REMOTE SENSING DATA ASSIMILATION IN WATER QUALITY NUMERICAL MODELS FOR SIMULATION OF WATER COLUMN TEMPERATURE

Indiana University-Purdue University Indianapolis (IUPUI)Numerical models are important tools for simulating processes within complex natural systems, such as hydrodynamics and water quality processes within a water body. From decision makers’ perspectives, such models also serve as useful tools for predicting the impacts of water quality problems or develop early warning systems. However, accuracy of a numerical model developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing observations and spatially discrete in-situ observations to change initial conditions of the numerical model. This research addresses the challenge of improving the model performance by combining water temperature from multi-spectral remote sensing analysis and in-situ measurements. Results of the approach on a eutrophic reservoir in Central Indiana show that with four images of multi-spectral remote sensing data assimilated, the model results oscillate more from the in-situ measurements during the data assimilation period. For validation, the data assimilation has negative impacts on the root mean square error. According to quantitative analysis, more significant water temperature stratification leads to larger deviations. Sampling depth differences for remote sensing technology, in-situ measurements and model output are considered as possible error source

Statistical Modeling of Impacts El Niño Southern Oscillations (ENSO) on Land Surface Temperature in Small Medium Size City: Case Study Kuching Sarawak

El Niño Southern Oscillation (ENSO) can affect the daily temperature and the amount of rainfall and extreme weather such as floods and droughts. For that reason, scientists need to understand the process of developing ENSO and develop statistical models to predict the impact of ENSO to land surface temperature. The remote sensing data provide spatial information that allows studying the impact of ENSO on land surface temperature spatial patterns. This study examines the ability of remote sensing data to study and develop model statistical for predicting the ENSO effect on land surface temperature spatial patterns. Remote sensing data needs to go through a pre-process and digital Number conversion to Land Surface Temperature (LST). To ensure accurate remote sensing information, the calibration process is carried out using temperature data from the Meteorological Malaysia Department (MMD). The next step is to conduct a correlation analysis between LST and Oceanic Niño Index (ONI). The final step is to use linear regression in building a statistical model predicting the effect of ENSO on temperature and LST. This study also found that changes in ONI values ​​influence the value of LST and temperature. Improving knowledge and understanding of ENSO can provide ideas and strategies in reducing and adapting to the impact of ENSO on human beings

Perbandingan Penggunaan Data Hujan Satelit dan Data Hujan Lapangan untuk Pemodelan Hidrologi Hujan-Aliran (Studi Kasus DAS Tapung Kiri)

Data availability for hidrologic modeling usually become a problem because of incompleteness and imprecision data. The development of advanced technology recently encourage the development of hydrological modeling by using remote sensing data. This research conducted rainfall-runoff modeling using remote sensing data and ground rainfall data with Integrated Flood Analysis System (IFAS) tools and took a case study in Tapung Kiri sub-watershed in Riau Province. This model was simulated and calibrated with rainfall-runoff periode of forward verification (2005-2006) and backward verification (2006-2005). The results of this research shows that the model to be optimal after calibration process in rain satellite data backward verification period from 1 January to 31 December 2005 by corellation value (R) 0.75, volume error (VE) of 3.22%, and coefficient of efficiency (CE) 0.9. It means that this model has a high degree of association with measurement data (0,7 0,75)

Mixture of Latent Variable Models for Remotely Sensed Image Processing

The processing of remotely sensed data is innately an inverse problem where properties of spatial processes are inferred from the observations based on a generative model. Meaningful data inversion relies on well-defined generative models that capture key factors in the relationship between the underlying physical process and the measurements. Unfortunately, as two mainstream data processing techniques, both mixture models and latent variables models (LVM) are inadequate in describing the complex relationship between the spatial process and the remote sensing data. Consequently, mixture models, such as K-Means, Gaussian Mixture Model (GMM), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), characterize a class by statistics in the original space, ignoring the fact that a class can be better represented by discriminative signals in the hidden/latent feature space, while LVMs, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Sparse Representation (SR), seek representational signals in the whole image scene that involves multiple spatial processes, neglecting the fact that signal discovery for individual processes is more efficient. Although the combined use of mixture model and LVMs is required for remote sensing data analysis, there is still a lack of systematic exploration on this important topic in remote sensing literature. Driven by the above considerations, this thesis therefore introduces a mixture of LVM (MLVM) framework for combining the mixture models and LVMs, under which three models are developed in order to address different aspects of remote sensing data processing: (1) a mixture of probabilistic SR (MPSR) is proposed for supervised classification of hyperspectral remote sensing imagery, considering that SR is an emerging and powerful technique for feature extraction and data representation; (2) a mixture model of K “Purified” means (K-P-Means) is proposed for addressing the spectral endmember estimation, which is a fundamental issue in remote sensing data analysis; (3) and a clustering-based PCA model is introduced for SAR image denoising. Under a unified optimization scheme, all models are solved via Expectation and Maximization (EM) algorithm, by iteratively estimating the two groups of parameters, i.e., the labels of pixels and the latent variables. Experiments on simulated data and real remote sensing data demonstrate the advantages of the proposed models in the respective applications

Pemodelan Hujan - Aliran Daerah Aliran Sungai Rokan dengan Menggunakan Data Penginderaan Jauh

Data availability for modeling usually become a problem because of incompleteness and imprecision data. The development of knowledge and the advancement of technology progress encourage the development of hydrological modeling by using remote sensing data. This research conducted rainfall-runoff modeling using remote sensing data in Rokan watershed, Riau Province. To utilize remote sensing data, a special software namely Integrated Flood Analysis System (IFAS) is used in this research. IFAS is a remote sensing program that was developed by a research institution of the Japanese public works called the International Centre for Water Hazard and Risk Management (ICHARM). IFAS was used to model the rainfall-runoff in Rokan watershed with four types of different periods simulation and calibration. Rainfal-runoff period from 1 January 2003 until 31 December 2006, 1 January 2004 until 31 December 2006, 1 January 2005 until 31 December 2006 and 1 January 2006 until 31 December 2006 was used for this modeling and then was validated with period data of 2004 and 2005. The results become optimal after the calibration process period of two years and one year data. The two years period have the correlation (R) value 0,627, volume error (VE) 1,007%, and the coefficient of efficiency (CE) 0,615 and the one year period the correlation (R) value 0,663, volume error (VE) 3,30%, and the coefficient of efficiency (CE) 0,759

Spectral-Spatial Analysis of Remote Sensing Data: An Image Model and A Procedural Design

The distinguishing property of remotely sensed data is the multivariate information coupled with a two-dimensional pictorial representation amenable to visual interpretation. The contribution of this work is the design and implementation of various schemes that exploit this property. This dissertation comprises two distinct parts. The essence of Part One is the algebraic solution for the partition function of a high-order lattice model of a two dimensional binary particle system. The contribution of Part Two is the development of a procedural framework to guide multispectral image analysis. The characterization of binary (black and white) images with little semantic content is discussed in Part One. Measures of certain observable properties of binary images are proposed. A lattice model is introduced, the solution to which yields functional mappings from the model parameters to the measurements on the image. Simulation of the model is explained, as is its usage in the design of Bayesian priors to bias classification analysis of spectral data. The implication of such a bias is that spatially adjacent remote sensing data are identified as belonging to the same class with a high likelihood. Experiments illustrating the benefit of using the model in multispectral image analysis are also discussed. The second part of this dissertation presents a procedural schema for remote sensing data analysis. It is believed that the data crucial to a succc~ssful analysis is provided by the human, as an interpretation of the image representation of the remote sensing spectral data. Subsequently, emphasis is laid on the design of an intelligent implementation of existing algorithms, rather than the development of new algorithms for analysis. The development introduces hyperspectral analysis as a problem requiring multi-source data fusion and presents a process model to guide the design of a solution. Part Two concludes with an illustration of the schema as used in the classification analysis of a given hyperspectral data set

Using Satellite-Based Hydro-Climate Variables And Machine Learning For Streamflow Modeling At Various Scales In The Upper Mississippi River Basin

Streamflow data are essential to study the hydrologic cycle and to attain appropriate water resource management policies. However, the availability of gauge data is limited due to various reasons such as economic, political, instrumental malfunctioning, and poor spatial distribution. Although streamflow can be simulated by process-based and machine learning approaches, applicability is limited due to intensive modeling effort, or its black-box nature, respectively. Here, we introduce a machine learning (Boosted Regression Tree (BRT)) approach based on remote sensing data to simulate monthly streamflow for three of varying sizes watersheds in the Upper Mississippi River Basin (UMRB). By integrating spatial land surface and climate variables that describe the subwatersheds in a basin as an input dataset and streamflow as an output learning dataset in a machine learning model (MLM), relationships between watershed characteristics and streamflow are established. The testing results of NSE with UMRB, IRW, and RRW of 0.8042, 0.7593, and 0.6856, respectively showed the remote sensing-based MLM can be effectively applied to streamflow prediction and has advantages for large basins compared with the performances of process-based approaches. Further, Predictor Importance (PI) analysis revealed the most important remote sensing variables and the most representative subwatersheds

Application of airborne LiDAR bathymetry in Norway

New technologies in remote sensing provide opportunities for effectively sampling information on topography and bathymetry for large areas. With Airborne LiDAR Bathymetry (ALB) terrain and also the river bottom (bathymetry) can be measured with high accuracy. In this report we present our contributions to 1) validation, 2) flood risk analysis and mitigation, and 3) river restoration. All rivers could be classified to river types according to Hauer & Pulg (2018) from remote sensing data only. ALB data can be much faster than other surveying or mapping methods and has higher accuracy. Ecological information can be acquired from ALB data in higher resolution than with other methods, and also parameters like grain size and shelter have a high correlation with ALB derivates. The ALB datasets can be used for planning and assessing ecological and flood related questions from the desktop with a strongly reduced requirement for field work compared to data from other data sources, additionally giving a model verification with much higher accuracy and detail degree than other methods. ALB can therefore improve planning safety and speed up planning and modelling process for high- flow, low-flow, morphodynamics and ecological applications. The Lærdal flood case study shows that advances in remote sensing can be used to develop and model nature-based and integrated solutions for improving flood safety and ecological status.Application of airborne LiDAR bathymetry in NorwaypublishedVersio

Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy)

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM-OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (SVM) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time