Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Changement de masse des glaciers à l’échelle mondiale par analyse spatiotemporelle de modèles numériques de terrain

Les glaciers de la planète rétrécissent rapidement, et produisent des impacts qui s'étendent de la hausse du niveau de la mer et la modification des risques cryosphériques jusqu'au changement de disponibilité en eau douce. Malgré des avancées significatives durant l'ère satellitaire, l'observation des changements de masse des glaciers est encore entravée par une couverture partielle des estimations de télédétection, et par une faible contrainte sur les erreurs des évaluations associées. Dans cette thèse, nous présentons une estimation mondiale et résolue des changements de masse des glaciers basée sur l'analyse spatio-temporelle de modèles numériques de terrain. Nous développons d'abord des méthodes de statistiques spatio-temporelles pour évaluer l'exactitude et la précision des modèles numériques de terrain, et pour estimer des séries temporelles de l'altitude de surface des glaciers. En particulier, nous introduisons un cadre spatial non stationnaire pour estimer et propager des corrélations spatiales multi-échelles dans les incertitudes d'estimations géospatiales. Nous générons ensuite des modèles numériques de terrain massivement à partir de deux décennies d'archives d'images optiques stéréo couvrant les glaciers du monde entier. À partir de ceux-ci, nous estimons des séries temporelles d'altitude de surface pour tous les glaciers de la Terre à une résolution de 100,m sur la période 2000--2019. En intégrant ces séries temporelles en changements de volume et de masse, nous révélons une accélération significative de la perte de masse des glaciers à l'échelle mondiale, ainsi que des réponses régionalement distinctes qui reflètent des changements décennaux de conditions climatiques. En utilisant une grande quantité de données indépendantes et de haute précision, nous démontrons la validité de notre analyse pour produire des incertitudes robustes et cohérentes à différentes échelles de la structure spatio-temporelle de nos estimations. Nous espérons que nos méthodes favorisent des analyses spatio-temporelles robustes, en particulier pour identifier les sources de biais et d'incertitudes dans les études géospatiales. En outre, nous nous attendons à ce que nos estimations permettent de mieux comprendre les facteurs qui régissent le changement des glaciers et d'étendre nos capacités de prévision de ces changements à toutes échelles. Ces prédictions sont nécessaires à la conception de politiques adaptatives sur l'atténuation des impacts de la cryosphère dans le contexte du changement climatique.The world's glaciers are shrinking rapidly, with impacts ranging from global sea-level rise and changes in freshwater availability to the alteration of cryospheric hazards. Despite significant advances during the satellite era, the monitoring of the mass changes of glaciers is still hampered by a fragmented coverage of remote sensing estimations and a poor constraint of the errors in related assessments. In this thesis, we present a globally complete and resolved estimate of glacier mass changes by spatiotemporal analysis of digital elevation models. We first develop methods based on spatiotemporal statistics to assess the accuracy and precision of digital elevation models, and to estimate time series of glacier surface elevation. In particular, we introduce a non-stationary spatial framework to estimate and propagate multi-scale spatial correlations in uncertainties of geospatial estimates. We then massively generate digital elevation models from two decades of stereo optical archives covering glaciers worldwide. From those, we estimate time series of surface elevation for all of Earth's glaciers at a resolution of 100,m during 2000--2019. Integrating these time series into volume and mass changes, we identify a significant acceleration of global glacier mass loss, as well as regionally-contrasted responses that mirror decadal changes in climatic conditions. Using a large amount of independent, high-precision data, we demonstrate the validity of our analysis to yield robust and consistent uncertainties at different scales of the spatiotemporal structure of our estimates. We expect our methods to foster robust spatiotemporal analyses, in particular to identify sources of biases and uncertainties in geospatial assessments. Furthermore, we anticipate our estimates to advance the understanding of the drivers that govern glacier change, and to extend our capabilities of predicting these changes at all scales. Such predictions are critically needed to design adaptive policies on the mitigation of cryospheric impacts in the context of climate change

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

The global tree carrying capacity (keynote)

editorial reviewe

A multi-scale approach to assessing the spatio-temporal variability of seasonal snow in the Clutha Catchment, New Zealand

Seasonal snow is an important, but under-observed component of New Zealand's hydrological cycle. Measurement and characterisation of seasonal snow is complicated because it varies over a range of spatial and temporal scales. This makes spatially distributed in situ observations difficult to acquire, and limits efforts to scale point-based observations up to larger areas. Sparse observations of seasonal snow lead to reduced understanding of seasonal snow processes, and subsequent uncertainty in efforts to model seasonal snow. This thesis addresses these issues within the Clutha Catchment, New Zealand's largest, by leveraging remote sensing and geospatial approaches to map and characterise seasonal snow both regionally, and at very high spatial resolution over a small alpine basin. A daily snow covered area (SCA) time series and regional scale snow cover climatology is derived from MODIS imagery for the period 20002016. Metrics including annual snow cover duration (SCD) anomaly and daily SCA and snowline elevation (SLE) were derived and assessed for temporal trends. On average, SCA peaks in late June (~30 % of the catchment area), with 10 % of the catchment area sustaining snow cover for > 120 d yr -1. A persistent mid-winter reduction in SCA is attributed to the prevalence of winter blocking anticyclones in the New Zealand region. No significant decrease in SCD occurred over the period 20002016, but substantial spatial and temporal variability was observed. Raster principal component analysis (rPCA) identified distinct modes of spatial variability within the time series. Spatio-temporal variability extends beyond that associated with topographic controls, which can result in out of phase snow cover conditions across the catchment. Specific spatial modes of SCD are associated with anomalous airflow from the NE, E and SE. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. In order to resolve sub-MODIS scale processes, the potential of remotely piloted aircraft system (RPAS) photogrammetry to map snow depth was evaluated within an alpine catchment of the Pisa Range. Differencing between snow-covered and snow-free digital surface models (DSMs) acquired during 2016 provided high resolution snow depth maps. The accuracy of snow depth maps was thoroughly assessed with in situ snow probe measurements, and by analysing residuals for snow-free areas between DSMs. This accuracy assessment demonstrated repeatability and revealed substantial departures of errors from a normal distribution. This reflects the influence of DSM co-registration and terrain characteristics on vertical uncertainty. Error propagation provided lower uncertainties for snow depth (±0.08 m, 90 % c.l.) than the characterization of uncertainties on snow-free areas (±0.14 m). Comparisons between RPAS and in situ snow depth measurements confirm this level of performance. Semivariogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine-scale spatial variability. Following the successful evaluation of RPAS photogrammetry for mapping snow depth, further snow depth maps were acquired for 2017. A total of six snow depth maps that resolved fine scale spatial variability in snow distribution facilitated the assessment of topographic controls on snow depth and snow water equivalent (SWE) distribution. Topographic controls were assessed via regression tree analysis between snow depth and terrain indices, including the kernel density of tussock vegetation (KDtussock), elevation (ELEV), the topographic position index (TPI), a Shade index (Shade), and Sx (maximum upwind slope). Despite substantial differences in both total snow volume and spatial distribution, the range of spatial-autocorrelation for snow depth was comparable for both winters at 20 – 30 m. Regression tree modelling reproduced some of the observed spatial structure, and demonstrated temporal variability in the relative importance of controlling parameters. The impact of varying wind regimes on the spatial distribution of snow was highlighted. These findings illustrate the complexity of atmospheric controls on SCD within the Clutha Catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological catchment (∼0.4 km2) at very high resolution. Snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. This thesis demonstrates the utility of mapping snow at differing spatial scales for improved understanding of seasonal snow processes and highlights the need to robustly capture dynamic processes in spatial snow models