ISSN:0143-1161    

DOI:10.1080/01431169408954047

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

A study was conducted to evaluate the sensitivity of the microwave return recorded by the ERS-1 C-band synthetic aperture radar ( SAR) to changes in above-ground woody biomass in young loblolly pine forests ( dry weight biomass ⩾ 6kgm− 1Radar backscattering coefficients were derived from ERS-1 SAR imagery collected over 15 test stands near Durham, North Carolina, United States. Ground reference data were collected in order to characterize the aboveground biomass of the pine trees in the test stands. Significant linear correlations atp= 000l or better) were found between the radar backscattering and the various components of biomass ( both wet and dry weight) of the pine canopy ( e.g., bole biomass, stem biomass, needle biomass, canopy biomass and total biomass). The linear correlation coefficients ranged between 087 and 0-93. These results demonstrate the potential for using the C-band ERS-1 SAR to monitor biomass changes during the early successional stages in temperate coniferous forests, as well as throughout later successional stages in coniferous forests with lower overall growth rates, such as those found in boreal regions.

ISSN:0143-1161    

DOI:10.1080/01431169408954048

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

The green vegetation cover gradient in three regions referred to as subtropical, semi-arid and arid in Northern Territory was assessed in terms of spectral reflectance characteristics. Incremental vegetation cover over different background components (combined spectra) resulted in three vegetation types being defined, Type 1, darkening Type 2 partial darkening and Type 3, highly near-infrared (NIR) reflective. Spectral plots on field sites showed the subtropical region as being typified by high NIR reflectance, the semi-arid curves showed extensive darkening, while the arid region curves show less darkening due to a lower vegetation cover and concomitant high soil reflectance. Results of analysis of variance of regression using different combinations of single bands, band transforms and principal component data resulted in the single band, MSS-5 providing the most useful predictive data in the subtropical and semi-arid areas. Normalized albedo on MSS-5 proved most useful in arid areas. Transforms involving relationships between near-infrared and visible wavebands were not ranked as suitable predictive tools for the semi-arid region. Prior to classification, single bands and band transforms were ranked. Only those showing high significance levels in relation to green vegetation cover were used as input data. Results showed that classification maps using maximum likelihood classifiers can be uniformly applied throughout all three regions resulting in high levels of accuracy in terms of map areas and community types. The classification technique separates out darkening (Type 1) and NIR reflective (Type 3) classes with the threshold of separation for the two types standing at 45-50 per cent green vegetation cover.

ISSN:0143-1161    

DOI:10.1080/01431169408954049

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

This study used Landsat-5 Thematic Mapper (TM) data to develop empirical models for determining soybean and corn crop yield, leaf area index, wet biomass, dry biomass and plant height. Ground-truth data was obtained from more than 50 commercial farms in Ohio, USA, during 1988 and 1989. Several significant linear, non-linear, logarithmic, exponential, and power models were developed. The best soybean models generally comprised of information from the commonly-used bands 3 and 4. TM data for the most significant soybean models explained 69 to 76 per cent of the between field variability in wet biomass, dry biomass, and plant height, 63 per cent of the variability in leaf area index, and 35 per cent of the variability in yield. The best corn models incorporated band 5 and/or band 7 along with band 4. The most significant corn models explained 80 per cent of the variability in wet biomass, 66 to 67 per cent of the variability in dry biomass, plant height, and leaf area index, and 52 per cent of the variability in yield. A new cubed ratio vegetation index, (TM4/TM5)3, was found to be particularly useful for modelling corn characteristics.

ISSN:0143-1161    

DOI:10.1080/01431169408954050

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

Remote sensing applied to tasks of mapping soil and rock surfaces must address the problem of vegetation cover in all but the most arid terrain. Masking out pixels with a high proportion of vegetation using a threshold on the near-infrared/red ratio is a popular strategy for live vegetation. The important effects of dead vegetation on the SWIR reflectance is usually ignored. Data gathered by the GER-II imaging spectrometer over a semi-arid area near Almaden, south central Spain were used to test the sensitivity of thematic soil mapping to variable cover of live and dead vegetation. After calibration to reflectance a least-squares unmixing analysis was performed using image end-members and proportions maps of vegetation and soil/rock components generated. Despite a low signal-to-noise ratio, three soil/rock and four vegetation endmembers were successfully mapped and validated from field estimates. A quantitative assessment was made of the effects of live and dead vegetation on the ability of the unmixing analysis to distinguish between granite and shale soils using synthetically mixed spectra gathered using field spectroradiometry and statistical analysis of the imaging spectrometer data. Dead vegetation was shown to have a greater impact on soil spectra than live vegetation. The ability to distinguish between the soils was lost at 50-60 per cent vegetation cover.

ISSN:0143-1161    

DOI:10.1080/01431169408954051

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

The Aravalli environment in the vicinity of the Thar Desert in Rajasthan, India presents a clear picture of environmental degradation. This paper presents the study of the phenomenon occurring between the years 1975-1988. The approach adopted here uses remote sensing as a tool for the study of desertification using data from Landsat and IRS -1A (Indian Remote Sensing Satellite). First the satellite imagery of the area is interpreted using visual interpretation techniques. Next, some distinct features from this analysis are identified and are verified with the ground-truth collected by visiting the study area site. Finally, digitial image processing has been carried out using an Earth Resources Data Analysis System (ERDAS) to quantify the temporal changes. A rigorous data analysis yields the extent of the environmental degradation which appears to be quite alarming. Severe depletion in the area under the water and vegetation covers and accordingly an increase in the saline and aeolinous areas have been observed.

ISSN:0143-1161    

DOI:10.1080/01431169408954052

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

The surface temperature of the Earth is a very important climate parameter. With remote sensing techniques its global distribution can be derived with good temporal and spatial resolution. The 10·7 μm and ll·9 μm channels of the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA satellites provide information for monitoring the surface temperature. In principle, the sea surface temperature (SST) can be obtained twice a day with the split window algorithm. An algorithm has been developed to determine the brightness temperature of arbitrary surfaces from the data of one AVHRR channel. It takes into account the dominant effects as scan angle dependency, surface heating and different atmospheric conditions. Neglecting one of these effects causes errors that are bigger than those due to unknown emissivity of natural surfaces of the Earth, Atmospheric parameters are taken from actual radiosonde data. Land surface temperatures can be determined at least four times a day, in high latitude regions even more often, with an accuracy of about l·6 deg K.

ISSN:0143-1161    

DOI:10.1080/01431169408954053

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

A split-window equation is derived for land surface temperature, yielding T = T4+ A(T4− T5) + B(ε), whereTis the true surface temperature,T4andT5are the brightness temperatures measured in AVHRR channels 4 and 5,Ais a coefficient related to the atmospheric transmittances in AVHRR channels 4 and 5, being dependent on the atmosphere type and independent on surface emissivity, andB(ε)takes into account the emissivity effect, which depends on both the channel surface emissivities (ε4and ε5) and the atmosphere type. The atmospheric dependence of split-window coefficients,Aand B(ε), is discussed by means of satellite measurements simulations andin situdata. It is shown that linear, global-scale algorithms can produce inaccurate estimates of surface temperature when they are applied to areas of reduced atmospheric variability. A simple, non-linear split-window algorithm has been proposed, in which A = a0+ a1(T4− T5). The constants a0and a1have been calculated from a set ofin situand coincident satellite measurements of sea surface temperatures taken over a wide range of latitudes. A linear split-window algorithm optimized for mid-latitude conditions has been also derived. The combined effects of atmospheric attenuation and surface emissivity on T4− T5have been discussed. Moreover, the theoretical results derived are applied to a NOAA-9 image, for which a procedure for estimating the channel emissivity difference (ε4− ε5) using satellite sensor data is suggested.

ISSN:0143-1161    

DOI:10.1080/01431169408954054

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

This paper describes a computationally fast and accurate technique for the atmospheric correction of satellite measurements in the solar spectrum. The main advantage of the method is that it is several hundred times faster than more detailed radiative transfer models like 5S and that it does not require precalculated look-up tables. The method is especially useful for correcting the huge amounts of data acquired by large-field-of-view high-repetitivity sensors, like the ones on board polar orbiting and geostationary meteorological satellites.

ISSN:0143-1161    

DOI:10.1080/01431169408954055

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

This paper reviews the applications of NOAA-AVHRR 1 km data for environmental monitoring. This topic is very significant in view of the development of 1 km global datasets. Research papers reviewed herein largely describe application of AVHRR data for North America, Sahelian Africa and tropical forests of both Africa and South America. All 5 AVHRR bands have found some level of use for land-cover studies. However, the majority of the land characterization research papers used AVHRR/ NDVI as the main data source. Multi-temporal NDVI datasets have found wide use to describe vegetation phenology. AVHRR thermal bands have also been employed by a number of researchers for both surface temperature mapping and for land-cover discrimination especially in tropical rain forests. The most common data analysis techniques employed in these papers were regressions between bands and supervised classification procedures. The review indicated that land-cover mapping and direct estimation of land-cover parameters using AVHRR data is still at an early stage of development. Applications of AVHRR data for these purposes, however, are likely to increase with the availability of I km standard products.

ISSN:0143-1161    

DOI:10.1080/01431169408954056

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor