利用地形参数提高土地覆被分类精度方法的改进

为提高MODIS卫星影像土地覆被产品的分类精度,以京津冀为研究区,在1∶25万土地覆被数据与MODIS土地覆被产品(MCD12Q1)分类一致区内,构建土地覆被类型面积占比与地形因子之间的多元回归模型,并据此改进MODIS土地覆被产品中分类精度较低区域的分类。用面积构成比例和空间一致性比率两个评价指标对改进结果进行评价。结果表明:林地、草地、耕地三种地类的回归模型适合用来改进MODIS土地覆被产品的分类,三种地类与参考数据的空间一致性比率比改进前分别提高了30.02%、40.87%和4.94%;对于与地形因子关系密切的林地和草地,两个评价指标均显示,基于分类一致区建模来改进目标产品的分类精度,比基于整个区域建模改进目标产品的分类精度的效果更加明显。其中,林地的空间一致性比率的提升幅度由8.55%升到30.02%,草地由27.44%升到40.87%。由此可见,地形地貌对土地覆被类型的形成具有重要影响,土地覆被类型面积占比与地形因子之间具有很强的相关关系,基于这种定量关系对土地覆被分类进行改进是完全可行的。     

影像的土地覆被快速分类

精确的土地覆盖信息是进行碳循环、气候变化监测、土壤退化等相关科学研究的基础。随着云计算技术的不断成熟,一些高效算法与平台被不断提出,用来充分挖掘遥感数据所包含的海量信息。基于Google Earth Engine(GEE)云平台,利用随机森林监督分类法对1990、2000、2010、2017年的山西省土地覆被进行了分类。参考Google Earth高清影像选择的1580个样本点,对分类结果进行了验证;同时将分类结果与CNLUCC、GlobeLand30、FROM-GLC等现有土地覆被分类产品进行比较。验证和对比发现时间序列分类结果的总体精度达到86%~94%,比同期单时相分类总体精度提高了5%~10%;本文时间序列结果达到了CNLUCC、GlobeLand30、FROM-GLC等产品的分类精度。结果表明:①在快速准确土地覆被分类方面,时间序列影像与云平台结合,显示出时效性强、时间周期短、成本低等优势;②时间序列百分位数指标能有效地区分不同土地覆被类型的物候差别,在进行土地覆被分类方面显示出简单、易用、高效等特点。该方法对于深入研究大区域尺度的土地覆被变化过程具有重要的参考价值。     

多源遥感数据土地覆被空间尺度效应探讨

鉴于目前土地覆被空间尺度效应的探讨大多集中于低空间分辨率数据,而对中高分辨率数据却鲜有研究的情况,基于3景中高分辨率(2.5m、10m和30m)的同区域同时相遥感数据的土地覆被分类图,从土地覆被空间一致性、土地覆被空间信息转移、土地覆被空间格局变化3方面进行了空间尺度效应探讨。研究表明,不同空间分辨率影像上反映的区域土地覆被空间格局在宏观上是一致的,但构成各个土地覆被类型斑块的边界、形状和数量随着空间分辨率的转换发生了变化;土地覆被类型的空间信息转移趋势取决于构成该土地覆被类型的斑块的形状、大小和空间分布情况;随着空间分辨率的降低,各个土地覆被类型逐渐趋于完整,斑块的狭长程度和边界的复杂程度逐渐降低。     

CBERS与TM遥感影像湿地覆被分类对比分析

为了对比CBERS与TM两种遥感影像在地表覆被信息提取中的具体性能,验证基于CBERS遥感影像进行湿地覆被分类的可行性,以典型内陆淡水湿地区为对象,基于CBERS与TM遥感影像,针对各波段进行信息量统计及光谱特性分析,获取了各波段覆被探测性能的初步认识;运用非监督、监督与面向对象三种代表性分类方法进行分类实验,通过精度误差矩阵对比分类结果,分析了两种遥感影像在湿地覆被分类中的准确程度差异;基于分类结果,通过景观格局指数计算,对比分析了两种影像在湿地覆被信息提取结果上的空间差异和特性。     

基于SPOT5影像的山东南四湖地被覆盖分类研究

以南四湖2006年6月中旬的两景SPOT5多光谱卫星影像为数据源,借助ERDAS Imagine和ArcView软件,运用非监督分类与人工目视解译的方法对影像进行分类处理。运用实地调查记录检验分类结果,符合率达到83.3%。依据分类结果绘制了南四湖地被覆盖图,并且计算了各种地被类型的面积。南四湖大堤内总面积为1206.9 km²,其中开阔水域占总面积的45.54%,湿地植被面积占21.06%,围网养殖区面积占15.6%,无水区的村庄、农田和林地占17.8%。南四湖地被覆盖格局主要受人为因素影响,为保护南四湖生态环境,必须禁止湖内土地围垦,控制围网养殖区,逐步恢复湿地植被。     

多源卫星遥感土地覆被产品在南美洲的一致性分析

针对不同卫星遥感产品在不同区域缺乏一致性基准的问题,提出类型构成相似性、类别混淆程度、空间一致性及参考程度等4种方法,对比分析不同土地覆被产品间的一致性。鉴于南美洲区域土地覆被空间结构和变化对全球变化研究具有重要意义,利用上述4种方法研究了GLOBCOVER2005、GLOBCOVER2009、GLC2000、MODIS2000、GLOBELAND30-2010等5种全球卫星土地覆被产品在南美洲地区的一致性。结果表明,5种产品对于南美洲土地类型的构成刻画基本一致,且对林地识别的一致性最高;南美洲有近60%的土地具有较高的一致性;5种产品两两比较时,参考精度大致在42.27~87.59%之间,GLOBCOVER2009/GLOBCOVER2005组合的参考精度最高,反映出土地覆被动态变化所引起的误差远小于不同制作机构、不同数据源、不同判读方法所带来的制作误差。     

基于面向对象分类的土地利用信息提取及其时空变化研究

基于面向对象的影像分类技术与土地利用变化模型,选取处于剧烈变化环境下的东江流域为研究对象,对其1980~2008年的土地利用变化特征进行了研究。结果表明:(1)面向对象的遥感分类方法在SPOT5高分辨率遥感影像分类中具有较高的精度(总体精度达87.7%),可以有效避免"椒盐现象"发生;(2)1980~2008年东江流域的土地利用方式和空间格局发生了显著变化。耕地面积急剧减少了2 854.4km2,流失的耕地主要转化为了林地、城镇建设用地;园地面积减少了667km2,流失的园地主要转化为了林地;林地面积增加了1 988.7km2,呈波动变化;草地面积比由4.9%缩减为2.0%;水域面积先减少后增加;城镇用地呈快速增长趋势,年增长率高达186.23%。加强耕地保护和适度限制城镇用地增长对区域可持续发展至关重要。     

基于优化分割分类层次体系的土地覆被分类制图方法探讨

随着高分辨率卫星的广泛应用,基于对象的影像分析方法逐渐成为提取土地覆被信息的主要方法。分割优化是基于对象的影像分析方法中的一个基本步骤。不同土地覆被类型通常具有不同的优化分割参数,如何充分利用多尺度最优分割建立分割分类层次体系,从高分辨率影像中提取各种土地覆被类型,实现高精度土地覆被制图,是面向对象影像分析方法中有待解决的一个难题。在获取不同土地覆被类别各自最优的分割参数基础上,探索了一种基于参考数据集的最小分割单元与决策树的分割分类层次体系构建方法。实验表明:该方法可以有效地降低设置分割分类层次体系时对操作者个人经验的依赖,提高分类精度,满足自动制图要求。     

基于多时相协同变化检测的耕地撂荒遥感监测

针对地表覆被复杂、地块破碎等原因导致的撂荒地提取精度较低问题,提出一种基于多时相协同变化检测的耕地撂荒信息提取方法。以河北省石家庄市鹿泉区为研究区,采用Sentinel-2A和Landsat 7多光谱影像,在野外样本的支持下,分析耕地各种覆盖类型的归一化植被指数（Normalized Difference Vegetation Index,NDVI）季相变化规律,以季节性撂荒、常年性撂荒、冬小麦、多年生园地为分类体系,构建多时相协同变化检测模型,开展研究区耕地撂荒状态遥感监测。研究结果表明：基于Sentinel-2A影像的季节性撂荒和常年撂荒耕地的分类精度分别为95.83%和96.55%;基于Landsat 7影像的季节性撂荒和常年撂荒耕地的分类精度分别为91.67%和93.10%;2019年鹿泉区季节性撂荒占耕地面积的4.7%,常年撂荒耕地占7.1%。利用该方法能够快速、准确地获取研究区耕地空间分布、面积等信息,对于不同分辨率的影像均具有较好的撂荒地提取精度。     

基于航空高光谱影像的额济纳绿洲土地覆被提取

土地覆被分类是生态环境评价、植被变化分析以及区域生态水文过程研究的基础。航空高光谱遥感具有高机动、高空间分辨率和高光谱分辨率等特点,在土地覆被提取方面极具优势。以黑河下游机载高光谱遥感数据为基础,针对额济纳旗胡杨林国家级自然保护区植被单一、景观破碎和异质性强的景观特点,以及高光谱数据量大、冗余度高等数据特点,对比分析最小噪声变换与主成分分析两种降维方法,最大似然法、支持向量机与面向对象3种监督分类方法。依据研究结果,首先利用NDVI区分高光谱遥感数据中的植被与非植被类别,然后采用最小噪声变换分别进行降维处理,最后利用最大似然法对研究区内土地覆被类型进行分类提取,提取结果聚类处理。依据随机验证点结合地面调查数据和正射影像,对土地覆被分类结果进行精度验证,总体精度和Kappa系数分别为87.95%和0.855,表明分类结果精度高,能够为生态研究等提供有效数据。     

Annual Total Gross Primary Production Estimation based on Vegetation Indices Seasonal Variation Curve

For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP) as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs) and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicable ΣVIs for estimating the GPP of all vegetation types.The results show that：(1) ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2) ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect of ΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior to ΣEVI andΣEVI2;(3) Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type by ΣNDVI is larger,while using ΣEVI and ΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.     

The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10⁶ km² for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10⁶ km², i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.     

Mapping the invasive species,Chinese tallow,with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress‐tupelo forest, senescing Chinese tallow with red leaves (‘red tallow’), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress‐tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non‐active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs.     

Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data

The preliminary results of Normalized Difference Vegetation Index (NDVI) change studies over India using data from Advanced Very High Resolution Radiometer Global Inventory Modeling and Mapping Studies (AVHRR GIMMS) between 1982 and 2003 are presented. The three methodologies of univariate differencing, temporal profiling and anomaly analysis were undertaken. Univariate differencing was used to determine overall NDVI change between 1982 and 2003. A persistence filter was used to filter out ephemeral changes. The temporal profile analyses were carried out over different meteorological subdivisions to compare changes in NDVI with rainfall patterns. In the anomaly analysis, the areas of change were analysed over different land cover categories derived from IRS‐WiFS data. The preliminary results indicate that positive trends in vegetation change occurred over most parts of the country and these changes appear not to be highly correlated with rainfall data, indicating that land cover transformations may be the major driving force behind the changes. The land cover classifications experiencing the greatest increasing NDVI were tropical thorn forests and intensive agriculture and the land cover experiencing very slow growth included current jhum, tropical moist deciduous and temperate evergreen forest. Five‐year moving averages indicate a general increase in NDVI from 1986 to 1998 and then declining thereafter. This is a concern in most of the meteorological subdivisions.     

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis

This study compared the suitability of LIDAR (LIght Detection And Ranging) data, three-band multispectral data, and LIDAR data integrated with multispectral information, for classifying spatially complex vegetation in the Aspen Parkland of western Canada. Classifications were performed for both a) general vegetation classes limited to three major formations of deciduous forest, shrubland and grassland, and b) eight detailed vegetation classes including upland mixed prairie and fescue grasslands, closed and semi-open aspen forests, western snowberry and silverberry shrublands, and fresh and saline riparian (lowland) meadows. A Digital Elevation Model (DEM) and Surface Elevation Model (SEM) developed from LIDAR data incorporated both topographic and biological biases in community positioning across the landscape. Using multispectral data, the original digital image mosaic, its hybrid color composite, and an intensity-hue-saturation (IHS) image were each tested. Final vegetation classification was done through integration of information from both digital images and LIDAR data to evaluate the improvement in classification accuracy. Among the land cover schedules with three and eight classes of vegetation, classification from the multispectral imagery, specifically the hybrid color composite image, had the highest accuracy, peaking at 74.6% and 59.4%, respectively. In contrast, the LIDAR classification schedules led to an average classification accuracy of 64.8% and 52.3%, respectively, for the general and detailed vegetation data. Subsequent integration of the LIDAR and digital image classification schedules resulted in accuracy improvements of 16 to 20%, resulting in a superior final accuracy of 91% and 80.3%, respectively, for the three and eight classes of vegetation. A final land cover map including 8 classes of vegetation, fresh and saline water, as well as bare ground, was created for the study area with an overall accuracy of 83.9%, highlighting the benefit of integrating LIDAR and multispectral imagery for enhanced vegetation classification in heterogenous rangeland environments.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.     

Assessment of the MODIS LAI product for Australian ecosystems

The leaf area index (LAI) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) is important for monitoring and modelling global change and terrestrial dynamics at many scales. The algorithm relies on spectral reflectances and a six biome land cover classification. Evaluation of the specific behaviour and performance of the product for regions of the globe such as Australia are needed to assist with product refinement and validation. We made an assessment of Collection 4 of the MODIS LAI product using four approaches: (a) assessment against a continental scale Structural Classification of Australian Vegetation (SCAV); (b) assessment against a continental scale land use classification (LUC); (c) assessment against historical field-based measurement of LAI collected prior to the Terra Mission; and (d) direct comparison of MODIS LAI with coincident field measurements of LAI, mostly from hemispherical photography. The MODIS LAI product produced a wide variety of geographically and structurally specific temporal response profiles between different classes and even for sub-groups within classes of the SCAV. Historical and concurrent field measurements indicated that MODIS LAI was giving reasonable estimates for LAI for most cover types and land use types, but that major overestimation of LAI occurs in some eastern Australian open forests and woodlands. The six biome structural land cover classification showed some significant deviations in class allocation compared to the SCAV particularly where grasslands are allocated to shrubland, savanna woodlands are allocated to shrubland, savanna and broadleaf forest, and open forests are allocated to savanna and broadleaf forest. The land cover and LAI products could benefit from some additional examination of Australian data addressing the structural representation of Eucalypt canopies in the “space of canopy realisation” for savanna and broadleaf forest classes.