Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

The glaciers climate change initiative: Methods for creating glacier area, elevation change and velocity products

Glaciers and their changes through time are increasingly obtained from a wide range of satellite sensors. Due to the often remote location of glaciers in inaccessible and high-mountain terrain, satellite observations frequently provide the only available measurements. Furthermore, satellite data provide observations of glacier character- istics that are difficult to monitor using ground-based measurements, thus complementing the latter. In the Glaciers_cci project of the European Space Agency (ESA), three of these characteristics are investigated in detail: glacier area, elevation change and surface velocity. We use (a) data from optical sensors to derive glacier outlines, (b) digital elevation models from at least two points in time, (c) repeat altimetry for determining elevation changes, and (d) data from repeat optical and microwave sensors for calculating surface velocity. For the latter, the two sensor types provide complementary information in terms of spatio-temporal coverage. While (c) and (d) can be generated mostly automatically, (a) and (b) require the intervention of an analyst. Largely based on the results of various round robin experiments (multi-analyst benchmark studies) for each of the products, we suggest and describe the most suitable algorithms for product creation and provide recommendations concerning their practical implementation and the required post-processing. For some of the products (area, velocity) post-processing can influence product quality more than the main-processing algorithm

Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm

Modeling Spatial Surface Energy Fluxes of Agricultural and Riparian Vegetation Using Remote Sensing

Modeling of surface energy fluxes and evapotranspiration (ET) requires the understanding of the interaction between land and atmosphere as well as the appropriate representation of the associated spatial and temporal variability and heterogeneity. This dissertation provides new methodology showing how to rationally and properly incorporate surface features characteristics/properties, including the leaf area index, fraction of cover, vegetation height, and temperature, using different representations as well as identify the related effects on energy balance flux estimates including ET. The main research objectives were addressed in Chapters 2 through 4 with each presented in a separate paper format with Chapter 1 presenting an introduction and Chapter 5 providing summary and recommendations. Chapter 2 discusses a new approach of incorporating temporal and spatial variability of surface features. We coupled a remote sensing-based energy balance model with a traditional water balance method to provide improved estimates of ET. This approach was tested over rainfed agricultural fields ~ 10 km by 30 km in Ames, Iowa. Before coupling, we modified the water balance method by incorporating a remote sensing-based estimate for one of its parameters to ameliorate its performance on a spatial basis. Promising results were obtained with indications of improved estimates of ET and soil moisture in the root zone. The effects of surface features heterogeneity on measurements of turbulence were investigated in Chapter 3. Scintillometer-based measurements/estimates of sensible heat flux (H) were obtained over the riparian zone of the Cibola National Wildlife Refuge (CNWR), California. Surface roughness including canopy height (hc), roughness length, and zero-plane displacement height were incorporated in different ways, to improve estimates of H. High resolution, 1-m maps of ground surface digital elevation model and canopy height, hc, were derived from airborne LiDAR sensor data to support the analysis. The effects of using different pixel resolutions to account for surface feature variability on modeling energy fluxes, e.g., net radiation, soil, sensible, and latent heat, were studied in Chapter 4. Two different modeling approaches were applied to estimate energy fluxes and ET using high and low pixel resolution datasets obtained from airborne and Landsat sensors, respectively, provided over the riparian zone of the CNWR, California. Enhanced LiDAR-based hc maps were also used to support the modeling process. The related effects were described relative to leaf area index, fraction of cover, hc, soil moisture status at root zone, groundwater table level, and vegetation stress conditions

The Impact of Sensor Characteristics and Data Availability on Remote Sensing Based Change Detection

Land cover and land use change are among the major drivers of global change. In a time of mounting challenges for sustainable living on our planet any research benefits from interdisciplinary collaborations to gain an improved understanding of the human-environment system and to develop suitable and improve existing measures of natural resource management. This includes comprehensive understanding of land cover and land use changes, which is fundamental to mitigate global change. Remote sensing technology is essential for the analyses of the land surface (and hence related changes) because it offers cost-effective ways of collecting data simultaneously over large areas. With increasing variety of sensors and better data availability, the application of remote sensing as a means to assist in modeling, to support monitoring, and to detect changes at various spatial and temporal scales becomes more and more feasible. The relationship between the nature of the changes on the land surface, the sensor properties, and the conditions at the time of acquisition influences the potential and quality of land cover and land use change detection. Despite the wealth of existing change detection research, there is a need for new methodologies in order to efficiently explore the huge amount of data acquired by remote sensing systems with different sensor characteristics. The research of this thesis provides solutions to two main challenges of remote sensing based change detection. First, geometric effects and distortions occur when using data taken under different sun-target-sensor geometries. These effects mainly occur if sun position and/or viewing angles differ between images. This challenge was met by developing a theoretical framework of bi-temporal change detection scenarios. The concept includes the quantification of distortions that can occur in unfavorable situations. The invention and application of a new method – the Robust Change Vector Analysis (RCVA) – reduced the detection of false changes due to these distortions. The quality and robustness of the RCVA were demonstrated in an example of bi-temporal cross-sensor change detection in an urban environment in Cologne, Germany. Comparison with a state-of-the-art method showed better performance of RCVA and robustness against thresholding. Second, this thesis provides new insights into how to optimize the use of dense time series for forest cover change detection. A collection of spectral indices was reviewed for their suitability to display forest structure, development, and condition at a study site on Vancouver Island, British Columbia, Canada. The spatio-temporal variability of the indices was analyzed to identify those indices, which are considered most suitable for forest monitoring based on dense time series. Amongst the indices, the Disturbance Index (DI) was found to be sensitive to the state of the forest (i.e., forest structure). The Normalized Difference Moisture Index (NDMI) was found to be spatio-temporally stable and to be the most sensitive index for changes in forest condition. Both indices were successfully applied to detect abrupt forest cover changes. Further, this thesis demonstrated that relative radiometric normalization can obscure actual seasonal variation and long-term trends of spectral signals and is therefore not recommended to be incorporated in the time series pre-processing of remotely-sensed data. The main outcome of this part of the presented research is a new method for detecting discontinuities in time series of spectral indices. The method takes advantage of all available information in terms of cloud-free pixels and hence increases the number of observations compared to most existing methods. Also, the first derivative of the time series was identified (together with the discontinuity measure) as a suitable variable to display and quantify the dynamic of dense Landsat time series that cannot be observed with less dense time series. Given that these discontinuities are predominantly related to abrupt changes, the presented method was successfully applied to clearcut harvest detection. The presented method detected major events of forest change at unprecedented temporal resolution and with high accuracy (93% overall accuracy). This thesis contributes to improved understanding of bi-temporal change detection, addressing image artifacts that result from flexible acquisition features of modern satellites (e.g., off-nadir capabilities). The demonstrated ability to efficiently analyze cross-sensor data and data taken under unfavorable conditions is increasingly important for the detection of many rapid changes, e.g., to assist in emergency response. This thesis further contributes to the optimized use of remotely sensed time series for improving the understanding, accuracy, and reliability of forest cover change detection. Additionally, the thesis demonstrates the usability of and also the necessity for continuity in medium spatial resolution satellite imagery, such as the Landsat data, for forest management. Constellations of recently launched (e.g., Landsat 8 OLI) and upcoming sensors (e.g., Sentinel-2) will deliver new opportunities to apply and extend the presented methodologies.Der Einfluss von Sensorcharakteristik und Datenverfügbarkeit auf die fernerkundungsbasierte Veränderungsdetektion Landbedeckungs- und Landnutzungswandel gehören zu den Haupttriebkräften des Globalen Wandels. In einer Zeit, in der ein nachhaltiges Leben auf unserem Planeten zu einer wachsenden Herausforderung wird, profitiert die Wissenschaft von interdisziplinärer Zusammenarbeit, um ein besseres Verständnis der Mensch-Umwelt-Beziehungen zu erlangen und um verbesserte Maßnahmen des Ressourcenmanagements zu entwickeln. Dazu gehört auch ein erweitertes Verständnis von Landbedeckungs- und Landnutzungswandel, das elementar ist, um dem Globalen Wandel zu begegnen. Die Fernerkundungstechnologie ist grundlegend für die Analyse der Landoberfläche und damit verknüpften Veränderungen, weil sie in der Lage ist, große Flächen gleichzeitig zu erfassen. Mit zunehmender Sensorenvielfalt und besserer Datenverfügbarkeit gewinnt Fernerkundung bei der Modellierung, beim Monitoring sowie als Mittel zur Erkennung von Veränderungen in verschiedenen räumlichen und zeitlichen Skalen zunehmend an Bedeutung. Das Wirkungsgeflecht zwischen der Art von Veränderungen der Landoberfläche, Sensoreigenschaften und Aufnahmebedingungen beeinflusst das Potenzial und die Qualität fernerkundungsbasierter Landbedeckungs- und Landnutzungsveränderungs-detektion. Trotz der Fülle an bestehenden Forschungsleistungen zur Veränderungsdetektion besteht ein dringender Bedarf an neuen Methoden, die geeignet sind, das große Aufkommen von Daten unterschiedlicher Sensoren effizient zu nutzen. Die in dieser Abschlussarbeit durchgeführte Forschung befasst sich mit zwei aktuellen Problemfeldern der fernerkundungsbasierten Veränderungsdetektion. Das erste sind die geometrischen Effekte und Verzerrungen, die auftreten, wenn Daten genutzt werden, die unter verschiedenen Sonne-Zielobjekt-Sensor-Geometrien aufgenommen wurden. Diese Effekte treten vor allem dann auf, wenn unterschiedliche Sonnenstände und/oder unterschiedliche Einfallswinkel der Satelliten genutzt werden. Der Herausforderung wurde begegnet, indem ein theoretisches Konzept von Szenarien dargelegt wurde, die bei der bi-temporalen Veränderungsdetektion auftreten können. Das Konzept beinhaltet die Quantifizierung der Verzerrungen, die in ungünstigen Fällen auftreten können. Um die Falscherkennung von Veränderungen in Folge der resultierenden Verzerrungen zu reduzieren, wurde eine neue Methode entwickelt – die Robust Change Vector Analysis (RCVA). Die Qualität der Methode wird an einem Beispiel der Veränderungsdetektion im urbanen Raum (Köln, Deutschland) aufgezeigt. Ein Vergleich mit einer anderen gängigen Methode zeigt bessere Ergebnisse für die neue RCVA und untermauert deren Robustheit gegenüber der Schwellenwertbestimmung. Die zweite Herausforderung, mit der sich die vorliegende Arbeit befasst, betrifft die optimierte Nutzung von dichten Zeitreihen zur Veränderungsdetektion von Wäldern. Eine Auswahl spektraler Indizes wurde hinsichtlich ihrer Tauglichkeit zur Erfassung von Waldstruktur, Waldentwicklung und Waldzustand in einem Untersuchungsgebiet auf Vancouver Island, British Columbia, Kanada, bewertet. Um die Einsatzmöglichkeiten der Indizes für dichte Zeitreihen bewerten zu können, wurde ihre raum-zeitliche Variabilität untersucht. Der Disturbance Index (DI) ist ein Index, der sensitiv für das Stadium eines Waldes ist (d. h. seine Struktur). DerNormalized Difference Moisture Index (NDMI) ist raum-zeitlich stabil und zudem am sensitivsten für Veränderungen des Waldzustands. Beide Indizes wurden erfolgreich zur Erkennung von abrupten Veränderungen getestet. In der vorliegenden Arbeit wird aufgezeigt, dass die relative radiometrische Normierung saisonale Variabilität und Langzeittrends von Zeitreihen spektraler Signale verzerrt. Die relative radiometrische Normierung wird daher nicht zur Vorprozessierung von Fernerkundungszeitreihen empfohlen. Das wichtigste Ergebnis dieser Studie ist eine neue Methode zur Erkennung von Diskontinuitäten in Zeitreihen spektraler Indizes. Die Methode nutzt alle wolkenfreien, ungestörten Beobachtungen (d. h. unabhängig von der Gesamtbewölkung in einem Bild) in einer Zeitreihe und erhöht dadurch die Anzahl an Beobachtungen im Vergleich zu anderen Methoden. Die erste Ableitung und die Messgröße zur Erfassung der Diskontinuitäten sind gut geeignet, um die Dynamik dichter Zeitreihen zu beschreiben und zu quantifizieren. Dies ist mit weniger dichten Zeitreihen nicht möglich. Da diese Diskontinuitäten im Untersuchungsgebiet üblicherweise abrupter Natur sind, ist die Methode gut geeignet, um Kahlschläge zu erfassen. Die hier dargelegte neue Methode detektiert Waldbedeckungsveränderungen mit einzigartiger zeitlicher Auflösung und hoher Genauigkeit (93% Gesamtgenauigkeit). Die vorliegende Arbeit trägt zu einem verbesserten Verständnis bi-temporaler Veränderungsdetektion bei, indem Bildartefakte berücksichtigt werden, die infolge der Flexibilität moderner Sensoren entstehen können. Die dargestellte Möglichkeit, Daten zu analysieren, die von unterschiedlichen Sensoren stammen und die unter ungünstigen Bedingungen aufgenommen wurden, wird zukünftig bei der Erfassung von schnellen Veränderungen an Bedeutung gewinnen, z. B. bei Katastropheneinsätzen. Ein weiterer Beitrag der vorliegenden Arbeit liegt in der optimierten Anwendung von Fernerkundungszeitreihen zur Verbesserung von Verständnis, Genauigkeit und Verlässlichkeit der Waldveränderungsdetektion. Des Weiteren zeigt die Arbeit den Nutzen und die Notwendigkeit der Fortführung von Satellitendaten mit mittlerer Auflösung (z. B. Landsat) für das Waldmanagement. Konstellationen kürzlich gestarteter (z. B. Landsat 8 OLI) und zukünftiger Sensoren (z. B. Sentinel-2) werden neue Möglichkeiten zur Anwendung und Optimierung der hier vorgestellten Methoden bieten

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Time delay evaluation on thewater-leaving irradiance retrieved from empirical models and satellite imagery

Temporal delays and spatial randomness between ground-based data and satellite overpass involve important deviations between the empirical model output and real data; these are factors poorly considered in the model calibration. The inorganic matter-generated turbidity in Lake Chapala (Mexico) was taken as a study case to expose the influence of such factors. Ground-based data from this study and historical records were used as references. We take advantage of the at-surface reflectance from Landsat-8, sun-glint corrections, a reduced NIR-band range, and null organic matter incidence in these wavelengths to diminish the physical phenomena-related radiometric artifacts; leaving the spatio-temporal relationships as the principal factor inducing the model uncertainty. Non-linear correlations were assessed to calibrate the best empirical model; none of them presented a strong relationship (<73%), including that based on hourly delays. This last model had the best predictability only for the summer-fall season, explaining 71% of the turbidity variation in 2016, and 59% in 2017, with RMSEs < 24%. The instantaneous turbidity maps depicted the hydrodynamic complexity of the lake, highlighting a strong component of spatial randomness associated with the temporal delays. Reasonably, robust empirical models will be developed if several dates and sampling-sites are synchronized with more satellite overpasses.</p

Error sources and guidelines for quality assessment of glacier area, elevation change, and velocity products derived from satellite data in the Glaciers_cci project

Satellite data provide a large range of information on glacier dynamics and changes. Results are often reported, provided and used without consideration of measurement accuracy (difference to a true value) and precision (variability of independent assessments). Whereas accuracy might be difficult to determine due to the limited availability of appropriate reference data and the complimentary nature of satellite measurements, precision can be obtained from a large range of measures with a variable effort for determination. This study provides a systematic overview on the factors influencing accuracy and precision of glacier area, elevation change (from altimetry and DEM differencing), and velocity products derived from satellite data, along with measures for calculating them. A tiered list of recommendations is provided (sorted for effort from Level 0 to 3) as a guide for analysts to apply what is possible given the datasets used and available to them. The more simple measures to describe product quality (Levels 0 and 1) can often easily be applied and should thus always be reported. Medium efforts (Level 2) require additional work but provide a more realistic assessment of product precision. Real accuracy assessment (Level 3) requires independent and coincidently acquired reference data with high accuracy. However, these are rarely available and their transformation into an unbiased source of information is challenging. This overview is based on the experiences and lessons learned in the ESA project Glaciers_cci rather than a review of the literature