Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.     

Identification and mapping of submerged plants in a shallow lake using Quickbird satellite data

Turkey is a country rich in lakes and wetlands--monitoring of all these will require advances in technology such as remote sensing. In this study, the aquatic plants of the large and shallow Lake Mogan, located in Central Anatolia were identified and mapped using high spatial resolution Quickbird imagery. As Lake Mogan is an important bird area the assessment of submerged plant species is of great value for ecosystem conservation and management. Quickbird multispectral image acquired on August 6, 2005 was geometrically corrected and a water mask was used based on strong absorption of Near Infrared (NIR) wavelengths by calm, clear and deep water. The water mask was applied using band reflectance values for a specific pixel satisfying the conditions of band decreasing property (Green>Red>NIR) and NIR相似文献   

利用数码相片对冬小麦生物量的试算

数码相片属于地面遥感资料的一种,被广泛应用于实地调查中.然而,目前利用数码相片对植被生物量进行推算的研究很少.利用数码相片提取小麦生长期的小麦覆盖度,结合地面实测叶面积指数(LAI)、归一化植被指数(NDVI)和小麦生物量,分析小麦覆盖度与LAI,NDVI,生物量与小麦覆盖度、LAI,NDVI各自的相关系数,建立各自合适的回归模型推算冬小麦的生物量.结果表明,在小麦生长期,小麦覆盖度与LAI,NDVI的相关系数R2分别达到0.765,0.896,生物量与小麦覆盖度、LAI,NDVI的相关系数R2分别为0.774,0.876,0.712,生物量与其小麦覆盖度、LAI,NDVI之间具有很高的相关性,多元回归分析效果更好,系数R2达0.891.结果说明,在不破坏植被生长状态的情况下,基于数码相片可有效地推算小麦的生物量,这为大面积实地测量和遥感监测作物长势及其生物量估算提供了理论参考依据.     

Use of Radar Remote Sensing (RADARSAT) to Map Winter Wetland Habitat for Shorebirds in an Agricultural Landscape

Many of todays agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while class 3 coverage decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (30% coverage) in December and January, decreasing in March by 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. Also, Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon 97331, USA     

An introduction to digital methods in remote sensing of forested ecosystems: Focus on the Pacific Northwest,USA

Aerial photography has been routinely used for several decades by natural resource scientists and managers to map and monitor the condition of forested landscapes. Recently, along with the emergence of concepts in managing forests as ecosystems, has come a significant shift in emphasis from smaller to larger spatial scales and the widespread use of geographic information systems. These developments have precipitated an increasing need for vegetation information derived from other remote sensing imagery, especially digital data acquired from high-elevation aircraft and satellite platforms. This paper introduces fundamental concepts in digital remote sensing and describes numerous applications of the technology. The intent is to provide a balanced, nontechnical view, discussing the shortcomings, successes, and future potential for digital remote sensing of forested ecosystems.     

Use of Radar Remote Sensing (RADARSAT) to Map Winter Wetland Habitat for Shorebirds in an Agricultural Landscape

Many of todays agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. Also, Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon 97331, USA     

REMOTE SENSING OF RIPARIAN BUFFERS: PAST PROGRESS AND FUTURE PROSPECTS1

Riparian buffer zone management is an area of increasing relevance as human modification of the landscape continues unabated. Land and water resource managers are continually challenged to maintain stream ecosystem integrity and water quality in the context of rapidly changing land use, which often offsets management gains. Approaches are needed not only to map vegetation cover in riparian zones, but also to monitor the changes taking place, target restoration activities, and assess the success of previous management actions. To date, these objectives have been difficult to meet using traditional techniques based on aerial photos and field visits, particularly over large areas. Recent advances in remote sensing have the potential to substantially aid buffer zone management. Very high resolution imagery is now available that allows detailed mapping and monitoring of buffer zone vegetation and provides a basis for consistent assessments using moderately high resolution remote sensing (e.g., Landsat). Laser‐based remote sensing is another advance that permits even more detailed information on buffer zone properties, such as refined topographic derivatives and multidimensional vegetation structure. These sources of image data and map information are reviewed in this paper, examples of their application to riparian buffer mapping and stream health assessment are provided, and future prospects for improved buffer monitoring are discussed.     

Observation and simulation of net primary productivity in Qilian Mountain, western China

We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4 tCha(-1)y(-1). The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.     

Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing

Recent advances in remote sensing provide opportunities to map plant species and vegetation within wetlands at management relevant scales and resolutions. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space-based sensors that can document detailed information on the distribution of vegetation community types, and sometimes species. Development of spectral libraries of wetland species is a key component needed to facilitate advanced analytical techniques to monitor wetlands. Canopy and leaf spectra at five sites in California, Texas, and Mississippi were sampled to create a common spectral library for mapping wetlands from remotely sensed data. An extensive library of spectra (n=1336) for coastal wetland communities, across a range of bioclimatic, edaphic, and disturbance conditions were measured. The wetland spectral libraries were used to classify and delineate vegetation at a separate location, the Pacheco Creek wetland in the Sacramento Delta, California, using a PROBE-1 airborne hyperspectral data set (5m pixel resolution, 128 bands). This study discusses sampling and collection methodologies for building libraries, and illustrates the potential of advanced sensors to map wetland composition. The importance of developing comprehensive wetland spectral libraries, across diverse ecosystems is highlighted. In tandem with improved analytical tools these libraries provide a physical basis for interpretation that is less subject to conditions of specific data sets. To facilitate a global approach to the application of hyperspectral imagers to mapping wetlands, we suggest that criteria for and compilation of wetland spectral libraries should proceed today in anticipation of the wider availability and eventual space-based deployment of advanced hyperspectral high spatial resolution sensors.     

基于NDVI的大同煤田植被覆盖度动态变化研究

利用大同煤田地区1987年9月10日、2000年9月2日和2010年9月6日的TM遥感影像数据,采用基于NDVI的像元二分模型法反演了植被覆盖度,获取了该地区3个时期的植被覆盖度等级图,定量分析了该地区在大规模煤炭开采影响下的植被覆盖度的动态变化情况.研究结果表明1987年～2010年近24年以来,大同煤田地区植被覆盖度呈较明显的下降趋势,植被退化较严重.     

QuickBird影像融合最佳波段选择及不同融合方法质量评价研究

分析了QuickBird多光谱遥感影像各个波段的光谱特征,并进行了定量评价。根据信息量的大小和波段相关性得到最佳波段组合,在最佳组合波段下利用遥感影像处理软件ERDAS9.2选择或利用建模模块建立不同的影像融合模型对QuikBird多光谱数据与全色遥感影像进行融合,按照定性及定量指标对融合前后的遥感影像进行色彩、分辨率及信息量评价,得到QuikBird影像数据最佳融合方法。     

Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.     

SOIL MOISTURE VARIATION PATTERNS OBSERVED IN HAND COUNTY,SOUTH DAKOTA1

ABSTRACT: Sail moisture data were taken during nine sampling events (1976-1978) at a test site in South Dakota as part of the ground truth used in NASA's aircraft experiments studying the microwave sensing of soil moisture. This portion of the study dealt only with the spatial variability observed with regard to the ground data. Samples were taken over three surface depths at each point, and the data reported as the mean field moisture content within each of three surface horizons. The results shed additional light on the relationship between ground sampling and remote sensing of soil moisture. First, it was found that it is best to partition data of well drained sites from poorly drained areas when attempting to characterize the surface moisture content throughout an area of varying soil and cover conditions. It was also found that the moisture coefficient of variation within a field decreased as the mean field soil moisture increased, and that the standard deviation was at a maximum in the mid-range of observed moisture conditions (15-25 percent). Within field sample variation also decreases as the sample is integrated over a greater surface depth. It was determined that a sampling intensity of 10 samples per kilometer was adequate to characterize the mean field soil moisture at all three depths along a transect in the areas of moderate to good drainage-.     

Observations of relative humidity in the near-wake of a wind turbine using an instrumented unmanned aerial system

Simulation, modeling, and limited observations have shown that wind farms have an impact on the near-surface atmospheric boundary layer as turbulent wakes enhance vertical mixing of momentum, heat, and moisture. The few observational datasets that do exist lack high spatial resolution due to their use of a limited number of meteorological sensors or remote sensing techniques. This study utilizes an instrumented small unmanned aerial system to gather high-resolution in situ field measurements in order to differentially map near-wake changes to relative humidity. Observations show that downstream relative humidity is differentially altered in the vertical, spanwise and downstream directions.     

遥感调查方法在河北省省级自然保护区生态环境变化评估方面的应用研究

河北省省级自然保护区以自然生态系统类型为主,人类活动较为频繁,将遥感评估方法应用于河北省省级自然保护区生态环境变化评估具有重要意义。本文通过分析高分辨率遥感卫星影像的特点,结合河北省省级自然保护区的实际情况,优先选取分辨率为2m的民用卫星,提出了遥感调查方法在自然保护区人类活动和开发建设活动中自动监测、生态系统格局变化、生态系统质量变化以及人类活动干扰程度四方面的评估应用。     

Emergence of Indigenous Vegetation Classifications Through Integration of Traditional Ecological Knowledge and Remote Sensing Analyses

Traditional ecological knowledge (TEK) can play an important role in the understanding of ecological systems. Although TEK has complemented scientific and managerial programs in a variety of contexts, its formal incorporation into remote sensing exercises has to date been limited. Here, we show that the vegetation classifications of the Ache, an indigenous hunter-gatherer tribe of the Mbaracayu Forest Reserve in Paraguay, are reflected in a supervised classification of satellite imagery of the reserve. Accuracy of classification was toward the low end of the range of published values, but was reasonable given the difficult nature of separating forest classes from satellite images. Comparison of the resultant map with a more traditionally elaborated vegetation map highlights differences between the two approaches and the gain in information obtained by considering TEK classifications. We suggest that integration of TEK and remote sensing may provide alternative insights into the ecology of vegetation communities and land cover, particularly in remote and densely forested areas where ecological field research is often limited by roads and/or trail systems.     

Ground-Cover Measurements: Assessing Correlation Among Aerial and Ground-Based Methods

Wyoming’s Green Mountain Common Allotment is public land providing livestock forage, wildlife habitat, and unfenced solitude, amid other ecological services. It is also the center of ongoing debate over USDI Bureau of Land Management’s (BLM) adjudication of land uses. Monitoring resource use is a BLM responsibility, but conventional monitoring is inadequate for the vast areas encompassed in this and other public-land units. New monitoring methods are needed that will reduce monitoring costs. An understanding of data-set relationships among old and new methods is also needed. This study compared two conventional methods with two remote sensing methods using images captured from two meters and 100 meters above ground level from a camera stand (a ground, image-based method) and a light airplane (an aerial, image-based method). Image analysis used SamplePoint or VegMeasure software. Aerial methods allowed for increased sampling intensity at low cost relative to the time and travel required by ground methods. Costs to acquire the aerial imagery and measure ground cover on 162 aerial samples representing 9000 ha were less than $3000. The four highest correlations among data sets for bare ground—the ground-cover characteristic yielding the highest correlations (r)—ranged from 0.76 to 0.85 and included ground with ground, ground with aerial, and aerial with aerial data-set associations. We conclude that our aerial surveys are a cost-effective monitoring method, that ground with aerial data-set correlations can be equal to, or greater than those among ground-based data sets, and that bare ground should continue to be investigated and tested for use as a key indicator of rangeland health.     

高分辨率遥感影像在城市绿地信息提取中的应用研究

高分辨率卫星遥感图像数据量大、空间分辨率高、结构信息复杂、地物同物异谱现象更为突出等特征给专题信息提取技术带来了新的挑战。基于植被的光谱特征,利用监督分类、植被指数分类和目视解译等方法对QuickBird高分辨率卫星遥感影像的绿地信息进行提取,并对分类精度作了比对分析。研究结果表明,监督分类方法不能得到令人满意的结果,运用植被指数分类方法则有明显改善,其中归一化植被指数(NDVI)精度最高,因此NDVI能有效地对植被进行分类与识别。     

Remote sensing for land management and planning

The primary role of remote sensing in land management and planning has been to provide information concerning the physical characteristics of the land which influence the management of individual land parcels or the allocation of lands to various uses These physical characteristics have typically been assessed through aerial photography, which is used to develop resource maps and to monitor changing environmental conditions These uses are well developed and currently well integrated into the planning infrastructure at local, state, and federal levels in the United States.Many newly emerging uses of remote sensing involve digital images which are collected, stored, and processed automatically by electromechanical scanning devices and electronic computers Some scanning devices operate from aircraft or spacecraft to scan ground scenes directly; others scan conventional aerial transparencies to yield digital images. Digital imagery offers the potential for computer-based automated map production, a process that can significantly increase the amount and timeliness of information available to land managers and planners.Future uses of remote sensing in land planning and management will involve geographic information systems, which store resource information in a geocoded format. Geographic information systems allow the automated integration of disparate types of resource data through various types of spatial models so that with accompanying sample ground data, information in the form of thematic maps and/ or aerially aggregated statistics can be produced Key issues confronting the development and integration of geographic information systems into planning pathways are restoration and rectification of digital images, automated techniques for combining both quantitative and qualitative types of data in information-extracting procedures, and the compatibility of alternative data storage modes     

洪水灾害遥感监测研究综述

洪水灾害是最严重的自然灾害之一,洪灾造成的损失十分严重,对其进行科学的监测是防灾减灾的基础。在洪水灾害遥感监测研究进展的介绍基础上,着重对中分辨率、高时相、微波、高精度DEM、多源数据遥感洪水监测原理及方法的研究进展进行比较和总结,分析各种数据特点。在此基础上提出了洪水遥感监测向高分辨率、高时相性方向,遥感影像相互订正和利用3S技术是洪涝灾害动态监测发展的方向,为洪水灾害的快速反应和防洪辅助决策提供依据。