Comparing remote sensing and ground-based methods of quantifying cover type and carbon storage in an urban forest

Understanding the canopy cover of an urban environment leads to better estimates of carbon storage and more informed management decisions by urban foresters. The most commonly used method for assessing urban forest cover type extent is ground surveys, which can be both timeconsuming and expensive. The analysis of aerial photos is an alternative method that is faster, cheaper, and can cover a larger number of sites, but may be less accurate. The objectives of this paper were (1) to compare three methods of cover type assessment for Los Angeles, CA: handdelineation of aerial photos in ArcMap, supervised classification of aerial photos in ERDAS Imagine, and ground-collected data using the Urban Forest Effects (UFORE) model protocol; (2) to determine how well remote sensing methods estimate carbon storage as predicted by the UFORE model; and (3) to explore the influence of tree diameter and tree density on carbon storage estimates. Four major cover types (bare ground, fine vegetation, coarse vegetation, and impervious surfaces) were determined from 348 plots (0.039 ha each) randomly stratified according to land-use. Hand-delineation was better than supervised classification at predicting ground-based measurements of cover type and UFORE model-predicted carbon storage. Most error in supervised classification resulted from shadow, which was interpreted as unknown cover type. Neither tree diameter or tree density per plot significantly affected the relationship between carbon storage and canopy cover. The efficiency of remote sensing rather than in situ data collection allows urban forest managers the ability to quickly assess a city and plan accordingly while also preserving their often-limited budget.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Remote sensing of volcanic plumes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been used to quantify SO2 emissions from passively degassing volcanoes. This dissertation explores ASTER’s capability to detect SO2 with satellite validation, enhancement techniques and extensive processing of images at a variety of volcanoes. ASTER is compared to the Mini UV Spectrometer (MUSe), a ground based instrument, to determine if reasonable SO2 fluxes can be quantified from a plume emitted from Lascar, Chile. The two sensors were in good agreement with ASTER proving to be a reliable detector of SO2. ASTER illustrated the advantages of imaging a plume in 2D, with better temporal resolution than the MUSe. SO2 plumes in ASTER imagery are not always discernible in the raw TIR data. Principal Component Analysis (PCA) and Decorrelation Stretch (DCS) enhancement techniques were compared to determine how well they highlight a variety of volcanic plumes. DCS produced a consistent output and the composition of the plumes was easy to identify from explosive eruptions. As the plumes became smaller and lower in altitude they became harder to distinguish using DCS. PCA proved to be better at identifying smaller low altitude plumes. ASTER was used to investigate SO2 emissions at Lascar, Chile. Activity at Lascar has been characterized by cyclic behavior and persistent degassing (Matthews et al. 1997). Previous studies at Lascar have primarily focused on changes in thermal infrared anomalies, neglecting gas emissions. Using the SO2 data along with changes in thermal anomalies and visual observations it is evident that Lascar is at the end an eruptive cycle that began in 1993. Declining gas emissions and crater temperatures suggest that the conduit is sealing. ASTER and the Ozone Monitoring Instrument (OMI) were used to determine the annual contribution of SO2 to the troposphere from the Central and South American volcanic arcs between 2000 and 2011. Fluxes of 3.4 Tg/a for Central America and 3.7 Tg/a for South America were calculated. The detection limits of ASTER were explored. The results a proved to be interesting, with plumes from many of the high emitting volcanoes, such as Villarrica, Chile, not being detected by ASTER.

TECHNOLOGICAL ADVANCEMENT IN REMOTE SENSING AND INNOVATIVE APPLICATION TO VOLCANOLOGY

The integration of remote monitoring techniques at different scales is of crucial importance for monitoring of volcanoes and assessment of the associated hazard. In this optic, technological advancement and collaboration between research groups also play a key role. Vhub is a community cyberinfrastructure platform designed for collaboration in volcanology research. Within the Vhub framework, this dissertation focuses on two research themes, both representing novel applications of remotely sensed data in volcanology: advancement in the acquisition of topographic data via active techniques and application of passive multi-spectral satellite data to monitoring of vegetated volcanoes. Measuring surface deformation is a critical issue in analogue modelling of Earth science phenomena. I present a novel application of the Microsoft Kinect sensor to measurement of vertical and horizontal displacements in analogue models. Specifically, I quantified vertical displacement in a scaled analogue model of Nisyros volcano, Greece, simulating magmatic deflation and inflation and related surface deformation, and included the horizontal component to reconstruct 3D models of pit crater formation. The detection of active faults around volcanoes is of importance for seismic and volcanic hazard assessment, but not a simple task to be achieved using analogue models. I present new evidence of neotectonic deformation along a north-south trending fault from the Mt Shasta debris avalanche deposit (DAD), northern California. The fault was identified on an airborne LiDAR campaign of part of the region interested by the DAD and then confirmed in the field. High resolution LiDAR can be utilized also for geomorphological assessment of DADs, and I describe a size-distance analysis to document geomorphological aspects of hummock in the Shasta DAD. Relating the remote observations of volcanic passive degassing to conditions and impacts on the ground provides an increased understanding of volcanic degassing and how satellite-based monitoring can be used to inform hazard management strategies in nearreal time. Combining a variety of satellite-based spectral time series I aim to perform the first space-based assessment of the impacts of sulfur dioxide emissions from Turrialba volcano, Costa Rica, on vegetation in the surrounding environment, and establish whether vegetation indices could be used more broadly to detect volcanic unrest.

GROUND-BASED AND SATELLITE REMOTE SENSING OF PAROXYSMAL ERUPTIONS AT ETNA VOLCANO, 2011-2012.

Mt Etna's activity has increased during the last decade with a tendency towards more explosive eruptions that produce paroxysmal lava fountains. From January 2011 to April 2012, 25 lava fountaining episodes took place at Etna's New South-East Crater (NSEC). Improved understanding of the mechanism driving these explosive basaltic eruptions is needed to reduce volcanic hazards. This type of activity produces high sulfur dioxide (SO2) emissions, associated with lava flows and ash fall-out, but to date the SO2 emissions associated with Etna's lava fountains have been poorly constrained. The Ultraviolet (UV) Ozone Monitoring Instrument (OMI) on NASA's Aura satellite and the Atmospheric Infrared Sounder (AIRS) on Aqua were used to measure the SO2 loadings. Ground-based data from the Observatoire de Physique du Globe de Clermont-Ferrand (OPGC) L-band Doppler radar, VOLDORAD 2B, used in collaboration with the Italian National Institute of Geophysics and Volcanology in Catania (INGV-CT), also detected the associated ash plumes, giving precise timing and duration for the lava fountains. This study resulted in the first detailed analysis of the OMI and AIRS SO2 data for Etna's lava fountains during the 2011-2012 eruptive cycle. The HYSPLIT trajectory model is used to constrain the altitude of the observed SO2 clouds, and results show that the SO2 emission usually coincided with the lava fountain peak intensity as detected by VOLDORAD. The UV OMI and IR AIRS SO2 retrievals permit quantification of the SO2 loss rate in the volcanic SO2 clouds, many of which were tracked for several days after emission. A first attempt to quantitatively validate AIRS SO2 retrievals with OMI data revealed a good correlation for high altitude SO2 clouds. Using estimates of the emitted SO2 at the time each paroxysm, we observe a correlation with the inter-paroxysm repose time. We therefore suggest that our data set supports the collapsing foam (CF) model [1] as driving mechanism for the paroxysmal events at the NSEC. Using VOLDORAD-based estimates of the erupted magma mass, we observe a large excess of SO2 in the eruption clouds. Satellite measurements indicate that SO2 emissions from Etnean lava fountains can reach the lower stratosphere and hence could pose a hazard to aviation. [1] Parfitt E.A (2004). A discussion of the mechanisms of explosive basaltic eruptions. J. Volcanol. Geotherm. Res. 134, 77-107.

Urban expansion and its impact on urban agriculture – remote sensing based change analysis of Kizinga and Mzinga Valley – Dar Es Salaam, Tanzania

Urban agriculture is a phenomenon that can be observed world-wide, particularly in cities of devel-oping countries. It is contributing significantly to food security and food safety and has sustained livelihood of the urban and peri-urban low income dwellers in developing countries for many years. Population increase due to rural-urban migration and natural, coupled with formal as well as infor-mal urbanization are competing with urban farming for available space and scarce water resources. A multitemporal multisensoral urban change analysis over the period of 25 years (1982-2007) was performed in order to measure and visualize the urban expansion along the Kizinga and Mzinga valley in the South of Dar es Salaam. Airphotos and VHR satellite data were analyzed by using a combination of a composition of anisotropic textural measures and spectral information. The study revealed that unplanned built-up area is expanding continuously and vegetation covers and agricultural lands decline at a fast rate. The validation showed that the overall classification accuracy varied depending on the database. The extracted built-up areas were used for visual in-terpretation mapping purposes and served as information source for another research project. The maps visualize an urban congestion and expansion of nearly 18% of the total analyzed area that had taken place in the Kizinga valley between 1982 and 2007. The same development can be ob-served in the less developed and more remote Mzinga valley between 1981 and 2002. Both areas underwent fast changes where land prices still tend to go up and an influx of people both from rural and urban areas continuously increase density with the consequence of increasing multiple land use interests.

Hygroscopic growth of atmospheric aerosol particles based on active remote sensing and radiosounding measurements: selected cases in southeastern Spain

A new methodology based on combining active and passive remote sensing and simultaneous and collocated radiosounding data to study the aerosol hygroscopic growth effects on the particle optical and microphysical properties is presented. The identification of hygroscopic growth situations combines the analysis of multispectral aerosol particle backscatter coefficient and particle linear depolarization ratio with thermodynamic profiling of the atmospheric column. We analyzed the hygroscopic growth effects on aerosol properties, namely the aerosol particle backscatter coefficient and the volume concentration profiles, using data gathered at Granada EARLINET station. Two study cases, corresponding to different aerosol loads and different aerosol types, are used for illustrating the potential of this methodology. Values of the aerosol particle backscatter coefficient enhancement factors range from 2.1 ± 0.8 to 3.9 ± 1.5, in the ranges of relative humidity 60–90 and 40–83%, being similar to those previously reported in the literature. Differences in the enhancement factor are directly linked to the composition of the atmospheric aerosol. The largest value of the aerosol particle backscatter coefficient enhancement factor corresponds to the presence of sulphate and marine particles that are more affected by hygroscopic growth. On the contrary, the lowest value of the enhancement factor corresponds to an aerosol mixture containing sulphates and slight traces of mineral dust. The Hänel parameterization is applied to these case studies, obtaining results within the range of values reported in previous studies, with values of the γ exponent of 0.56 ± 0.01 (for anthropogenic particles slightly influenced by mineral dust) and 1.07 ± 0.01 (for the situation dominated by anthropogenic particles), showing the convenience of this remote sensing approach for the study of hygroscopic effects of the atmospheric aerosol under ambient unperturbed conditions. For the first time, the retrieval of the volume concentration profiles for these cases using the Lidar Radiometer Inversion Code (LIRIC) allows us to analyze the aerosol hygroscopic growth effects on aerosol volume concentration, observing a stronger increase of the fine mode volume concentration with increasing relative humidity.

Evolution of 1996-1999 La Niña and El Niño Conditions Off the Western Coast of South America: A Remote Sensing Perspective

We present the evolution of oceanographic conditions off the western coast of South America between 1996 and 1999, including the cold periods of 1996 and 1998-1999 and the 1997-1998 El Niño, using satellite observations of sea level, winds, sea surface temperature (SST), and chlorophyll concentration. Following a period of cold SST and low sea levels in 1996, both were anomalously high between March 1997 and May 1998. The anomalies were greatest between 5 degrees S and 15 degrees S, although they extended beyond 40 degrees S. Two distinct peaks in sea level and SST occurred in June-July 1997 and December 1997 to January 1998, separated by a relaxation period (August-November) of weaker anomalies. Satellite winds were upwelling favorable throughout the time period for most of the region and in fact increased between November 1997 and March 1998 between 5 degrees S and 25 degrees S. Satellite-derived chlorophyll concentrations are available for November 1996 to June 1997 (Ocean Color and Temperature Sensor (OCTS)) and then from October 1997 to present (Sea-viewing Wide Field-of-view Sensor (SeaWiFS)). Near-surface chlorophyll concentrations fell from May to June 1997 and from December 1997 to March 1998. The decrease was more pronounced in northern Chile than off the coast of Peru or central Chile and was stronger for larger cross-shelf averaging bins since nearshore concentrations remained relatively high.

Aerial Remote Sensing in Agriculture: A Practical Approach to Area Coverage and Path Planning for Fleets of Mini Aerial Robots

In this paper, a system that allows applying precision agriculture techniques is described. The application is based on the deployment of a team of unmanned aerial vehicles that are able to take georeferenced pictures in order to create a full map by applying mosaicking procedures for postprocessing. The main contribution of this work is practical experimentation with an integrated tool. Contributions in different fields are also reported. Among them is a new one-phase automatic task partitioning manager, which is based on negotiation among the aerial vehicles, considering their state and capabilities. Once the individual tasks are assigned, an optimal path planning algorithm is in charge of determining the best path for each vehicle to follow. Also, a robust flight control based on the use of a control law that improves the maneuverability of the quadrotors has been designed. A set of field tests was performed in order to analyze all the capabilities of the system, from task negotiations to final performance. These experiments also allowed testing control robustness under different weather conditions.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes.

Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.