Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

We developed a global,30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer(MODIS)Vegetation Continuous Fields(VCF)Tree Cover layer using circa-2000 and 2005 Landsat images,incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs(RMSE=8.6%in 2000 and 11.9%in 2005),but showed improved accuracy in agricultural areas and increased discrimination of small forest patches.Against lidar measurements,the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF(RMSE=16.8%for MODIS-based vs.17.4%for Landsat-based estimates),but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region.The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80%tree cover but showed greater potential for removal of errors through calibration to lidar,with post-calibration RMSE of 9.4%compared to 13.5%in MODIS estimates.Provided for free download at the Global Land Cover Facility(GLCF)website(www.landcover.org),the 30-m resolution GLCF tree cover dataset is the highest-resolution multi=temporal depiction of Earth’s tree cover available to the Earth science community.

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to create global products of forest cover and cover change at Landsat resolutions.Nevertheless,there are many challenges in ensuring the creation of high-quality products.And we propose various ways in which the challenges can be overcome.Among the challenges are the need for atmospheric correction,incorrect calibration coefficients in some of the data-sets,the different phenologies between compila-tions,the need for terrain correction,the lack of consistent reference data for training and accuracy assessment,and the need for highly automated character-ization and change detection.We propose and evaluate the creation and use of surface reflectance products,improved selection of scenes to reduce phenological differences,terrain illumination correction,automated training selection,and the use of information extraction procedures robust to errors in training data along with several other issues.At several stages we use Moderate Resolution Spectro-radiometer data and products to assist our analysis.A global working prototype product of forest cover and forest cover change is included.

Integrating global land cover products for improved forest cover characterization: an application in North America

Six widely used coarse-resolution global land cover data-sets–Global Land Cover Characterization(GLCC),Global Land Cover 2000(GLC2000),GlobCover land cover product(GlobCover),MODIS land cover product(MODIS LC),the University of Maryland land cover product(UMD LC),and the MODIS Vegetation Continuous Fields tree cover layer(MODIS VCF)disagree substantially in their estimates of forest cover.Employing a regression tree model trained on higher-resolution,Landsat-based data,these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America.Evaluated using a withheld test sample,the integrated percent forest cover(IPFC)data-set has a root mean square error of 11.75%–substantially better than the 17.37% of GLCC,17.61% of GLC2000,17.96% of GlobCover,15.23% of MODIS LC,19.25%of MODIS VCF,and 15.15%of UMD LC,respectively.Although demonstrated for forest,this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.

A global,high-resolution(30-m)inland water body dataset for 2000:first results of a topographic-spectral classification algorithm

The science and management of terrestrial ecosystems require accurate,high-resolution mapping of surface water.We produced a global,30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates,multispectral water and vegetation indices,terrain metrics,and prior coarse-resolution water masks.The dataset identified 3,650,723 km2 of inland water globally–nearly three quarters of which was located in North America(40.65%)and Asia(32.77%),followed by Europe(9.64%),Africa(8.47%),South America(6.91%),and Oceania(1.57%).Boreal forests contained the largest portion of terrestrial surface water(25.03%of the global total),followed by the nominal‘inland water’biome(16.36%),tundra(15.67%),and temperate broadleaf and mixed forests(13.91%).Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high,with commission errors<4%and omission errors<14%relative to each.Most of these were accounted for in the seasonality of water cover,snow and ice,and clouds–effects which were compounded by differences in image acquisition date relative to reference datasets.The Global Land Cover Facility(GLCF)inland surface water dataset is available for open access at the GLCF website(http://www.landcover.org).

基于线状特征增强的TM遥感影像细小河流提取方法

混合像元效应是导致难以从TM影像中提取细小河流的主要原因。本文提出一种综合多种数字图像处理技术的细小河流自动识别方法。首先,利用阈值分割来区分水体指数影像中的细小河流与面状水体;然后,对水体指数进行线状特征增强,突出线状河流信息,并抑制其他地物信息;再利用双阈值线段追踪方法,提取影像中的细小河流;最后通过3种方法分别去除阴影、道路和其他类型噪声。结果表明,本文方法能有效地提取细小河流,同时排除多种噪声的干扰,结果的制图精度高于82%,用户精度高于93%,Kappa系数高于0.993,完整度高于90%。

2000-2013年青藏高原湖泊面积MODIS遥感监测分析

青藏高原上分布着大量的高原内陆湖泊群,该区域湖泊面积与区域及全球气候变化之间存在较强的耦合关系,遥感监测湖泊的分布和面积变化趋势,对分析区域自然生态环境具有重要意义。本研究将MOD09A1(地表反射率8天合成数据)进行逐月合成,提出了一种综合多种水体指数的青藏高原地区湖泊提取方法,并通过活动窗口、DEM和时间序列去噪等方法,消除山体阴影、冰雪等因素的干扰。最后,提取和合成了2000-2013年青藏高原逐年和逐月的湖泊范围,并选取色林错和卓乃湖2个典型湖泊与人工解译Landsat系列影像进行验证分析,其线性拟合度分别为0.99和0.97,从时空变化趋势上分析了青藏高原湖泊面积动态变化。结果表明:(1)2000-2013年,青藏高原地区湖泊范围整体上呈较显著的扩张趋势,湖泊总面积增加速率约为490.98 km2a-1(R2约为0.96);(2)1-12月份湖泊面积逐月变化率均大于0,表明青藏高原湖泊面积呈整体扩张,而非季节性扩张。除2-4月份外,其他月份增加速率均在400 km2a-1以上(R2>0.79),表现为稳定且持续扩张趋势。

The Reconnaissance of Mineral Resources through ASTER Data-Based Image Processing, Interpreting and Ground Inspection in the Jiafushaersu Area, West Junggar, China

Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） data covering the Jiafushaersu area in Xinjiang were used for mapping lithology and hydrothermal alteration. The study area situates at a potential mineralization zone in relation to small hypabyssai granodiorite or quartz monzonite intrusions along the margin of granitoid batholiths of Darbut foot wall. The false colour composition of bands 521 and the first three principal component analyses （PCA1, PCA2, PCA3） in RGB identify the lithological units and discriminate the small intrusions very well from the adjacent granitoid batholiths. PCA and spectral angle mapper （SAM） algorithm were employed to discriminate alteration minerals. The results indicate that the hydroxyl-bearing or ferric and less commonly carbonate types show good correlation with the quartz monzonite porphyry and aplite. Field verification led to finding of the Jiafushaersu molybdenum mineralization. The lithological and geochemical features imply that the molybdenum mineralization is close to the porphyry type. This study further verified that the foot wall of the Darbut suture could have served as a more important metallogenic district for the porphyry copper and molybdenum deposits. It is concluded that the ASTER data-based methods can be used as a powerful tool for small intrusion-type mineral resources targeting.

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data.However,data interlinking,which is the most valuable contribution of Linked Data,remains incomplete and inaccurate.This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain.According to the characteristics and roles of geospatial data in data discovery,eight elementary data characteristics are adopted as data interlinking types.These elementary characteristics are further combined to form compound and overall data interlinking types.Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively.Therefore,geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value.The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data.The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network(NSTI-GEO)and data-links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.

Isolating type-specific phenologies through spectral unmixing of satellite time series

Vegetation phenology is commonly studied using time series of multispectral vegetation indices derived from satellite imagery.Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing,and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution.We present an alternative method to mitigate this‘mixed-pixel problem’and extract the phenological behavior of individual land-cover types inferentially,by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping.Parameterized using genetic algorithms,the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red,near infrared,and short-wave infrared wavelengths,as well as the Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index.In simulation,the unmixing procedure reproduced the reflectances and phenological signals of grass,crop,and deciduous forests with high fidelity(RMSE<0.007 NDVI);and in empirical tests,the algorithm extracted the phenological characteristics of evergreen trees and seasonal grasses in a semi-arid savannah.The approach shows potential for a wide range of ecological applications,including detection of differential responses to climate,soil,or other factors among vegetation types.