数均表明大通湖水体呈富营养状态。CCA表明水温、总磷及p H是影响大通湖浮游植物群落结构的主要环境因子。     

洪河湿地植被地上生物量遥感反演研究

在对洪河湿地植被地上生物量实地采样调查的基础上,利用准同步的TM数据建立了洪河湿地地上生物量遥感反演模型。主要研究了洪河湿地植被地上生物量的空间分布情况,并结合研究区的DEM分析生物量空间分布特征和高程的相关关系。并分析了不同生物量范围内,生物量与高程之间相关性存在差异的原因。研究表明:多元回归模型与其他模型相比拟合精度最高,决定系数为0.813,是洪河湿地地上生物量估算的精度最优模型;经估算得到2007年洪河湿地地上生物量主要集中分布于600~1 200 g/m²之间,总生物量为2.4856×10⁸g,平均生物量为934.7105 g/m²。通过生物量与DEM的相关分析得到,在生物量值为0~600 g/m²的低生物量分布区域,生物量与高程之间存在较好的相关性,相关系数为0.79839;在生物量为600~1 200 g/m²和1 200 g/m²以上范围内,生物量与高程值之间相关性较弱。     

基于Landsat 8 OLI遥感影像的天山北坡草地地上生物量估算

利用Landsat 8OLI遥感数据获得NDVI、RVI、DVI、EVI、GNDVI和SAVI等6种常用植被指数,同时结合研究区草地地面实测数据,再根据坡向将研究区划分为阴坡和阳坡两类,利用统计分析方法分别建立紫泥泉牧场阴坡和阳坡的草原生物量遥感估算模型,并进行生物量空间反演和验证。相关分析结果表明:所选植被指数与牧场生物量显著相关,依据坡向分类后数据与未分类数据相关性存在较大差异,其中NDVI相关性最高,EVI相关性最低;紫泥泉草场生物量最优反演模型为基于SAVI的二次多项式模型,精度达80%。利用该模型反演得到2015年紫泥泉牧场草原平均鲜草产草量为113g/m2,折合干草产草量41.85g/m2。研究表明:坡向是影响生物量分布变化的重要因素;利用遥感数据、地面实测生物量数据并结合研究区阴阳坡地形特征,提出的生物量估算模型精度较高,可为该牧区草原生物量合理估算和草地放牧管理提供科学依据。     

基于WorldView-2数据和随机森林算法的遥感水深反演

鉴于传统水深反演线性回归模型易受水质和环境因素的影响,利用甘泉岛区域的高分辨率WorldView-2遥感影像,结合相应的机载LiDAR实测水深数据,使用随机森林算法构建了浅海水深反演非线性回归模型。以反演的水深值和实测水深值的相关系数(R~2)和均方根误差(RMSE)为指标,并同传统的水深反演单波段线性回归模型、双波段比值线性回归模型以及多波段组合线性回归模型进行比较。结果表明,随机森林水深反演非线性回归模型反演精度最优,R~2和RMSE分别为0.967和0.868m。     

Estimation of Grassland Aboveground Biomass Using Landsat 8 OLI Satellite Image in the Northern Hillside of Tianshan Mountain

The Landsat 8 OLI remote sensing data was used to obtain six kinds of commonly vegetation indices including NDVI,RVI,DVI,EVI,GNDVI and SAVI.Meanwhile,combining with the measured data of grassland in the research area,the research area was divided into two kinds of shady and sunny slope according to the slope.Then the biomass remote sensing estimation models of shady and sunny slope in Ziniquan Ranch were created by Statistical analysis method and biomass space inversion and verification was implemented.The results of correlation analysis showed that the selected vegetation indices were significantly correlated with pasture biomass and there was a significant difference between the correlation of the classified data and the non classified data by slope,in which NDVI was the highest and EVI was the lowest.The optimal inversion model of Ziniquan Ranch biomass was based on the two order polynomial model of SAVI with the accuracy 80%.By using this model reversion,the grassland average yield of Ziniquan Ranch in 2015 was 113 g/m²,which equaled to dry grass yield 41.85 g/m².The research shows that the slope direction is an important factor affecting the distribution of biomass.Using remote sensing data and ground measured biomass data and combining with the characteristics of the topography of shady and sunny slope of the research area,the biomass estimation model has higher accuracy,which could provide scientific basis for the reasonable estimation of grassland biomass and management of grassland grazing in the pastoral area.     

基于GF-1遥感数据的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算

随着我国遥感技术迅速的发展,国产系列卫星数据越来越多的应用到各个行业中.在湿地遥感监测方面,湿地生物量和碳储量的遥感估算研究是研究人员非常关注的研究问题,我国自主研制的高分(GF)系列卫星为湿地生态系统的资源监测提供新的途径和方法.提出了基于GF-1卫星的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算方法,通过选取G...     

基于波谱匹配的Hyperion数据大气校正方法对比研究

遥感影像数据的大气校正是高光谱遥感地空对比、信息提取的前提和关键,如何根据不同数据、不同研究区、不同研究目的选择合适的大气校正方法是高光谱遥感应用研究的重点和难点。针对EO\|1卫星Hyperion高光谱遥感数据特点和研究区地形环境特征,分别选择线性回归经验模型、基于MODTRAN4模型的FLAASH和基于DEM数据的ACORN\|3模型不同大气校正方法对研究区Hyperion数据进行大气校正。从波谱匹配、识别的目的出发,通过计算不同方法校正后影像像元的波谱曲线与实测地面波谱曲线的匹配程度分析不同大气校正方法的校正效果。     

Mapping the concentrations of total suspended matter in Lake Taihu,China, using Landsat‐5 TM data

Remote sensing techniques can be used to estimate and map the concentrations of suspended matter in inland water, providing both spatial and temporal information. Although an empirical approach to remote sensing of inland waters has been carried out frequently, satellite imagery has not been incorporated into routine lake monitoring programmes due in part to the lack of a standard prediction equation with multi‐temporal capacity for suspended matter. Empirical and physical models must be developed for each lake and its corresponding turbidity composition if they are to be compared over time, or with other bodies of water.

This study aimed to develop and apply multi‐temporal models to estimate and map the concentrations of total suspended matter (TSM) in Lake Taihu, China. Two Landsat‐5 Thematic Mapper (TM) images and nearly contemporaneous in situ measurements of TSM were used. A modified Dark‐Object Subtraction (DOS) method was used, and appeared to be adequate for atmospheric correction. The relationships were examined between TSM concentrations and atmospherically corrected TM band and band ratios. Results of this study show that the ratio TM4/TM1 has a strong relationship with TSM concentrations for lake waters with relatively low concentrations of phytoplankton algae. However, TM3 provided a strong predictive relationship with TSM concentrations despite varied water quality conditions. Different prediction models were developed and compared using multiple regression analysis. The Akaike Information Criteria (AIC) approach was used to choose the best models. The validation of the multi‐temporal capability of the best models indicated that it is feasible to apply the linear regression model using TM3 to estimate TSM concentrations across time in Lake Taihu, even if no in situ data were available.     

使用高分遥感影像获取塔里木河胡杨高度信息

森林高度在森林生态状况、生物量水平研究中是一个重要参数,目前存在的多种获取树高的遥感技术,都不同程度存在一些问题。塔里木河下游胡杨林作为干旱区内陆河流域荒漠生态系统的核心构件和重要生态恢复对象,了解其高度信息有助于科学评估塔河下游受损生态系统的恢复程度。使用高分辨率遥感影像,利用面向对象影像分析技术,获取单木尺度的胡杨树冠,并提取对应的光谱、纹理和几何特征;在使用消费级无人机获取的树高数据支持下,分别使用Linear、MLP（Multilayer Perceptron, MLP）、PACE 和SVR（SVM Regression, SVR）方法建立树高回归模型获取塔里木河下游胡杨高度信息。结果表明：①基于光谱、纹理和几何特征建立的树高回归模型R²为0.668 7,RMSE为0.942 6 m,说明结合使用高分辨率卫星遥感和无人机遥感技术可以用于获取单木尺度的胡杨树高;②当使用所有特征时,MLP、PACE和SVR回归模型的相关系数均大于0.81,其中PACE回归模型精度最高;③在单木尺度上,光谱特征中包含有较多的树高信息,其次是纹理特征。     

Absorption and scattering properties of water body in Taihu Lake,China: absorption

In order to acquire inherent optical properties to serve the lake water colour/quality remote sensing in Taihu Lake 67 samples were distributed almost all over the lake. Surface water samples were collected and returned to the laboratory for the subsequent processing and analysis. In the laboratory, the absorptions due to the total particulate matter, non‐algal particulate matter, phytoplankton pigment, and CDOM, together with their concentrations were measured and/or calculated, respectively. Then their absorption properties were analysed and compared with those of other lake waters and/or coastal/open waters. Some different and similar characteristics were uncovered. On the one hand, it provides not only a solid basement for the Taihu Lake water colour/quality remote sensing with semi‐analytical/analytical approach but also a typical case for inherent optical properties of case two water especially for inland freshwater lakes. On the other, it is very helpful to improve the practical and intensive application and development of remote sensing in monitoring lake water quality.     

Hyperion数据玉米叶绿素含量制图

采用星地同步观测方法,对Hyperion影像进行预处理并提取玉米专题信息,计算与遥感影像同步获取的玉米地面实测光谱及其一阶微分形式,作物光谱指数参量与叶绿素含量之间的相关性。结果表明:作物叶绿素含量预测指数TCARI/OSAVI与叶绿素a和叶绿素b的相关性最好,R²分别为0.5694和0.5313。采用其与叶绿素含量进行回归分析,建立叶绿素反演模型。将回归结果应用到提取的玉米区域,得出叶绿素a和叶绿素b含量的空间分布图,直观显示玉米的长势状况,为农业估产和植被长势监测提供重要的数据源。     

Ecological indicators for the pelagic zone of the ocean from remote sensing

It is generally accepted that responsible stewardship of the ocean implies ecosystem-based management. A requirement then arises for ecosystem indicators that can be applied in serial fashion with a view to detection of ecosystem change in response to environmental perturbations such as climate change or overfishing. The status of ecological indicators for the pelagic ecosystem is reviewed. The desirable properties of such indicators are listed and it is pointed out that remote sensing (ocean colour, supplemented by sea-surface temperature) is an important aid to achieving them. Some ecological indicators that can be developed from remotely-sensed data on ocean colour are tabulated. They deal with the seasonal cycle of phytoplankton biomass, production and loss terms, annual production, new production, ratio of production to respiration, spatial variances in phytoplankton biomass and production, spatial distribution of phytoplankton functional types, delineation of ecological provinces and phytoplankton size structure.     

On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake,PR China

The visible and near infrared bands of the Landsat thematic mapper (TM) were used in an empirical assessment of submerged vegetation biomass in Honghu Lake in the middle reaches of the Yangtse River, in the People's Republic of China. The method used here was based on eigenvector rotation of the four bands to enhance submerged vegetation biomass variations. Field measurement of spectral reflectance of submerged vegetation was taken for various biomass and vegetation types to determine the possibility of estimating submerged vegetation biomass using remote sensing. The locations of sample points were determined by global positioning system (GPS) and field biomass was obtained at the same time as the TM image. Regression analyses were performed between the principal components and biomass, and a marked linear relationship between submerged vegetation biomass and first two principal components (PC) was revealed. This was used to determine the total biomass of submerged vegetation.