Crop classification with LANDSAT multispectral scanner data

The LANDSAT multispectral scanner (MSS) data have been analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, has been found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites are essentially two-dimensional, and that the data from different sites and different acquisitions lie on parallel planes in the four-dimensional feature space. These results have been exploited to gain new insight into the data and to develop alternate models for classification. In particular, it has been found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.     

Remote sensing characterization of benthic habitats and submerged vegetation biomass in Los Roques Archipelago National Park,Venezuela

Remote sensing is a useful tool for characterizing submerged aquatic vegetation and other benthic habitats in shallow water areas with clear water transparency. In the present study, the visible bands of the Thematic Mapper (TM) sensor aboard Landsat 7 satellite were used in a supervised classification of benthic habitats and for the assessment of submerged vegetation biomass in Los Roques Archipelago National Park, Venezuela. Initially, the TM visible bands were log‐transformed and linearly combined to reduce the depth‐dependent variance in the bottom reflectance signal. The supervised classification had an overall accuracy of 74%. Eight bottom types could be spectrally separated: sand, dispersed communities over sand (shallow and deep), dense seagrass, dispersed seagrass meadows over sand, reef communities, mixed vegetation over muddy bottom, and lagoons. Regression analyses were performed between the depth‐invariant band combinations and field samples of vegetation biomass. The regression using the TM band 2 and 3 combination accounted for 64% of the variability of submerged vegetation biomass. According to these estimates, seagrass meadows with biomass between 64–96?g?m^?2 and 96–128?g?m^?2 predominate in the Los Roques Archipelago National Park.     

Crop classification with landsat multispectral scanner data II

The Landsat multispectral scanner data have been analyzed with a view toward crop identification and inventory. The notion of spectral-temporal trajectory as crop signature, introduced in an earlier paper, has been further developed leading to a formulation of the problem similar to one of recognition of hand-written characters. A simple classification rule based on angular features of the trajectory is discussed and classification results are given for Landsat images from several sites.     

Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing

Among vegetated coastal habitats, mangrove forests are among the densest carbon pools. They store their organic carbon in the surrounding soil and thus the sequestered carbon stays in the sediment for a long time and cannot be easily returned to the atmosphere. Additionally, mangroves also provide various important ecosystem services in coastal areas and surroundings. Accordingly, it is important to understand the distribution of biomass carbon stock in mangrove habitats in a spatial and temporal context, not only to reduce CO₂ concentrations in the atmosphere, but also for their sustainability. The objectives of this research are to map the mangrove carbon stock and estimate the total biomass carbon stock sheltered by mangrove forests, with the Karimunjawa Islands as a study site, using the widely available passive remote sensing system ALOS AVNIR-2. The modelling and mapping of mangrove carbon stock incorporates the integration of image pixel values and mangroves field data via empirical modelling. Vegetation indices and PC bands at different levels of radiometric corrections were all used as the input in the mangrove carbon stock modelling so that the effectiveness and sensitivity of different image transformations to particular radiometric correction levels could be analysed and understood. Afterward, the accuracy and effectiveness of each mangrove carbon stock-mapping routine was compared and evaluated. The accuracy of the best mangrove above-ground carbon stock (AGC) map modelled from vegetation index is 77.1% (EVI1, SE 5.89 kg C m^?2), and for mangrove below-ground carbon stock (BGC) it is 60.0% (GEMI, SE 2.54 kg C m^?2). The mangrove carbon stock map from ALOS AVNIR-2 PC bands showed a maximum accuracy of 77.8% (PC2, SE 5.71 kg C m^?2) and 60.8% (PC2, SE 2.48 kg C m^?2) for AGC and BGC respectively. From the resulting maps, the Karimunjawa Islands are estimated to shelter 96,482 tonnes C of mangroves AGC with a mean value of 21.64 kg C m^?2 and 24,064 tonnes C of mangroves BGC with a mean value of 5.39 kg C m^?2. Potentially, there are approximately 120,546 tonnes C of mangrove biomass carbon stock in the Karimunjawa Islands. Remote-sensing reflectance can successfully model mangrove carbon stock based on the relationship between mangrove canopy properties, represented by leaf area index (LAI) and the tree or root biomass carbon stock. The accuracy of the mangrove carbon stock map is subject to errors, which are sourced mainly from: (1) the absence of a species-specific biomass allometric equation for several species present in the study area; (2) the generalized standard conversion value of mangrove biomass to mangrove carbon stock; (3) the relationship between mangrove reflectance and mangrove LAI; (4) the relationship between mangrove reflectance and above-ground mangrove biomass and carbon stock due to its relationship with LAI; (5) the relationship between mangrove LAI and mangrove below-ground parts; (6) the inability to perform mangrove carbon stock modelling at the species level due to the complexities of the mangrove forest in the study area; (7) background reflectance and atmospheric path radiance that could not be completely minimized using image radiometric corrections and transformations; and (8) spatial displacement between the actual location of the mangrove forest in the field and the corresponding pixel in the image. The availability of mangrove biomass carbon stock maps is beneficial for carrying out various management activities, and is also very important for the resilience of mangroves to changing environments.     

Remote sensing of biomass of salt marsh vegetation in France

Abstract

Spectral data (gathered using a hand-held radiometer) and harvest data were collected from four salt marsh vegetation types in Brittany, France, to develop equations predicting live aerial biomass from spectral measurements. Remote sensing estimates of biomass of the general salt marsh community (GSM) and of Spartina alternifiora can be obtained throughout the growing season if separate biomass prediction equations are formulated for different species mixtures (for the GSM) and for different canopy types (for S. alternifiora). Results suggest that remote sensing will not be useful for predicting Halimione portulacoides biomass, but can be used to estimate Puccinellia maritima biomass early in the growing season.     

Remote sensing with small satellites

Abstract

Some design issues relevant to the adaptation of small satellites for remote sensing applications are presented. Particular emphasis is placed on those areas which will necessitate the advancement of current small satellite technology. The aim of such a programme would be to provide a cost-effective but reliable way of obtaining remotely sensed data from a range of novel sensors, thus complementing the efforts of major missions towards the 21st century.     

Suppression of vegetation in multispectral remote sensing images

Vegetation cover is an impediment to the interpretation of multispectral remote sensing images for geological applications, especially in densely vegetated terrains. In order to enhance the underlying geological information in such terrains, it is desirable to suppress the reflectance component of vegetation. This article adapts the forced invariance technique proposed by Crippen and Blom (2001 Crippen, R.E. and Blom, R.G. 2001. Unveiling the lithology of vegetated terrains in remotely sensed imagery. Photogrammetric Engineering and Remote Sensing, 67: 935–943. [Web of Science ®] , [Google Scholar]) for the suppression of the vegetation reflectance component in a densely vegetated study area in northern Zhejiang province, eastern China. The approach uses a three-step process that comprises: (i) masking of barren or sparsely vegetated areas using a normalized difference vegetation index (NDVI) mask in order to retain their original spectral information through the subsequent processing; (ii) applying a forced invariance technique to subtract the spectral response of vegetation only in vegetated areas; and (iii) combining the processed vegetated areas with the masked barren or sparsely vegetated areas followed by a histogram equalization to eliminate the differences in colour scales between these two types of area. An evaluation based on comparison with the geological map shows that the forced invariance technique results in significant enhancement of the geological information in the processed image.     

Remote sensing terminology: a reply

A point in the Letter by Curran (1983) on remote sensing terminology is expanded and a further point made.     

Remote sensing terminology: a reply

Abstract

The widespread misuse of the word ‘acronym’ is pointed out.     

The estimation of green-leaf-area index from remotely sensed airborne multispectral scanner data

Airborne multispectral scanning system (MSS) data from the Natural Environment Research Council's MSS-82 project were used to estimate the green-leaf-area index (GLAI) of an area of semi-natural and agricultural limestone grassland. This research is the pilot study of a five phase investigation which will ultimately test the feasibility of using airborne and spaceborne MSS data to estimate GLAI over large areas. The pilot study was designed around four stages: (i) derivation of the relationship between multispectral reflectance and vegetation amount using ground radiometric data collected in the field, (ii) production of a perpendicular-vegetation index (PVI) image, (iii) production of a GLAI image using the relationship between PVI and GLAI and (iv) accuracy assessment of the resultant GLAI image. Due to a number of project- and environment-specific problems the proposed methodology was modified and GLAI was predicted to an accuracy of 50-86 per cent at the 95 per cent confidence level. For the future, a change of project design and the use of a vegetation model to correct for environmental anomalies will enable the technique to be used to greater effect.     

结合纹理去除的遥感图像分割

针对包含复杂纹理信息的遥感图像难以进行精准图像分割的问题,提出了一种结合纹理去除的遥感图像分割方法。首先,改进了相对全变差纹理去除方法,通过引入新的范数约束使相对全变差纹理去除方法可以在去除纹理信息的同时凸显图像中的主要结构,达到辅助分割的效果;然后,使用均值漂移算法对经过纹理去除的遥感图像进行无监督聚类,达到分割的目的;最后,提出的遥感图像分割算法在不同遥感图像上进行了测试。实验结果表明,在高分辨遥感图像的分割上,所提算法可以分割出遥感图像中的主要目标,和直接分割或者结合其他纹理去除方法相比取得了更好的分割结果。所提出的分割算法可以降低纹理信息对图像分割的影响,提高遥感图像分割的精度。     

Remote sensing of wave patterns with oceanographic implications

Abstract

First, a general review is presented of wave-current interaction processes ( for horizontal shears) and their effect on radar backscatter and radar imagery ( SAR/ RAR ) Then numerical results on the refraction of wave energy trajectories by complex bottom topography (finite depth) and a linear shear current are presented. For deep water, the wave-energy trajectories are given for mesoscale currents ( e.g. eddies and double-vortex configurations). The focusing of wave energy by variable currents found here should have important influence on the spatial scale of wind stress over the ocean, and on optical and acoustic properties of the upper layer of the ocean.     

Remote sensing of fires with the TRMM VIRS

Although the Visible and Infrared Scanner (VIRS) instrument on board the Tropical Rainfall Measuring Mission (TRMM) satellite is designed primarily to study precipitation, it offers a new opportunity for the remote sensing of fire activity in regions within 40 of the equator. VIRS is a five-channel imaging spectroradiometer with bands ranging from 0.6 to 12 mu m. Its similarity to the Advanced Very High Resolution Radiometer (AVHRR), which has operated since 1978 on board the National Oceanic and Atmospheric Administration (NOAA) satellite series, is no coincidence, as a primary objective of VIRS is to provide an important link between TRMM precipitation measurements and those derived from other current and historical satellite sensors. The similarity of the VIRS infrared bands, in particular, to those of AVHRR provides a foundation for fire detection, which has been clearly demonstrated for AVHRR. However, VIRS offers some additional capabilities which should enable it to make an important contribution to the remote sensing of fire.     

Directional reflectance properties determined by analysis of airborne multispectral scanner data and atmospheric correction

Directional reflectance properties of natural surfaces are very important in the interpretation of remotely sensed data. The analysis of multispectral scanner data shows a distinct dependence on scan angle, wavelength (0.4–1.1 μm), and classes (e.g., bare soil, vegetation). This relationship can be described by polynomials determined by regression methods. Atmospheric effects are computed with a simple model by parametrization of the multiple scattered skylight. The model permits a quick and sufficient estimation of the airlight, depending on the data collection conditions. Comparisons of the scanner data with the corresponding model yield the following results: The differences between airborne and ground measurements are due to atmospheric effects. The directional variation in brightness is mainly caused by the object itself with the exception of short wavelengths and/or a very low albedo. The hue shift of vegetation is essentially produced by the object and modified by the atmosphere. Taking into account the directional reflectance properties in a direction-dependent classification procedure results in an improvement of up to 20% in comparison with algorithms used so far. Quantitative relationships between ground measurements and radiation measurements by airborne sensors including atmospheric effects can be determined with the proposed methods.     

Remote sensing images super-resolution with deep convolution networks

Multimedia Tools and Applications - Remote sensing image data have been widely applied in many applications, such as agriculture, military, and land use. It is difficult to obtain remote sensing...     

Remote sensing of foliar chemistry

Remotely sensed data are being used to estimate foliar chemical content as a result of our need for the information and our increasing ability to understand and measure foliar spectra. This paper reviews how stepwise multiple regression and deconvolution have been used to extract chemical information from foliar spectra, and concludes that both methods are useful, but neither is ideal. It is recommended that the focus of research be modeling in the long term and experimentation in the short term. Long-term research should increase our understanding of the interaction between radiation and foliar chemistry so that the focus of research can move from leaf model to canopy model to field experiment. Short-term research should aim to design experiments in which remotely sensed data are used to generate unambiguous and accurate estimates of foliar chemical content.     

Remote sensing of soils in a eucalypt forest environment

The concentration of soil phosphorus and pH correlated significantly with Landsat TM and Compact Airborne Spectrographic Imager (CASI) data, terrain position and aspect. Band ratios of the infrared and visible wavelengths are particularly significant for phosphorus. Exchangeable soil cations (that is calcium and sodium), as well as electrical conductivity, correlated less strongly with CASI and TM, but the correlations were statistically significant. Magnesium, potassium, and pH were significantly correlated with TM data, but not with the CASI data. pH and phosphorus were significantly correlated with terrain position. Three factors may individually, or in combination, be causing these results: illumination differences caused by terrain, direct reflectance of soil, or differences in vegetation (due to the influence of the soil on vegetation). A review of the literature appears to support the latter as the most likely causal factor. The results are important for establishing whether forest soils may be mapped using only remotely-sensed data, digital terrain data, or a combination.     

Effects of the atmosphere on the detection of surface changes from Landsat multispectral scanner data

The atmospheric effects on radiometric data recorded in the Landsat multispectral scanner system (MSS) bands are compiled for cases of representative and ideal atmospheric conditions. The effects are expressed as a difference between the Earth's surface spectral reflectivity, a₀, and the surface-atmosphere system spectral reflectivity, a_s, derived from the satellite data,

a_s?a₀ = ?a₀[l+(l/μ₀)](B+W) + 2a² ₀B + g(μ₀)B/2μ₀

where μ₀ is the cosine of the solar zenith angle, B and W are the backscattering and absorption optical thickness respectively, and the function g( μ₀) is the anisotropy of backscattering to the zenith from the direct beam. This formula is accurate only for an atmosphere of low optical thickness. Also, the equation applies only to large areas having a uniform reflectivity, because adjacency effects due to reflection from the terrain surrounding the object pixel and subsequent scattering by the atmosphere are not considered.

It is concluded that in the quantitative monitoring of surface changes from satellites, scattering effects predominate in some applications (for example, bathy-metric mapping of coastal waters), whereas absorption effects predominate in other applications (for example, monitoring desert fringe areas). Different measurements are more appropriate for assessing the scattering effects than for assessing the absorption effects.

These effects on the monitoring of surface changes by the use of Landsat MSS data are discussed in terms of departures of the actual atmosphere at the time of a satellite passage from a ‘minima’ atmosphere having no aerosols and characterized by gaseous absorption corresponding to minimal water vapour amounts.