An Introduction to Spatial Data Analysis

2020 ◽  
Author(s):  
Martin Wegmann ◽  
Jakob Schwalb-Willmann ◽  
Stefan Dech
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Spatial Data Analysis ◽  
Data Sets ◽  
Data Handling ◽  
Book Covers

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

scholarly journals Progress in the R ecosystem for representing and handling spatial data

2020 ◽  
Author(s):  
Roger S. Bivand
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Data Handling ◽  
Good Match ◽  
R Programming Language ◽  
R Packages ◽  
R Programming

Abstract Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. 10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend.

scholarly journals CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)

2016 ◽  
Vol XLI-B8 ◽  
pp. 669-672
Author(s):  
B. A. Johnson ◽  
H. Scheyvens ◽  
H. Samejima ◽  
M. Onoda
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Median Number ◽  
Forest Carbon ◽  
Data Sets ◽  
Median Percentage ◽  
Forestry Sector

Developing countries must submit forest reference emission levels (FRELs) to the UNFCCC to receive incentives for REDD+ activities (e.g. reducing emissions from deforestation/forest degradation, sustainable management of forests, forest carbon stock conservation/enhancement). These FRELs are generated based on historical CO2 emissions in the land use, land use change, and forestry sector, and are derived using remote sensing (RS) data and in-situ forest carbon measurements. Since the quality of the historical emissions estimates is affected by the quality and quantity of the RS data used, in this study we calculated five metrics (i-v below) to assess the quality and quantity of the data that has been used thus far. Countries could focus on improving on one or more of these metrics for the submission of future FRELs. Some of our main findings were: (i) the median percentage of each country mapped was 100%, (ii) the median historical timeframe for which RS data was used was 11.5 years, (iii) the median interval of forest map updates was 4.5 years, (iv) the median spatial resolution of the RS data was 30m, and (v) the median number of REDD+ activities that RS data was used for operational monitoring of was 1 (typically deforestation). Many new sources of RS data have become available in recent years, so complementary or alternative RS data sets for generating future FRELs can potentially be identified based on our findings; e.g. alternative RS data sets could be considered if they have similar or higher quality/quantity than the currently-used data sets.

Using high-resolution groundwater data for the validation of a global hydrological model: evaluating WaterGAP and calibration/data assimilation (C/DA) performance over France

2021 ◽  
Author(s):  
Kuei-Hua Hsu ◽  
Laurent Longuevergne ◽  
Annette Eicker ◽  
Mehedi Hasan ◽  
Andreas Güntner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Freshwater Resources ◽  
Sensing Data

<p>The dynamic global water cycle is of ecological and societal importance as it affects the availability of freshwater resources and influences extreme events such as floods and droughts. This work is set in the frame of the GlobalCDA Research Unit, which has the goal of developing a calibration/data assimilation approach (C/DA) to improve the quantification of freshwater resources by combining the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data. This presentation focuses on the validation of the C/DA results using an independent in-situ groundwater data set based on ~1500 monitoring boreholes in France.</p><p>The resulting validation data set is applied to independently assess the output of several C/DA experiments: data assimilation using different combinations of the available geodetic and remote sensing data sets and different methods of model calibration, based on either an ensemble Kalman filter approach or a Pareto-optimal calibration algorithm.</p><p>To further understand in-situ groundwater and WaterGAP data set, we subtract the coherent signals using Empirical orthogonal function (EOF).  Over 85% variances can be explained by the first 3 EOFs for both data sets.</p>

scholarly journals Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

2015 ◽  
Vol 8 (1) ◽  
pp. 483-503 ◽  
Author(s):  
M. Schneider ◽  
Y. González ◽  
C. Dyroff ◽  
E. Christner ◽  
A. Wiegele ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapour ◽  
Moisture Transport ◽  
Lower Troposphere ◽  
Remote Sensing Data ◽  
Added Value ◽  
Data Sets ◽  
Empirical Validation ◽  
Sensing Data

Abstract. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isotopologue remote sensing and in situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products, generated from ground-based FTIR (Fourier transform infrared), spectrometer and space-based IASI (infrared atmospheric sounding interferometer) observation. The study is made in the area of the Canary Islands in the subtropical northern Atlantic. As reference we use well calibrated in situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues, and the scatter with respect to the in situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In both remote sensing data sets we find a positive δD bias of 30–70‰. Complementing H2O observations with δD data allows moisture transport studies that are not possible with H2O observations alone. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data. We document that the δD–H2O curves obtained from the different in situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2015 ◽  
Vol 19 (5) ◽  
pp. 2337-2351 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Landsat Images ◽  
Forested Areas ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

scholarly journals Mathematical aspects of the kriging applied on landslide in Halenkovice (Czech Republic)

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Robert Zůvala ◽  
Eva Fišerová ◽  
Lukáš Marek
Keyword(s):  
Czech Republic ◽  
Data Analysis ◽  
Statistical Methods ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Data Sets ◽  
Natural Phenomena ◽  
Digital Elevation ◽  
Geostatistical Techniques ◽  
Landslide Modelling

AbstractKriging is one of the geostatistical techniques for spatial data analysis that is usually used for a modelling of natural phenomena or a creation of digital elevation models. In this paper, we introduce kriging methods in the context of a landslide modelling in time. The proposed procedure, as well as most of the statistical methods, is designed for complete data sets,

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