Satellite observed seasonal and inter-annual variation of vegetation over the Kalahari, the Great Victoria Desert, and the Great Sandy Desert - 1979-1984
TL;DR: In this article, the authors compare and contrast seasonal and interannual variation of vegetation over these three deserts using the normalized difference vegetation index and the 37 GHz brightness temperature, and find that the seasonal variation from both sensors was most pronounced over the Kalahari, followed by the Great Sandy Desert and the Great Victoria Desert.
About: This article is published in Remote Sensing of Environment.The article was published on 1987-11-01. It has received 40 citations till now. The article focuses on the topics: Desert climate & Normalized Difference Vegetation Index.
Citations
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TL;DR: The proposed methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance.
Abstract: The frequent presence of clouds in passive remotely sensed imagery severely limits its regular exploitation in various application fields. Thus, the removal of cloud cover from this imagery represents an important preprocessing task consisting in the reconstruction of cloud-contaminated data. The intent of this study is to propose two novel general methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images. Given a cloud-contaminated image of the sequence, each area of missing measurements is reconstructed through an unsupervised contextual prediction process that reproduces the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. In the first method, the contextual prediction process is implemented by means of an ensemble of linear predictors, each trained over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected subset. In order to obtain such regions, each temporal image is locally classified by an unsupervised classifier based on the expectation-maximization (EM) algorithm. In the second method, the local spectro-temporal relationships are reproduced by a single nonlinear predictor based on the support vector machines (SVM) approach. To illustrate the performance of the two proposed methods, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor over a total period of four months is reported and discussed. It includes a detailed simulation study that aims at assessing with different reconstruction quality criteria the accuracy of the methods in different qualitative and quantitative cloud contamination conditions. Compared with two techniques based on compositing algorithms for cloud removal, the proposed methods show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance.
132Â citations
Cites background from "Satellite observed seasonal and int..."
...Several algorithms, like the second highest (SH) [24] and the modified maximum average (MMA) [25], have been developed to remove cloud effects from SSM/I images....
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TL;DR: A review of remote sensing techniques in change detection studies is presented in this article, where the authors focus on the utility of these methods for various resource applications, including transparency compositing, image differencing, classification, band ratioing, and principal components analysis.
Abstract: A review of remote sensing techniques in change detection studies is presented. Due to the nature of imagery examined, most change detection studies have involved the use of Advanced Very High Resolution Radiometer (AVHRR), Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS), SPOT data, and aerial photography. Change detection techniques have included transparency compositing, image differencing, classification, band ratioing, and principal components analysis. This paper will focus on a review of the utility of these methods for various resource applications.
124Â citations
TL;DR: In this article, simplified radiative transfer models for 37 GHz brightness temperatures were developed and applied to observations taken with the Nimbus-7 scanning multichannel microwave radiometer, and it was found that, as the vegetation density increases, polarization difference decreases.
Abstract: Simplified radiative transfer models for 37 GHz brightness temperatures are developed and applied to observations taken with the Nimbus-7 scanning multichannel microwave radiometer. It is found that, as the vegetation density increases, polarization difference decreases. The polarization difference also decreases with increasing surface roughness. Empirical relations are established between the satellite observations and annual rainfall over Africa and Australia, primary productivity, and actual evaporation. Color-coded maps of primary productivity and evaporation are presented.
116Â citations
TL;DR: In this article, the authors compared AVHRR normalized difference vegetation index (NDVI) data with Scanning Multichannel Microwave Radiometer microwave polarization difference temperature (MPDT) data for the study of vegetation phenology.
Abstract: AVHRR normalized difference vegetation index (NDVI) data for a one-year period were compared with Scanning Multichannel Microwave Radiometer microwave polarization difference temperature (MPDT) data for the study of vegetation phenology. It is shown that the MPDT response differs considerably from the seasonal NDVI pattern. The results do not support the hypothetical relationship between MPDT and leaf water content. It is found that only vegetation types with a substantial seasonal variation in the areal extent of vegetated cover show strong seasonality in MPDT data.
63Â citations
References
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TL;DR: In this paper, satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics.
Abstract: Red and near-infrared satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics. The technique minimizes cloud contamination, reduces directional reflectance and off-nadir viewing effects, minimizes sun-angle and shadow effects, and minimizes aerosol and water-vapor effects. The improvement is highly dependent on the state of the atmosphere, surface-cover type, and the viewing and illumination geometry of the sun, target and sensor. An example from southern Africa showed an increase of 40 percent from individual image values tothe final composite image. Limitations associated with the technique are discussed, and recommendations are given to improve this approach.
2,760Â citations
TL;DR: In this paper, a two-stream approximation model of radiative transfer was used to calculate values of hemispheric canopy reflectance in the visible and near-infrared wavelength intervals.
Abstract: A two-stream approximation model of radiative transfer is used to calculate values of hemispheric canopy reflectance in the visible and near-infrared wavelength intervals. Simple leaf models of photosynthesis and stomatal resistance are integrated over leaf orientation and canopy depth to obtain estimates of canopy photosynthesis and bulk stomatal or canopy resistance. The ratio of near-infrared and visible reflectances is predicted to be a near linear indicator of minimum canopy resistance and photosynthetic capacity but a poor predictor of leaf area index or biomass.
2,198Â citations
1,262Â citations
TL;DR: In this paper, the authors presented coarse spatial resolution, high temporal frequency satellite data from the NOAA/AVHRR system to demonstrate their utility for monitoring vegetation seasonal dynamics, including forest clearance in Brazil, the productivity of African grasslands, Indian tropical forest and Chinese agriculture.
Abstract: Coarse spatial resolution, high temporal frequency satellite data from the NOAA/AVHRR system are presented to demonstrate their utility for monitoring vegetation seasonal dynamics. The techniques for processing and analysing the data are outlined and examples are given for selected applications at a range of scales. Normalized difference vegetation index images are presented for the entire globe and for the continents of Africa, South America and south-east Asia, with descriptions of the seasonal dynamics of major vegetation formations as portrayed by the transformed AVHRR data. Monitoring of forest clearance in Brazil, the productivity of African grasslands, Indian tropical forest and Chinese agriculture are selected for discussion. The paper concludes that coarse-resolution satellite data provide a valuable tool for vegetation mapping and monitoring at regional and global scales.
1,125Â citations
TL;DR: In this article, a strong correlation between the integrated NOAA-7 satellite data and end-of-season above-ground dry biomass was found for ground samples collected over a 3-year period.
801Â citations