Mid‐infrared video: A possible tool for thermal analysis of wildfires

This paper demonstrates the applicability of a black‐and‐white visible‐infrared sensitive video camera, filtered to record radiation in the 1.45–2.0 μm mid‐infrared (MIR) spectral region, for detecting burning wood coals. Airbome imagery of a burned rangeland area and a wood fire at a dump site showed that hot spots (smoldering wood coals) could be delineated from other landscape features. MIR video imagery should be useful to detect burning wood coals at temperatures as low as 900K. These results indicate that the MIR video system may be a potential tool to aid wild‐land managers in the thermal analysis of wildfires.     

Multiple band digital orthophoto quadrangle data: A source for generating land use maps

Abstract

The purpose of this study was to investigate the use of color infrared‐digital orthophoto quadrangle (CIR‐DOQ) data to generate land use/land cover (LULC) maps and to incorporate them as data layers in geographic information systems (GIS) involving various resource management scenarios. The Danville 7.5‐minute quadrangle located in the southern part of Limestone and Morgan counties, Alabama, was used as the study site. Data for the special CIR‐DOQ were generated by scanning four 9x9 inch CIR aerial photographs at a uniform pixel sample grid of 25 microns resulting in 2 meters ground sample resolution. One‐half of the quadrangle was used to identify training sites for performing a supervised classification of the data and the other half to verify the accuracy of the classification. The CIR‐DOQ data were found to be adequate for using a supervised classification algorithm to differentiate major LULC classes, resulting in a classification accuracy of 93 percent. The superior spatial quality of the data over commençai satellite data affords resource managers an opportunity to more effectively study land cover and surface hydrological properties of an area, soil moisture and surface soil textures, as well as differentiate among vegetation species, using remote sensing techniques. However, caution must be exercised when using multispectral classification techniques to classify mosaicked CIRDOQ data because of the image enhancements used to generate the final product. In its present form, there are some limitations to the use of the data for performing spectral classifications. Hozvever, the high spatial resolution of the data enables even the novice resource planner to effectively use the data in visual interpretations of major LULC classes.     

Towards 3D map generation from digital aerial images

This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated.     

Assessment of crop stress conditions using low altitude aerial color‐infrared photography and computer image processing

Stress conditions on various crops caused by moisture deficiency, nutrient deficiency and plant diseases were studied using low altitude aerial color‐infrared (CIR) photography. A remotely controlled aircraft, carrying a color video camera and a 35 mm reflex camera, flying at altitudes of 50 to 100 m, was used for obtaining aerial photography. The CIR transparencies recorded for different crops were scanned into an AT 386 desktop computer and processed by MIPS software using on screen interpretation to classify crop stress conditions. In a rootrot‐affected lemon orchard diseased trees could be identified and differentiated from healthy trees. Soybean plots under moisture stress were classified into high, moderate and low stress levels. These stress levels related well with infrared (IR) thermal measurements of the crop canopy. In a wheat field experiment that was fertilized at different levels of phosphorus (P), areas with P deficiency and sufficient P could be classified on the processed CIR image. Soil P and wheat yield correlated well with the classified CIR image. Results indicate that low altitude CIR aerial photography obtained from remotely controlled aircraft and image processing computers can be a relative inexpensive tool to assess crop stress conditions.     

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.     

Texture augmented detection of macrophyte species using decision trees

Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 × 5 and 13 × 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier.     

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。     

Combined Radar and Landsat data analysis for land use/cover studies over parts of the Punjab plains

L-band (HH) synthetic aperture radar imagery from Shuttle Imaging Radar-B (SIR-B) and Landsat multispectral scanner (MSS) images over parts of the Punjab plains were combined in order to utilize the complementary information contained in multispectral data sets. Among the various combination of Landsat MSS with SIR-B, the combination of Landsat MSS band 5 (0.6–0.7 μm) and band 7 (0.8–1.1 μm) with SIR-B data was found to be optimum in delineating landcover units. The integrated data was found to be superior in providing landcover information in comparison to SIR-B alone or a combination of landsat MSS band 4,5 and 7.     

制作高质量天绘彩色正射影像的方法分析

随着数字正射影像(DOM)的广泛应用,对正射影像的质量要求也越来越高。影像匀光、匀色处理是制作正射影像时的一个重要环节,它决定着正射影像色彩的优劣。本文主要介绍以低层级谷歌影像为参考基准色,对天绘多光谱影像DGP进行颜色匹配法匀光、匀色;利用波段调整功能对天绘多光谱影像DGP进行绿色增强;采用Pansharp最小二乘法对多光谱影像DGP和高分辨率影像GFB进行影像融合,从而获得如谷歌影像一样丰富的纹理层次感强、色彩真实的彩色天绘正射影像。     

Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

Jack pine budworm (Choristoneura pinus pinus (Free.)) is a native insect defoliator of mainly jack pine (Pinus banksiana Lamb.) in North America east of the Rocky Mountains. Periodic outbreaks of this insect, which generally last two to three years, can cause growth loss and mortality and have an important impact ecologically and economically in terms of timber production and harvest. The jack pine budworm prefers to feed on current year needles. Their characteristic feeding habits cause discolouration or reddening of the canopy. This red colouration is used to map the distribution and intensity of defoliation that has taken place that year (current defoliation). An accurate and consistent map of the distribution and intensity of budworm defoliation (as represented by the red discolouration) at the stand and within stand level is desirable.Automated classification of multispectral imagery, such as is available from airborne and new high resolution satellite systems, was explored as a viable tool for objectively classifying current discolouration. Airborne multispectral imagery was acquired at a 2.5 m resolution with the Multispectral Electro-optical Imaging Sensor (MEIS). It recorded imagery in six nadir looking spectral bands specifically designed to detect discolouration caused by budworm and a near-infrared band viewing forward at 35° was also used. A 2200 nm middle infrared image was acquired with a Daedalus scanner. Training and test areas of different levels of discolouration were created based on field observations and a maximum likelihood supervized classification was used to estimate four classes of discolouration (nil-trace, light, moderate and severe). Good discrimination was achieved with an overall accuracy of 84% for the four discolouration levels. The moderate discolouration class was the poorest at 73%, because of confusion with both the severe and light classes. Accuracy on a stand basis was also good, and regional and within stand discolouration patterns were portrayed well. Only three or four well-placed spectral bands were needed for a good classification. A narrow red band, a near-infrared and short wave infrared band were most useful. A forward looking band did not improve discolouration estimation, but further testing is needed to confirm this result.This method of detecting and classifying discolouration appears to provide a mapping capability useful for conducting jack pine budworm discolouration surveys and integrating this information into decision support systems, forest inventory, growth and yield predictions and the forest management decision-making process.     

基于神经网络的SPOT数据模拟真彩色非线性方法研究

在总结现有真彩色模拟方法的基础上,提出了一种基于神经网络非线性逼近机制的真彩色模拟方法.该方法充分利用神经网络强大的学习能力,建立波段之间复杂的非线性映射关系.在具体实现上,首先通过对具有真彩色波段TM/ETM+数据的学习训练,得到各个网络节点的权重参数; 然后将该组权重参数应用到SPOT影像,模拟出蓝光波段,实现SPOT真彩色合成.在新疆伊犁地区林地调查应用结果表明,非线性方法要明显优于传统的真彩色模拟方法.     

关于新一代激光雷达系统

激光雷达系统配备有当今世界上最高分辨率的彩色数码相机和多光谱航拍相机。它同时集常用测量装置和全球定位装置于一身 ,当飞机机载该装置飞越地球表面时 ,它们能在获取所需图像及数据的同时 ,计算出传感器的精确位置和取向 ,给出数字高程图 (DEM)及全数码彩色影像。数码相机的底部配备着一个超大像幅的CCD传感器。该激光雷达系统还配置有一个高分辨率多光谱成形相机 ,因此多谱段像素直接与XYZ坐标值相对应 ,得以自动清晰地区分道路、建筑、树林、河流和其他地貌     

Water body mapping method with HJ-1A/B satellite imagery

This paper proposes an integrated water body mapping method with HJ-1A/B satellite imagery, the CCD (charge coupled device) data of the Chinese environmental satellites that were launched on September 6th, 2008. It combines the difference between NDVI and NDWI (NDVI–NDWI) with SLOPE and near-infrared (NIR) band. The NDVI–NDWI index is used to enhance the contrast between water bodies and the surrounding surface features; the topographic SLOPE is used to eliminate the mountain shadow; and the NIR band is used to reduce the effects of artificial construction land. The objectives are evaluating the potential of the HJ-1A/B imagery on water body monitoring, and proposing ideally mapping method. The test study results indicated that the NDVI–NDWI index is superior to the single index of NDVI and NDWI to enhance the contrast between water bodies and the rest of the features. On the basis of the accurately mapped water bodies in the HJ-1A/B CCD images of the study area, we conclude that the HJ-1A/B multi-spectral satellite images is an ideal data source for high spatial and temporal resolution water bodies monitoring. And the integrated water body mapping method is suitable for the applications of HJ-1A/B multi-spectral satellite images in this field.     

Progress in the Development of a High Performance Airborne Digital Sensor

Joint development work by LH Systems and Deutsches Zentrum für Luft- und Raumfahrt (German Aerospace Center) has produced encouraging results using forward, nadir and backward looking linear arrays on the focal plane to provide panchromatic imagery and geometric information, supplemented by further arrays to acquire multispectral imagery suitable for both high precision photogrammetric mapping and image processing for interpretative purposes. The geometric characteristics of line scanner imagery necessitate line-by-line rectification for aircraft tilts and shifts. Satisfactory execution of this process is enhanced by using supplementary data from high performance, on-board GPS and inertial measurement systems. Similarly, high demands are placed on other sub-systems, such as the camera mount, lens, electronics and storage technology. In addition to rectification for aircraft tilts and shifts, rectification for terrain characteristics is also required in order to generate colour and false colour composite images, since the various multispectral arrays are in different places on the focal plane. The special geometry affects triangulation. Thereafter, the imagery can be processed using existing software packages from both photogrammetry and remote sensing.
The concept has been demonstrated in several successful test flights and the production model is scheduled for market introduction at the ISPRS Congress in July 2000. The imagery from the new sensor will fulfil many market requirements between the highest resolution film imagery (<0.1 m) and high resolution space imagery (1m to 10 m). The sensor's unique blend of multispectral information with high quality geometric information will give rise to numerous new applications.     

A six-camera digital video imaging system sensitive to visible,red edge,near-infrared,and mid-infrared wavelengths

This paper describes a six-camera multispectral digital video imaging system designed for natural resource assessment and shows its potential as a research tool. It has five visible to near-infrared light sensitive cameras, one near-infrared to mid-infrared light sensitive camera, a monitor, a computer with a multichannel digitizing board, a keyboard, a power distributor, an amplifier, and a mouse. Each camera is fitted with a narrowband interference filter, allowing the system to obtain imagery in the blue (447 – 455 nm), green (555 – 565 nm), red (625 – 635 nm), red edge (704 – 716 nm), near-infrared (814–826 nm), and mid-infrared (1631 – 1676 nm) regions of the electromagnetic spectrum. Analogue video acquired by this system is converted to digital format. Radiometric resolution of the imagery is 8-bit (pixel values range 0 – 255). Images obtained by the system can be evaluated individually and/or in combination with each other to assess natural resources.     

Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAir^TM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS–NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm⁻², efficiency of 0.76, and 9 relevance vectors.     

Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city

ABSTRACT

Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.     

The design and the development of a hyperspectral and multispectral airborne mapping system

Flexible and cost-effective tools for rapid image acquisition and natural resource mapping are needed by land managers. This paper describes the hardware and software architecture of a low-cost system that can be deployed on a light aircraft for rapid data acquisition. The Hyperspectral and Multispectral Cameras for Airborne Mapping (HAMCAM) was designed and developed in the Geospatial Laboratory for Environmental Dynamics at the University of Idaho as a student-learning tool, and to enhance the existing curriculum currently offered. The system integrates a hyperspectral sensor with four multispectral cameras, an Inertial Navigation System (INS), a Wide Area Augmentation System (WAAS)-capable Global Positioning System (GPS), a data acquisition computer, and custom software for running the sensors in a variety of different modes. The outputs include very high resolution imagery obtained in four adjustable visible and near-infrared bands from the multispectral imager. The hyperspectral sensor acquires 240 spectral bands along 2.7 nm intervals within the 445–900 nm range. The INS provides aircraft pitch, roll and yaw information for rapid geo-registration of the imagery. This paper will discuss the challenges associated with the development of the system and the integration of components and software for implementation of this system for natural resource management applications. In addition, sample imagery acquired by the sensor will be presented.     

Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B

China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images.