Using spectral information from the NIR water absorption features for the retrieval of canopy water content

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.     

Application of remote sensing technology for Impact Assessment of Watershed Development Programme

The current study was taken up to investigate the utility of remote sensing and GIS tools for evaluation of Integrated Wasteland Development Programme (IWDP) implemented during 1997–2001 in Katangidda Nala watershed, Chincholi taluk, Gulbarga district, Karnataka. The study was carried out using IRS 1C, LISS III data of December 11, 1997 (pre-treatment) and November 15, 2002 (post-treatment) covering the watershed to assess the changes in land use / land cover and biomass that have changed over a period of five years (1997–2002). The images were classified into different land use/land cover categories using supervised classification by maximum likelihood algorithm. They were also classified into different biomass levels using Normalized Difference Vegetation Index (NDVI) approach. The results indicated that the area under agriculture crops and forest land were increased by 671 ha (5.7%) and 1,414 ha (11.94%) respectively. This is due to the fact that parts of wastelands and fallow lands were brought into cultivation. This increase in the area may be attributed to better utilization of surface and ground waters, adoption of soil and water conservation practices and changes in cropping pattern. The area under waste lands and fallow lands decreased by 1,667 ha (14.07%) and 467 ha (3.94%), respectively. The vegetation vigour of the area was classified into three classes using NDVI. Substantial increase in the area under high and low biomass levels was observed (502 ha and 19 ha respectively). The benefit-cost analysis indicates that the use of remote sensing and GIS was 2.2 times cheaper than the conventional methods. Thus, the repetitive coverage of the satellite data provides an excellent opportunity to monitor the land resources and evaluate the land cover changes through comparison of images for the watershed at different periods.     

Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition

We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm for space-geodetic applications: firstly, we investigate how convergence can be accelerated by means of both subspace and block-diagonal preconditioning. The efficiency of the latter dominates if the design matrix exhibits block-dominant structure. Secondly, we address Tikhonov-Phillips regularization in general. Thirdly, we demonstrate an effective implementation of the algorithm in a high-performance computing environment. In this context, an important issue is to avoid the twofold computation of the design matrix in each iteration. The computational platform is a 64-processor shared-memory supercomputer. The runtime results prove the successful parallelization of the LSQR solver. The numerical examples are chosen in view of the forthcoming satellite mission GOCE (Gravity field and steady-state Ocean Circulation Explorer). The closed-loop scenario covers 1 month of simulated data with 5 s sampling. We focus exclusively on the analysis of radial components of satellite accelerations and gravity gradients. Our extensions to the basic algorithm enable the method to be competitive with well-established inversion strategies in satellite geodesy, such as conjugate gradient methods or the brute-force approach. In its current development stage, the LSQR method appears ready to deal with real-data applications.     

The ionospheric eclipse factor method (IEFM) and its application to determining the ionospheric delay for GPS

A new method for modeling the ionospheric delay using global positioning system (GPS) data is proposed, called the ionospheric eclipse factor method (IEFM). It is based on establishing a concept referred to as the ionospheric eclipse factor (IEF) λ of the ionospheric pierce point (IPP) and the IEF’s influence factor (IFF) . The IEF can be used to make a relatively precise distinction between ionospheric daytime and nighttime, whereas the IFF is advantageous for describing the IEF’s variations with day, month, season and year, associated with seasonal variations of total electron content (TEC) of the ionosphere. By combining λ and with the local time t of IPP, the IEFM has the ability to precisely distinguish between ionospheric daytime and nighttime, as well as efficiently combine them during different seasons or months over a year at the IPP. The IEFM-based ionospheric delay estimates are validated by combining an absolute positioning mode with several ionospheric delay correction models or algorithms, using GPS data at an international Global Navigation Satellite System (GNSS) service (IGS) station (WTZR). Our results indicate that the IEFM may further improve ionospheric delay modeling using GPS data.     

On the Use of CloudSat and MODIS Data for Estimating Hurricane Intensity

This letter presents preliminary results concerning the use of new observations from the A-Train Constellation for testing a new technique of remotely sensing hurricane intensity from space based on modeling a hurricane as a balanced, convectively neutral vortex. The key observational requirements are simultaneous, accurate measurements of cloud-top height, cloudtop temperature, and cloud profiling information across the center of the storm, although there are ways to bypass the need for cloud-top temperature. In this letter, the Moderate Resolution Imaging Spectroradiometer onboard Aqua provides an estimation of the cloud-top temperature, and the near-simultaneous CloudSat observations provide the essential cloud-top height and cloud profiling information. Initial results indicate that the new technique is a promising method for estimating storm intensity when compared post facto to the best track database. Potential uncertainties and room for further refinement of the technique are discussed.     

Population growth and the development of a central place system

This paper describes the spatial and functional evolution of a central place system as market conditions change with population growth. Utilizing a partial equilibrium optimization model, we examine the spatial response of two economic sectors to increases in market populations resulting from natural increase and migration. Response in both sectors is conditioned by threshold demand, with factor prices also affecting one of the sectors. As the central place system evolves it exhibits spatial and functional characteristics that are initially consistent with a Löschian landscape, then a Christallerian landscape at higher populations, while at even larger populations Krugman’s landscape emerges.