Lightning flash mensuration using video from the space shuttle Columbia (STS‐32)

Abstract

Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

Combining AVHRR imagery with CORINE Land Cover data to observe forest fires and to assess their consequences

This paper describes an operational application of AVHRR satellite imagery in combination with the satellite-based land cover database CORINE Land Cover (CLC) for the comprehensive observation and follow-up of 10000 fire outbreaks and of their consequences in Greece during summer 2000. In the first stage, we acquired and processed satellite images on a daily basis with the aim of smoke-plume tracking and fire-core detection at the national level. Imagery was acquired eight times per day and derived from all AVHRR spectral channels. In the second stage, we assessed the consequences of fire on vegetation by producing a burnt-area map on the basis of multi-annual normalised vegetation indices using AVHRR data in combination with CLC. In the third stage we used again CLC to assess the land cover of burnt areas in the entire country. The results compared successfully to available inventories for that year. Burnt area was estimated with an accuracy ranging from 88% to 95%, depending on the predominant land cover type. These results, along with the low cost and high temporal resolution of AVHRR satellite imagery, suggest that the combination of low-resolution satellite data with harmonised CLC data can be applied operationally for forest fire and post-fire assessments at the national and at a pan-European level.     

Multi‐Season Atmospheric Normalization of NOAA AVHRR Derived NDVI for Crop Yield Modeling

Abstract

A methodology has been developed to normalize the multi‐temporal NDVIs derived from NOAA AVHRR data for the atmospheric effects to the least affected NDVI for development of spectral and spectrometeorological (or spectromet, for short) crop yield models. This is found to reduce the noise in NDVI due to varying atmospheric conditions from season to season and improve the predictability of statistical multiple linear regression yield models. The spectromet yield models for mustard crop in the nine districts of Rajasthan state haven been developed based on normalized NDVIs and have been validated by comparing the predicted yields with the estimated from crop cutting experiments by the state Development of Agriculture.     

Knowledge‐based digital mapping of vegetation types in Big Bend National park,Texas

A knowledge‐based strategy is utilized to develop a model for performing automated mapping of twenty vegetation cover types occurring within Big Bend National P ark, Texas. Many of the cover types found within this desert region cannot be reliably identified solely on a spectral basis, even on large‐scale, aircraft‐borne color imagery. Positive identification may be improved, however, by incorporating additional spatial information that may distinguish given cover types on a non‐spectral basis. In this study, digital soils and digital terrain data are utilized with spectral imagery from Landsat Thematic Mapper.

This knowledge‐based strategy is comprised of three primary elements: knowledge acquisition, rules development, and model structuring. Knowledge acquisition identifies the vegetation composition and non‐vegetative site characteristics associated with the occurrence of each cover type. Rules development compares and contrasts these characteristics among pairs of cover types and their subsets Model structuring places the presumed digital analogs of these characteristics within a multi‐layered classification.

After implementing the automated mapping model, its quality was evaluated with an accuracy assessment. Based upon the cover types field‐truthed at 142 sites within the park, the model performed at an 72% level of accuracy. For comparative purposes, a traditional supervised (spectral, statistical) classification yielded a 42% accuracy. The superiority of the model is attributed to its incorporation of knowledge‐based information; in essence, identification by considering only those cover types likely to occur over given spectral and physiographic conditions.     

Wheat growth profile: satellite monitoring and crop yield modelling

The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas, and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops, these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1) growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected growth periods have been related to the district average wheat yield through statistical regression models. Various methods adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by the area under the profile in different growth periods.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

高分辨率影像城市绿地快速提取技术与应用

高分辨率遥感影像是城市绿地信息快速提取的主要数据源 ,文中以多尺度影像分割与面向对象影像分析方法为主要技术 ,利用样本多边形对象的成员函数建立训练区 ,自动提取大庆市城市绿地覆盖信息 ,达到清查城市绿地的目的。该方法信息获取周期短、精度高、成本低 ,实现了城市绿地信息精确获取与快速更新。     

The 13th Asian conference on remote sensing,Mongolia

Abstract

Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi‐layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18^th, 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co‐occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co‐occurrence texture and spectral signatures combined, and a thirteen‐class multi‐layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per‐plot classification accuracies by 15.7%, compared to the use of the spectral data alone.     

Remote sensing and geoinformation center of Mongolia: A brief outlook

Abstract

A GIS based approach is proposed for the integration of three thematic maps viz. geomorphology, drainage density and slope using fuzzy logic for the assessment of ground water resource potential of a soft rock terrain of Midnapur District, West Bengal, India. The geomorphology and drainage density maps of the area are prepared from IRS‐1B LISS‐II data, and the slope map is obtained from the contours depicted on the topographic map of Survey of India. Each feature of all the thematic maps is assigned with individual fuzzy set values within a range between 0 to 1 according to their relative importance in the prediction of ground water occurrence. The maps are then integrated through fuzzy operation to model the ground water potential zone of the study area. The evolved model while verified with surface geophysical results is found to be in good agreement.     

Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data

ABSTRACT

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30?m) resolution down to 2–4?m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83?K prior to and 0.76–1.21?K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.     

Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous,dry savannahs

Woody canopy cover (CC) is the simplest two dimensional metric for assessing the presence of the woody component in savannahs, but detailed validated maps are not currently available in southern African savannahs. A number of international EO programs (including in savannah landscapes) advocate and use optical LandSAT imagery for regional to country-wide mapping of woody canopy cover. However, previous research has shown that L-band Synthetic Aperture Radar (SAR) provides good performance at retrieving woody canopy cover in southern African savannahs. This study’s objective was to evaluate, compare and use in combination L-band ALOS PALSAR and LandSAT-5 TM, in a Random Forest environment, to assess the benefits of using LandSAT compared to ALOS PALSAR. Additional objectives saw the testing of LandSAT-5 image seasonality, spectral vegetation indices and image textures for improved CC modelling. Results showed that LandSAT-5 imagery acquired in the summer and autumn seasons yielded the highest single season modelling accuracies (R² between 0.47 and 0.65), depending on the year but the combination of multi-seasonal images yielded higher accuracies (R² between 0.57 and 0.72). The derivation of spectral vegetation indices and image textures and their combinations with optical reflectance bands provided minimal improvement with no optical-only result exceeding the winter SAR L-band backscatter alone results (R² of ∼0.8). The integration of seasonally appropriate LandSAT-5 image reflectance and L-band HH and HV backscatter data does provide a significant improvement for CC modelling at the higher end of the model performance (R² between 0.83 and 0.88), but we conclude that L-band only based CC modelling be recommended for South African regions.     

Use of Very High Spatial Resolution Remotely Sensed Imagery for Assessing Land‐Cover Changes in Shrub Habitat Preserves of Southern California

Abstract

The goal of this research was to explore the utility of very high spatial resolution, digital remotely sensed imagery for monitoring land‐cover changes in habitat preserves within southern California coastal shrublands. Changes were assessed for Los Penasquitos Canyon Preserve, a large open space in San Diego County, over the 1996 to 1999 period for which imagery was available.

Multispectral, digital camera imagery from two summer dates, three years apart, was acquired using the Airborne Data Acquisition and Registration (ADAR) digital‐camera system. These very high resolution (VHR) image data (1m), composed of three visible and one near‐infrared wavebands (V/NIR), were the primary image input for assessing land cover change. Image‐derived datasets generated from georeferenced and registered ADAR imagery included multitemporal overlays and multitemporal band differencing with threshold selection. Two different multitemporal image classifications were generated from these datasets and compared. Single‐date imagery was analyzed interactively with image‐derived datasets and with information from field observations in an effort to discern change types. A ground sampling survey conducted soon after the 1999 image acquisition provided concurrent ground reference data.

Most changes occurring within the three‐year interval were associated with transitional phenological states and differential precipitation effects on herbaceous cover. Variations in air temperatures and timing of rainfall contributed to differences that the seven‐week image acquisition offset may have caused. Disturbance factors of mechanical clearing, erosion, potentially invasive plants, and fire were evident and their influence on the presence, absence, and type of vegetation cover were likely sources of change signals.

The multitemporal VHR, V/NIR image data enabled relatively fine‐scale land cover changes to be detected and identified. Band differencing followed by multitemporal classification provided an effective means for detecting vegetation increase or decrease. Detailed information on short‐term disturbance effects and long‐term vegetation type conversions can be extracted if image acquisitions are carefully planned and geometric and radiometric processing steps are implemented.     

利用多光谱影像检测资源三号卫星平台震颤

卫星平台震颤是影响高分辨率卫星成像质量的因素之一,会引起影像模糊和内部畸变。本文从资源三号卫星多光谱相机的成像特点和多光谱影像配准误差影响因素入手,理论推导和仿真分析了卫星平台震颤对配准误差的影响规律,在此基础上提出了基于多光谱影像高精度密集匹配的平台震颤检测方法和流程,最后利用不同波段、不同时间的成像数据进行试验。试验结果表明资源三号卫星在试验数据成像阶段存在约0.6Hz的平台震颤,且垂轨方向震颤幅值大于沿轨方向,同时引起波段间相同频率周期性配准误差。检测结果为进一步提高资源三号处理精度提供了可能,也为卫星平台震颤源的分析和优化卫星平台设计提供了重要参考依据。     

Land Cover and Vegetation Change in the Yellow River Delta Nature Reserve Analyzed with Landsat Thematic Mapper Data

Abstract

Coastal wetland is a major part of wetlands in the world. Land cover and vegetation mapping in a deltaic lowland environment is complicated by the rapid and significant changes of geomorphic forms. Remote sensing provides an important tool for coastal land cover classification and landscape analysis. The study site in this paper is the Yellow River Delta Nature Reserve (YRDNR) at the Yellow River mouth in Shangdong province, China. Yellow River Delta is one of the fastest growing deltas in the world. YRDNR was listed as a national level nature reserve in 1992. The objectives of this paper are two fold: to study the land cover status of YRDNR, and to examine the land cover change since it was declared as a nature reserve. Land cover and vegetation mapping in YRDNR was developed using multi‐spectral Landsat Thematic Mapper (TM) imagery acquired in 1995. Land cover and landscape characteristics were analyzed with the help of ancillary GIS. Land use investigation data in 1991 were used for comparison with Landsat classification map. Our results show that YRDNR has experienced significant landscape change and environmental improvement after 1992.     

Multi‐temporal Mapping of Burned Forest over Canada Using Satellite‐based Change Metrics

Abstract

A procedure for continental‐scale mapping of burned boreal forest at 10‐day intervals was developed for application to coarse resolution satellite imagery. The basis of the technique is a multiple logistic regression model parameterized using 1998 SPOT‐4 VEGETATION clear‐sky composites and training sites selected across Canada. Predictor features consisted of multi‐temporal change metrics based on reflectance and two vegetation indices, which were normalized to the trajectory of background vegetation to account for phenological variation. Spatial‐contextual tests applied to the logistic model output were developed to remove noise and increase the sensitivity of detection. The procedure was applied over Canada for the 1998‐2000 fire seasons and validated using fire surveys and burned area statistics from forest fire management agencies. The area of falsely mapped burns was found to be small (3.5% commission error over Canada), and most burns larger than 10 km2 were accurately detected and mapped (R² = 0.90, P<0.005, n = 91 for burns in two provinces). Canada‐wide satellite burned area was similar, but consistently smaller by comparison to statistics compiled by the Canadian Interagency Forest Fire Centre (by 17% in 1998, 16% in 1999, and 3% in 2000).     

利用AVHRR植被指数数据集分析中国东部季风区的物候季节特征

利用ＮＯＡＡ／ＮＡＳＡＰａｔｈｆｉｎｄｅｒＡＶＨＲＲ陆地数据集,建立了中国东部季风区（１０８°－１２３°Ｅ,２１°－４５°Ｎ）的１９８６年归一化植被指数（ＮＤＶＩ）距平图像序列。对此数据集进行主成分分析（ＰＣＡ）,前２个主成分的时间序列和空间场展示了中国东部季风区植被物候季节性特征和地域差异。南岭一五夷山以南的华南地区,植被生长季的物候季节性变化不明显。在南岭－五夷山以北地区,植被生长季的物候季节性特征明显,可以比较清晰地确定生长季的变化过程。以淮河流域为界,植被生长季的物候季节性特征又存在明显差异。华北平原表现出强烈的双峰植被物候过程。淮河以南地区,虽然也存在这种双峰物候过程,但比较华北平原的植被,还具有持续性的植被生长特征。淮河流域构成一条区分南北物候季节特征差异的过渡带。     

Integration of Satellite Telemetry Data and Land Cover Imagery: A Study of Migratory Cranes in Northeast Asia

The present study describes a procedure for quantitatively analyzing satellite telemetry data to identify interspecific land use differences among four threatened crane species. The inherent inaccuracy of satellite telemetry data points, the temporal autocorrelation of those points, and the resolution of two land‐cover imagery products from the IGBP‐DISCover Global Land‐Cover Characterization Project (derived from AVHRR data) were assessed and integrated in a GIS. Satellite telemetry is a system where animals are tracked using battery‐operated transmitters and locations are calculated using triangulation from satellites. Using the variable spatial inaccuracy of the telemetry locations, each point was buffered using a radius based on the accuracy of the point, and then intersected with the land cover imagery. The research concluded that the methodology is valuable for studies of birds at a regional scale, with interspecific differences clearly evident, but that diurnal and nocturnal differences were not discernable due to the coarse resolution of both satellite telemetry and land‐cover data.