ESTIMATES OF THE SENSITIVITY OF COMBINATIONS OF SPECTRAL RADIANCE COEFFICIENTS TO PROPERTIES OF THE VEGETATION COVER

A method is proposed for the calculation, based on the Goudriaan model, of estimates of various combinations of spectral radiance coefficients P_i which are optimal for interpretation of soil color and moisture, vegetation cover density, above-ground reserves of phytomass, and color and orientation of phytoelements. These estimates correspond rather closely with actual measurements obtained from the field. Translated from: Vestnik Moskovskogo Universiteta, geografiya, 1986, No. 4, pp. 75-79.     

THE MULTIPLICITY PRINCIPLE IN MODERN REMOTE SENSING METHODS AND ITS APPLICATION TO AGRICULTURAL IMAGE INTERPRETATION

The authors elaborate upon the “multiplicity principle” in remote sensing, i.e., the need for repeated imaging at a variety of scales, spatial resolutions, spectral bands, and times of imaging in order to attain the maximum information possible. They then explore the ways it can be applied in agricultural research, through two different image comparison and interpretation strategies. A detailed example is presented of the use of a multitemporal imaging strategy for the recognition of several agricultural crops from false color composite imagery. Translated by Edward Torrey, Alexandria, VA 22308 from: G. V. Dobrovol'skiy and V. L. Andronikov, eds., Aerokosmicheskiye metody v pochvo-vedenii i ikh ispol'zovaniye v sel'skom khozyaystve: sbornik nauchnykh trudov [Remote Sensing Methods in Soil Science and Their Utilization in Agriculture: A Collection of Scientific Works]. Moscow: Nauka, 1990, pp. 47-55.     

Pan Sharpening for Hyper Spectral Imagery Using Spectral Mixing-Based Color Preservation Model

With nanometric spectral resolution and number of bands as high as 220, Hyper spectral sensors like Hyperion and AVIRIS are gaining wide appreciation. Narrow, continuous wavelength of bands upon a vast spectrum of electromagnetic wavelength enables better precision in identification of materials by distinguishing between their unique spectral signatures. However, their poor spatial resolution is a major impediment in realising the full potential of hyperspectral imaging. Efforts are being made worldwide to improve the spatial resolution of hyperspectral imagery by fusing them with registered panchromatic imagery of higher resolution. However, most of the conventional methods fail to preserve the spectral fidelity of the fused images due to severe color distortion. Here, we propose an efficient paradigm to sharpen hyperspectral imagery with high spatial information content and minimal color distortion using color normalization by Lαβ and intensity image generation using Spectral Mixture Analysis. Quantitative assessment indices have been calculated to prove that our method is superior in terms of minimization of color distortion and maximization of spatial details as compared to other methods.     

Discrimination of low-grade magnetite ores using remote sensing techniques

The remote sensing community in geology is widely using the Multispectral Landsat Thematic Mapper (TM) data which has a wider choice of spectral bands (six between 0.45 and 2.35 μm, plus a thermal infrared channel 10.4-12.5 urn). These were evaluated for low-grade magnetite ores mapping over the high-grade granulite region of Kanjamalai area of Tamil Nadu state, India. The Fourier Transform Infrared (FTIR) spectroscopy data (0.4-4.0 μm) for powders of the magnetite ores exposed with granulite rock and published spectral reflectance data were used as guides in selecting TM band reflectance ratios, which maximize discrimination of magnetite ores on the basis of their respective mineralogies. The study shows that the weathering mineralogy of magnetite ores causes absorption features in their reflectance spectra which are particularly characteristic of the near infrared. Comparison of TM data with field and petrographic observations shows the presence of magnetite and aluminosilicate minerals & show strong absorption at 0.7-1 μ.m wavelength spectral region & increase in the product of two TM band ratios: band 5 (1.55-1.75 μm) to band 4 (0.76-0.9 μm) and band 3 (0.63-0.69 μm) to band 4 (0.76-0.9 μm). Various computer image enhancement and data extraction techniques such as interactive digital image classification techniques using color compositing stretched ratio, maximum likelihood and thresholding statistical approaches using Landsat TM data are used to map the low-grade magnetite ores of the granulite region. The field traverses and local verification enhanced to map the other rock types namely granulites and gneisses of the study area.     

Contrast enhancement of landsat data on multispectral data analysis system

Contrast enhancement, one of the image processing techiques, is developed on the Multispectral Data Analysis System (MDAS) for enhancing the LANDSAT data. The purpose of image processing for enhancement is to improve the obscure objects data in the image to stand out more readily for good sensing to the human eye. It is observed on MDAS that some of the LANDSAT scenes when examined on the color display, give inadequate information for the required objective of interpretation. This is due to poor tonal contrast in the scene because of prevailing climatological conditions at the time of satellite pass over that area. Also, the LANDSAT data usually occupy a small subset of the total brightness range 0–127. To provide optimal contrast and variation for color compositing, contrast enhancement may by performed on the data before going to trie information processing (categolization) on the landsat scene. This paper describes the algorithms of parametric linear and non linear contrast enhancement techniques. A typical example to differentiate the degree of salinity in the soils was tested with the suggested algorithms and the results are tabulated in the form of photographs. The test area is selected from Haryana (frame no. 158-040 dated 2nd May, 1977) for testing the algorithms. The enhancement software developed on the MDAS stretches all the four Landsat bands and generates an output tape with the format similar to LANDSAT computer compatible tape (CCT). The stretched results of 5 and 7 bands are displayed in this paper. A false color composite which appears as on the color displya could also be generated from 4, 5 and 7 bands. The enhanced output was found to be useful for easily categorizing the data into various categories on MDAS.     

Using in situ hyperspectral reflectance data to distinguish nine aquatic plant species

In situ hyperspectral reflectance data were studied at 50 bands (10 nm bandwidth) over the 400–900 nm spectral range to determine their potential for distinguishing among nine aquatic plant species: American lotus [Nelumbo lutea (Willd.) Pers.], American pondweed (Potamogeton nodusus Poir.), giant duckweed [Spirodela polyrrhiza (L.) Schleid.], Mexican waterlily (Nymphaea mexicana Zucc.), white waterlily (Nymphaea odorata Aiton), spatterdock [Nuphar lutea (L.) Sm.], giant salvinia (Salvinia molesta Mitchell), waterhyacinth [Eichhornia crassipes (Mart.) Solms] and waterlettuce (Pistia stratiotes L.). The species were studied on three dates: 30 May, 1 July and 3 August 2009. All nine species were studied in July and August, while only eight species were studied in May; giant duckweed was not studied in May due to insufficient availability. Two procedures were used to determine the optimum bands for discriminating among species: multiple comparison range tests and stepwise discriminant analysis. Multiple comparison range tests results for May showed that most separations among species occurred at bands 795–865 nm in the near-infrared (NIR) spectral region where up to six species could be distinguished. For July, few species could be distinguished amongthe 50 bands; most separations occurred at the 715 nm red-NIR edge band where four species could be differentiated. The optimum bands in August occurred in the green (525–595 nm), red (605–635 nm) and red-NIR edge (695–705 nm) spectral regions where up to six species could be distinguished. Stepwise discriminant analysis identified 11 bands in the blue, green, red-NIR edge and NIR spectral regions to be significant to discriminate among the eight species in May. For July and August, stepwise discriminant analysis identified 15bands and 13 bands, respectively, from the blue to NIR regions to be significant for discriminating among the nine species.     

NARROW-BAND REMOTE SENSING FROM THE “SALYUT-7” SPACE STATION

An experiment in the use of narrow-band space photography in the study of earth resources carried out in July-August 1985 on board the Salyut-7 space station is described. Exposure of photographic film highly sensitive to reflected and emitted radiation for 10-nm-wide spectral bands that maximize contrast between particular natural features and their surroundings provides an effective means for increasing the accuracy of recognition of such features as ring structures, condition and species composition of plant communities, agricultural crops, and turbid and polluted waters in seas and lakes. Translated from: Geodezlya i kartografiya, 1986, No. 4, pp. 19-27.     

高光谱数据处理及在铀资源勘查中的应用——以广西苗儿山地区为例

以广西苗儿山地区机载成像细分红外高光谱数据为例,探讨了高光谱数据的特点及其处理方法。编制了一系列数据预处理软件,对图像进行了条带去除、波段间配准、航向压缩、镜向变换、辐射校正、正切校正、相对反射率转换等预处理,获得了高质量图像; 在 ENVI软件基础上建立了研究区野外实测光谱曲线库及分类子库,并对各子库进行了系统研究。同时,提出了正确的图像配准方案,对图像进行了精校正和镶嵌,取得了满意的效果; 在纯净像元提取、混合像元分解、匹配滤波、光谱特征拟合等方面进行了探索,提取了该区铀矿化的特征光谱信息; 最后,探讨了高光谱数据的地质应用潜力,尤其在硅化带识别方面的应用取得了成功,发现了一系列的新的含铀硅化断裂带,预测了三个成矿有利地段。经验证,其中两片地区的铀明显偏高并有矿化显示。     

一种利用TM图像自动提取城镇用地信息的有效方法

如何快速、准确与客观地提取城镇用地信息,以获得城镇用地的分布范围和面积资料,是有关城镇问题研究中经常涉及到的一个基本问题,现代遥感技术为这个问题的有效解决提供了强有力的保证,运用提出的归一化建筑指数,从TM图像进行了无锡市城镇用地信息的自动提取,研究结果表明,与传统的计算机分类和手工屏幕数字化方法相比,归一化建筑指数法是一种非常行之有效的方法。     

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.     

Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery

Vegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.     

Visual versus digital analysis for vegetation mapping: Some examples on central Spain

Digital processing of Landsat images has been considered the most appropriate interpretation method for vegetation mapping. However, digital processing presents several difficulties: (i) it demands significant inversions, with respect both the images and the equipment; (ii) it presents problems to discriminate heterogeneous categories, and (iii) it requires much more training effort.

Visual analysis, on the other hand, is less demanding both in economic investments and training. Therefore, it is a fruitful alternative to digital mapping, especially when it is applied to small and medium scale inventories. A consistent methodology for visual interpretation of vegetation categories is presented in this paper. Benefits and disadvantages of this procedure are analyzed, as well as keys‐for visual identification of land cover categories. A TM Quarter of scene on Central Spain is presented as an example of this method. Two false‐color images from different seasons were interpreted at 1: 250,000 scale. Fourteen land cover categories were identified, yielding 83.03% of final accuracy.     

Urban land use mapping: A case study of ahmedabad city and its environs

Proper urban planning and effective implementation requires reliable urban land use statistics. In this context, satellite remote sensing data has been studied using both visual and digital techniques. A portable eight-band radiometer has been used to collect spectral signatures of surface features present in Ahmedabad city and its environs. Using these signatures a suitable approach employing visual and digital techniques has been developed for urban land use/sprawl mapping. Urban land-use maps of Ahmedabad city and its environs were prepared on 1:25,000 scale and for Ahmedabad Urban Development Authority Area on 1:50,000 scale using this methodology. It has been found that edge-enhancement techniques are useful to enhance the contrast among different urban land uses. Classification techniques such as MXL and Bayes classifiers are not successful in discriminating urban land uses. Tonal characteristics alongwith other elements of interpretation are required to classify urban land uses such as residential, industrial etc. Spatial distribution of various urban and uses and the space devoted to each urban land use has been brought out.     

Age discrimination among eruptives of Menengai Caldera,Kenya, using vegetation parameters from satellite imagery

This paper reports an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. Since Landsat data display vegetation parameters well, and plant communities vary with type and depth of soil development, selective digital processing techniques were applied to take advantage of these characteristics for discriminating relative age differences of the underlying volcanics. A selective series of five images, consisting of a color‐coded Landsat 5 classification and four color composites, were compared with geologic maps. These included a color coded, modified, unsupervised classification and contrast enhanced, color composite images using TM bands 3–2–1, 4–3–2 and 7–5–3, and the first 3 Karhunen‐Loeve transformation axes that had been generated using 7 Landsat TM bands.

The most recent of more than 70 post‐caldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such, supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. It was found that the Landsat images correlated well with geologic maps, but that the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.     

Comparison of Remote Sensing Image Fusion Strategies Adopted in HSV and IHS

It has been always a challenging task to keep an ideal balance of spectral and spatial resolution for merging panchromatic image and multispectral image. The mathematical theories such as color space transformation and Wavelet Packet Analysis are usually employed in information fusion area. Combining color space conversion with wavelet packet theory is a way of researching remote sensing image fusion algorithms further. In the paper, there are three existing image fusion strategies applied to the second layer of frequency bands decomposed by wavelet packet analysis in the HSV and the IHS (triangular coordinate) color space, respectively. Serial experiments demonstrate two core concepts. One is the effects of image fusion strategies based on region is super to those of fusion strategy based on pixel for the same color space; the other is the different performances are measured in the two color spaces. Specially, the space definition for image fused in the former color space is inferior to that in the latter color space; while the spectrum content for image fused in the former color space retains better than in the latter color space, when using the same fusion strategy in the two color space. As a result, application containing HSV space conversion can alleviate spectral deterioration, whereas fusion operation of IHS transformation can lift spatial definition.     

天宫一号高光谱遥感场景分类数据集及应用

天宫一号高光谱成像仪具有空间分辨率高、光谱分辨率高、图谱合一等特性，在中国航天高光谱领域具有里程碑的意义。针对一般遥感场景分类数据集尺度单一、光谱分辨率较低等问题，本文提出基于天宫一号的多谱段、高空间分辨率、多时相高光谱遥感场景分类数据集(TG1HRSSC)。利用天宫一号高光谱成像仪获取的高质量数据，经过辐射校正、几何校正、空间裁剪、波段筛选、数据质量分析与控制等，制作了一批通用的航天高光谱遥感场景分类数据集，通过载人航天空间应用数据推广服务平台（http://www.msadc.cn[2019-09-10]）进行分发和共享。该数据集包括天宫一号高光谱成像仪获取的城镇、农田、林地、养殖塘、荒漠、湖泊、河流、港口、机场等9个典型地物场景的204个高光谱影像数据，其中5 m分辨率全色谱段1个波段、10 m分辨率可见近红外谱段54个有效波段以及20 m分辨率短波红外谱段52个有效波段。研究利用AlexNet、VGG-VD-16、GoogLeNet等深度学习算法网络对构建的数据集进行场景分类的试验，结果表明该数据集的场景分类应用实现较好效果。由于该数据集具备高分辨、高光谱等特征优势，未来在语义理解、多目标检测等方面有着广泛的应用价值。     

Sentinel-2 time series based optimal features and time window for mapping invasive Australian native Acacia species in KwaZulu Natal,South Africa

The spread of invasive Australia native Acacia tree species threatens biodiversity and adversely affecting on vegetative structure and function, including plant community composition, quantity and quality worldwide. It is essential to provide researchers and land managers for biological invasion science and management with accurate information of the distribution of invasive alien species and their dynamics. Remotely sensed data that reveal spatial distribution of the earth’s surface features/objects provide great potential for this purpose. Consistent satellite monitoring of alien invasive plants is often difficult because of lack of sufficient spectral contrast between them and co-occurring plants species. Time series analysis of spectral properties of the species can reveal timing of their variations among adjacent species. This information can improve accuracy of invasive species discrimination and mapping using remote sensing data at large scale. We sought to identify and better understand the optimal time window and key spectral features sufficient to detect invasive Acacia trees in heterogeneous forested landscape in South Africa. We explored one-year (January to December 2018) time series spectral bands and vegetation indices derived from optical Copernicus Sentinel-2 data. The attributes correspond to geographical information of invasive Acacia and native species recorded during a field survey undertaken from 21 February to 25 February 2018 over Kwa-Zulu Natal grasslands landscape, in South Africa. The results showed comparable separability prospects between times series of spectral bands and that of vegetation indices.Substantial differences between Acacia species and native species were observed from spectral indices and spectral bands which are sensitive to Leaf Area Index, canopy chlorophyll and nitrogen concentrations. The results further revealed spectral differences between Acacia species and co-occurring native vegetation in April (senescence for deciduous plants), June-July (dry season), September (peak flowering period of Acacia spp) and December (leaf green-up) with vegetation indices (overall accuracy > 80 %). While spectral bands showed the beginning of the growing season (November–January) and peak vegetation productivity (February-March) as the optimal seasons or dates for image acquisition for discriminating Acacias from its co-occurring native species (overall accuracy > 80 %). In general, the use of Sentinel-2 time series spectral bands and vegetation indices has increased our understanding of Australian Acacias spectral dynamics, and proved that the sentinel-2 data is useful for characterization and monitoring Acacias over a large scale. Our results and approach could assist in deriving detailed geographic information of the species and assessment of a spread invasive plant species and severity of invasion.     

A band selection technique for spectral classification

In hyperspectral remote sensing, sensors acquire reflectance values at many different wavelength bands, to cover a complete spectral interval. These measurements are strongly correlated, and no new information might be added when increasing the spectral resolution. Moreover, the higher number of spectral bands increases the complexity of a classification task. Therefore, feature reduction is a crucial step. An alternative would be to choose the required sensor bands settings a priori. In this letter, we introduce a statistical procedure to provide band settings for a specific classification task. The proposed procedure selects wavelength band settings which optimize the separation between the different spectral classes. The method is applicable as a band reduction technique, but it can as well serve the purpose of data interpretation or be an aid in sensor design. Results on a vegetation classification task show an improvement in classification performance over feature selection and other band selection techniques.     

Relating forestry interpreter preference to sensitometric parameters of black and white and normal color aerial films

The photointerpretation accuracy of forest inventory surveys are highly contingent on the sensitometric attributes of aerial films. The sensitometric characteristics of the products that interpreters prefer to work with was determined as a basis for future specifications of 70 mm large-scale photography. Seven black and white and two color films were compared, of which color-positive film was clearly preferred. Of greater significance were the comparison results between the black and white films. Panchromatic films with an average gradient of 0.9 to 1.2 and a density range of 1.0–1.2 were most preferred for forest photo interpretation. Within each black and white film, interpreters preferred the higher contrast images characterized by the higher average gradients. In general, interpreter preference decreased as the spectral sensitivity of the black and white films increased     

AN INSTRUMENT-AIDED METHOD OF INTERPRETING SCANNER IMAGERY OF THE EARTH'S CLOUD COVER

The author, noting the high cost and complexity of automated methods of processing imagery of the earth's cloud cover, presents a less sophisticated, instrument-aided method yielding similar results. A procedure for analyzing cloud cover patterns based on color synthesis of two-band scanner imagery from Soviet “Meteor” and American NOAA-series weather satellites using a synthesizing projector is outlined. This method adds the interpretation key of color to those of cloud structure and form in the analysis of cloud patterns, enhances cloud-underlying surface contrasts, and compares favorably with more highly automated methods in image preparation and interpretation time. Translated by Jay Mitchell; PlanEcon, Inc.; Washington, DC 20005 from: Izvestiya vysshykh uchebnykh zavedeniy, Geodeziya i aerofotos'yemka, 1987, No. 5, pp. 95-98.