Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina

This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). The results obtained indicate that object-based analyses clearly outperform pixel-based classifications, with an increase in accuracy of up to 35%. The second stage focused on advanced object-based derived variables with topographic ancillary data classifications. The combinations of variables were tested in order to obtain the most accurate map of landforms based on the most successful classifiers identified in the previous stage (ML, SVM and DT). The results indicate that DT is the most accurate classifier, exhibiting the highest overall accuracies with values greater than 72% in both the winter and summer images. Future work could combine both, the most appropriate methodologies and combinations of variables obtained in this study, with physico-chemical variables sampled to improve the classification of landforms and even of types of soil.EEA Santiago del EsteroFil: Castillejo González, Isabel Luisa. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; EspañaFil: Angueira, Maria Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; ArgentinaFil: García Ferrer, Alfonso. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; EspañaFil: Sánchez de la Orden, Manuel. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; Españ

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Development of a Multimode Instrument for Remote Measurements of Unsaturated Soil Properties

The hydromechanical behavior of soil is governed by parameters that include the moisture content, soil matric potential, texture, and the mineralogical composition of the soil. Remote characterization of these and other key properties of the soil offers advantages over conventional in situ or laboratory-based measurements: information may be acquired rapidly over large, or inaccessible areas; samples do not need to be collected; and the measurements are non-destructive. A field-deployable, ground-based remote sensor, designated the Soil Observation Laser Absorption Spectrometer (SOLAS), was developed to infer parameters of bare soils and other natural surfaces over intermediate (100 m) and long (1,000 m) ranges. The SOLAS methodology combines hyperspectral remote sensing with differential absorption and laser ranging measurements. A transmitter propagates coherent, near-infrared light at on-line (823.20 nm) and off-line (847.00 nm) wavelengths. Backscattered light is received through a 203-mm diameter telescope aperture and is divided into two channels to enable simultaneous measurements of spectral reflectance, differential absorption, and range to the target. The spectral reflectance is measured on 2151 continuous bands that range from visible (380 nm) to shortwave infrared (2500 nm) wavelengths. A pair of photodetectors receive the laser backscatter in the 820–850 nm range. Atmospheric water vapor is inferred using a differential absorption technique in conjunction with an avalanche photodetector, while range to the target is based on a frequency-modulated, self-chirped, homodyne detection scheme. The design, fabrication, and testing of the SOLAS is described herein. The receiver was optimized for the desired backscatter measurements and assessed through a series of trials that were conducted in both indoor and outdoor settings. Spectral reflectance measurements collected at proximal range compared well with measurements collected at intermediate ranges, demonstrating the utility of the receiver. Additionally, the noise characteristics of the spectral measurements were determined across the full range of the detected wavelengths. Continued development of the SOLAS instrument will enable range-resolved and water vapor-corrected reflectance measurements over longer ranges. Anticipated applications for the SOLAS technology include rapid monitoring of earth construction projects, geohazard assessment, or ground-thruthing for current and future satellite-based multi- and hyperspectral data

Mapping Succession in Non-Forest Habitats by Means of Remote Sensing: Is the Data Acquisition Time Critical for Species Discrimination?

The process of secondary succession is one of themost significant threats to non-forest (natural and semi-natural open) Natura 2000 habitats in Poland; shrub and tree encroachment taking place on abandoned, low productive agricultural areas, historically used as pastures or meadows, leads to changes to the composition of species and biodiversity loss, and results in landscape transformations. There is a perceived need to create amethodology for themonitoring of vegetation succession by airborne remote sensing, both from quantitative (area, volume) and qualitative (plant species) perspectives. This is likely to become a very important issue for the effective protection of natural and semi-natural habitats and to advance conservation planning. A key variable to be established when implementing a qualitative approach is the remote sensing data acquisition date, which determines the developmental stage of trees and shrubs forming the succession process. It is essential to choose the optimal date on which the spectral and geometrical characteristics of the species are as different from each other as possible. As part of the research presented here, we compare classifications based on remote sensing data acquired during three different parts of the growing season (spring, summer and autumn) for five study areas. The remote sensing data used include high-resolution hyperspectral imagery and LiDAR (Light Detection and Ranging) data acquired simultaneously from a common aerial platform. Classifications are done using the random forest algorithm, and the set of features to be classified is determined by a recursive feature elimination procedure. The results show that the time of remote sensing data acquisition influences the possibility of differentiating succession species. This was demonstrated by significant differences in the spatial extent of species, which ranged from 33.2% to 56.2% when comparing pairs of maps, and differences in classification accuracies, which when expressed in values of Cohen’s Kappa reached ~0.2. For most of the analysed species, the spring and autumn dates turned out to be slightly more favourable than the summer one. However, the final recommendation for the data acquisition time should take into consideration th

Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

Environmental disturbances in semi-arid ecosystems have highlighted the need to monitor current and future vegetation conditions across the landscape. Imaging spectroscopy provide the necessary information to derive vegetation characteristics at high-spatial resolutions across large geographic areas. The work of this thesis is divided into two sections focused on using imaging spectroscopy to estimate and classify vegetation cover, and approximate aboveground biomass in a semi-arid ecosystem. The first half of this thesis assesses the ability of imaging spectroscopy to derive vegetation classes and their respective cover across large environmental gradients and ecotones often associated with semi-arid ecosystems. Optimal endmember selection and endmember bundling are coupled with classification and spectral unmixing techniques to derive vegetation species and abundances across Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho at high spatial resolution (1 m). Results validated using field data indicated classification of aspen, Douglas fir, juniper, and riparian classes had an overall accuracy of 57.9% and a kappa coefficient of 0.43. Plant functional type classification, consisting of deciduous and evergreen trees, had an overall accuracy of 84.4% and a kappa coefficient of 0.68. Shrub, grass, and soil cover were predicted with an overall accuracy of 67.4% and kappa coefficient of 0.53. I conclude that imaging spectroscopy can be used to map vegetation communities in semi-arid ecosystems across large environmental gradients at high-spatial resolution and with high accuracy. The second half of this thesis focuses on monitoring the changes of aboveground biomass (AGB) from the 2015 Soda Fire, which burned portions of southwest Idaho and southeastern Oregon. Classifications derived in the first study are used to estimate AGB loss within a portion of RCEW, and these estimates are used to compare to gross estimates made over the full extent of the Soda Fire. I found that there was an AGB loss of 174M kg within RCEW and approximately 1.8B kg lost over the full extent of the Soda Fire. Additionally, a post-fire analysis was performed to provide insight into the amount of AGB that returned to both RCEW and the full extent of the Soda Fire. An estimated 2,100 – 208,000 kg of AGB had returned to the burned portion of RCEW one-year post fire, and approximately 3.2M kg of AGB had returned over the full extent of the Soda Fire. These AGB loss and re-growth estimates can be used by researchers and practitioners to monitor carbon flux across the Soda Fire and as baseline data for wildfires in semi-arid ecosystems

Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen