The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics

Solving geoinformatics parametric polynomial systems using the improved Dixon resultant

Improvements in computational and observational technologies in geoinformatics, e.g., the use of laser scanners that produce huge point cloud data sets, or the proliferation of global navigation satellite systems (GNSS) and unmanned aircraft vehicles (UAVs), have brought with them the challenges of handling and processing this “big data”. These call for improvement or development of better processing algorithms. One way to do that is integration of symbolically presolved sub-algorithms to speed up computations. Using examples of interest from real geoinformatic problems, we will discuss the Dixon-EDF resultant as an improved resultant method for the symbolic solution of parametric polynomial systems. We will briefly describe the method itself, then discuss geoinformatics problems arising in minimum distance mapping (MDM), parameter transformations, and pose estimation essential for resection. Dixon-EDF is then compared to older notions of “Dixon resultant”, and to several respected implementations of Gröbner bases algorithms on several systems. The improved algorithm, Dixon-EDF, is found to be greatly superior, usually by orders of magnitude, in both CPU usage and RAM usage. It can solve geoinformatics problems on which the other methods fail, making symbolic solution of parametric systems feasible for many problems

Pareto optimality solution of the multi-objective photogrammetric resection-intersection problem

Reconstruction of architectural structures from photographs has recently experienced intensive efforts in computer vision research. This is achieved through the solution of nonlinear least squares (NLS) problems to obtain accurate structure and motion estimates. In Photogrammetry, NLS contribute to the determination of the 3-dimensional (3D) terrain models from the images taken from photographs. The traditional NLS approach for solving the resection-intersection problem based on implicit formulation on the one hand suffers from the lack of provision by which the involved variables can be weighted. On the other hand, incorporation of explicit formulation expresses the objectives to be minimized in different forms, thus resulting in different parametric values for the estimated parameters at non-zero residuals. Sometimes, these objectives may conflict in a Pareto sense, namely, a small change in the parameters results in the increase of one objective and a decrease of the other, as is often the case in multi-objective problems. Such is often the case with error-in-all-variable (EIV) models, e.g., in the resection-intersection problem where such change in the parameters could be caused by errors in both image and reference coordinates.This study proposes the Pareto optimal approach as a possible improvement to the solution of the resection-intersection problem, where it provides simultaneous estimation of the coordinates and orientation parameters of the cameras in a two or multistation camera system on the basis of a properly weighted multi-objective function. This objective represents the weighted sum of the square of the direct explicit differences of the measured and computed ground as well as the image coordinates. The effectiveness of the proposed method is demonstrated by two camera calibration problems, where the internal and external orientation parameters are estimated on the basis of the collinearity equations, employing the data of a Manhattan-type test field as well as the data of an outdoor, real case experiment. In addition, an architectural structural reconstruction of the Merton college court in Oxford (UK) via estimation of camera matrices is also presented. Although these two problems are different, where the first case considers the error reduction of the image and spatial coordinates, while the second case considers the precision of the space coordinates, the Pareto optimality can handle both problems in a general and flexible way

GNSS remote sensing of the Australian tropopause

Radio occultation (RO) techniques that use signals transmitted by Global Navigation Satellite Systems (GNSS) have emerged over the past decade as an important tool for measuring global changes in tropopause temperature and height, a valuable capacity given the tropopause’s sensitivity to temperature variations. This study uses 45,091 RO data from the CHAMP (CHAllenging Minisatellite Payload, 80 months), GRACE (Gravity Recovery And Climate Experiment, 23 months) and COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate, 20 months) satellites to analyse the variability of the tropopause’s height and temperature over Australia. GNSS RO temperature profiles from CHAMP, GRACE, and COSMIC are first validated using radiosonde observations provided by the Bureau of Meteorology (Australia). These are compared to RO soundings from between 2001 and 2007 that occurred within 3 h and 100 km of a radiosonde.The results indicate that RO soundings provide data of a comparable quality to radiosonde observations in the tropopause region, with temperature deviations of less than 0.5 ± 1.5 K. An analysis of tropopause height and temperature anomalies indicates a height increase over Australia as a whole of ca. 4.8 ± 1.3 m between September 2001 and April 2008, with a corresponding temperature decrease of −0.019 ± 0.007 K. A similar pattern of increasing height/decreasing temperature was generally observed when determining the spatial distribution of the tropopause height and temperature rate of change over Australia. Although only a short period has been considered in this study, a function of the operating time of these satellites, the results nonetheless show an increase in the height of the tropopause over Australia during this period and thus may indicate regional warming. Several mechanisms could be responsible for these changes, such as an increase in the concentration of greenhouse gases in the atmosphere, and lower stratospheric cooling due to ozone loss, both of which have been observed during the last decades

Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data

West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management.Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162days as well as a—hopefully—limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near real-time GRACE forecast over the regions that exhibit strong teleconnections

Basin-Scale Control on the Phytoplankton Biomass in Lake Victoria, Africa

The relative bio-optical variability within Lake Victoria was analyzed through the spatio-temporal decomposition of a 1997–2004 dataset of remotely-sensed reflectance ratios in the visible spectral range. Results show a regular seasonal pattern with a phase shift (around 2 months) between the south and north parts of the lake. Interannual trends suggested a teleconnection between the lake dynamics and El-Niño phenomena. Both seasonal and interannual patterns were associated to conditions of light limitation for phytoplankton growth and basin-scale hydrodynamics on phytoplankton access to light. Phytoplankton blooms developed during the periods of lake surface warming and water column stability. The temporal shift apparent in the bio-optical seasonal cycles was related to the differential cooling of the lake surface by southeastern monsoon winds. North-south differences in the exposure to trade winds are supported by the orography of the Eastern Great Rift Valley. The result is that surface layer warming begins in the northern part of the lake while the formation of cool and dense water continues in the southern part. The resulting buoyancy field is sufficient to induce a lake-wide convective circulation and the tilting of the isotherms along the north-south axis. Once surface warming spreads over the whole lake, the phytoplankton bloom dynamics are subjected to the internal seiche derived from the relaxation of thermocline tilting. In 1997–98, El-Niño phenomenon weakened the monsoon wind flow which led to an increase in water column stability and a higher phytoplankton optical signal throughout the lake. This suggests that phytoplankton response to expected climate scenarios will be opposite to that proposed for nutrient-limited great lakes. The present analysis of remotely-sensed bio-optical properties in combination with environmental data provides a novel basin-scale framework for research and management strategies in Lake Victoria

Global lake responses to climate change

Climate change is one of the most severe threats to global lake ecosystems. Lake surface conditions, such as ice cover, surface temperature, evaporation and water level, respond dramatically to this threat, as observed in recent decades. In this Review, we discuss physical lake variables and their responses to climate change. Decreases in winter ice cover and increases in lake surface temperature modify lake mixing regimes and accelerate lake evaporation. Where not balanced by increased mean precipitation or inflow, higher evaporation rates will favour a decrease in lake level and surface water extent. Together with increases in extreme-precipitation events, these lake responses will impact lake ecosystems, changing water quantity and quality, food provisioning, recreational opportunities and transportation. Future research opportunities, including enhanced observation of lake variables from space (particularly for small water bodies), improved in situ lake monitoring and the development of advanced modelling techniques to predict lake processes, will improve our global understanding of lake responses to a changing climate

Nonlinear homotopy in geodesy

In Paláncz et al. (J Geod 84: 79–85, 2009), linear homotopy was introduced and its applications to geodesy presented. Never before had the concept of nonlinear homotopy been used by the geodetic community. This is partly attributed to the complexity of the involved equations and partly due to the computational time required. Recently, however, Nor et al. (MATEMATIKA 29: 159–171, 2013) suggested the possibility of constructing nonlinear homotopy. In this short note, Nor et al. (MATEMATIKA 29: 159–171, 2013) idea is developed for geodetic applications and an example of its use illustrated