Validation of global forest change detection databases Abstract The main aim of the thesis is to validate selected databases of changes in forest areas based on the analysis of satellite imagery time series in the Czech Republic. For this purpose we are using databases of M. C. Hansen and P. V. Potapov which are mapping the evolution of forest areas internationally. For the purposes of validation, we have proposed a methodology primarily based on historical ortophotographs from 2000-2012, the same time period which is documented in the validated databases. The results obtained were statistically processed, allowing to assess the accuracy of validated databases. At the end of the thesis, we are discussing the causes of identified inaccuracies and presented with recommendations for future improvements of detection of changes in forest areas. Keywords: validation, forest, land cover, change detection, Hansen, Potapov

Northwestern Arctic Canada is one of the most rapidly warming regions in the Arctic (Serreze et al., 2009). Retrogressive thaw slumps (RTS) are one of the most dramatic thermokarst features in permafrost terrain (Kokelj et al., 2013). Many studies have focused on describing the distribution of thermokarst landscapes (i.e., Olefeldt et al., 2016), as well as change in thermokarst terrain over the historical record (i.e., Kokelj and Jorgenson, 2013). However, improved high temporal and spatial resolution monitoring of thaw slump activity is required to enhance our understanding of factors governing their growth. Recent advances in aerial and ground-based Structure from Motion (SfM), a photogrammetry application, allow for temporal and spatial high-resolution characterization of landscape changes. This thesis explores two methods in SfM photogrammetry: 1) aerial imaging using an unmanned aerial vehicle (UAV) and 2) ground-based imaging using stationary multi-camera time-lapse installations, to derive high-resolution temporal and spatial data for change detection. A trend in mean elevation change was produced, and agrees with the RTS behaviour over the study period, which supports the viability of the proposed capture method. The lack of congruency in data range suggests need for further development in terms of analyses and differencing algorithms employed. The proposed method may be feasible for employment in other fields of science in which high temporal resolution change detection is desired. This proof of concept study was conducted at a small slump on the Peel Plateau, NWT, Canada, and aims to enhance understanding of the development and perpetuation of thaw slumps, to better anticipate landscape and ecosystem responses to future climate change.

Recent advances in computing technology allow for collecting vast amount of data that arrive continuously in the form of streams. Mining data streams is challenged by the speed and volume of the arriving data. Furthermore, the underlying distribution of the data changes over the time in unpredicted scenarios. To reduce the computational cost, data streams are often studied in forms of condensed representation, e.g., Probability Density Function (PDF). This thesis aims at developing an online density estimator that builds a model called KDE-Track for characterizing the dynamic density of the data streams. KDE-Track estimates the PDF of the stream at a set of resampling points and uses interpolation to estimate the density at any given point. To reduce the interpolation error and computational complexity, we introduce adaptive resampling where more/less resampling points are used in high/low curved regions of the PDF. The PDF values at the resampling points are updated online to provide up-to-date model of the data stream. Comparing with other existing online density estimators, KDE-Track is often more accurate (as reflected by smaller error values) and more computationally efficient (as reflected by shorter running time). The anytime available PDF estimated by KDE-Track can be applied for visualizing the dynamic density of data streams, outlier detection and change detection in data streams. In this thesis work, the first application is to visualize the taxi traffic volume in New York city. Utilizing KDE-Track allows for visualizing and monitoring the traffic flow on real time without extra overhead and provides insight analysis of the pick up demand that can be utilized by service providers to improve service availability. The second application is to detect outliers in data streams from sensor networks based on the estimated PDF. The method detects outliers accurately and outperforms baseline methods designed for detecting and cleaning outliers in sensor data. The third application is to detect changes in data streams. We propose a framework based on Principal Component Analysis (PCA) that reduces the problem of detecting changes in multidimensional data into the problem of detecting changes in the projected data on the principal components. We provide a theoretical analysis, which is support by experimental results to show that utilizing PCA reflects different types of changes in data streams on the projected data over one or more principal components. Our framework is accurate in detecting changes with low computational costs and scales well for high dimensional data.

This thesis concerns with change detection problematics in digital images captured under indoor conditions with an ordinary integrated camera in two consecutive moments. All challenges that accompany this problem will be discussed, starting with preprocessing and arriving to evaluation of the results. Currently used methods from this field are described and compared with each other such as differencing and LCP (Local Correlation Peak). A novel method, based on LTP descriptors, effectively solving this problem is proposed in this work. The proposed method is then tested on real data. The results of this tests are discussed subsequently. Besides the change detection method a method for parallax error minimization is proposed here.

This thesis presents a case study on the application of deep learning methods for the dense prediction of land cover types in oblique ground-based photography. While deep learning approaches are widely used in land cover classification of remote-sensing data (i.e., aerial and satellite orthoimagery) for change detection analysis, dense classification of oblique landscape imagery used in repeat photography remains undeveloped. A performance evaluation was carried out to test two state-of the-art architectures, U-net and Deeplabv3+, as well as a fully-connected conditional random fields model used to boost segmentation accuracy. The evaluation focuses on the use of a novel threshold-based data augmentation technique, and three multi-loss functions selected to mitigate class imbalance and input noise. The dataset used for this study was sampled from the Mountain Legacy Project (MLP) collection, comprised of high-resolution historic (grayscale) survey photographs of Canada’s Western mountains captured from the 1880s through the 1950s and their corresponding modern (colour) repeat images. Land cover segmentations manually created by MLP researchers were used as ground truth labels. Experimental results showed top overall F1 scores of 0.841 for historic models, and 0.909 for repeat models. Data augmentation showed modest improvements to overall accuracy (+3.0% historic / +1.0% repeat), but much larger gains for under-represented classes. / Graduate

Detecting changes in land cover through time using remotely sensed imagery is a powerful application that has seen increased use as imagery has become more widely available and inexpensive. Before a time series of remotely sensed imagery can be used for change detection, images must first be standardized for effects outside of real surface change. This thesis established a validation protocol to evaluate the effectiveness of an automated technique for normalizing temporally separate but spatially coincident imagery. Using the concept of pseudo-invariant features between master-slave image pairs, spatially coincident dark and bright points are identified from images and a regression equation is calculated to normalize slave images to a master. I used two sets of imagery to test the performance of the standardization process, a spatially coincident, but temporally variable time series, and spatially and temporally variable images. I tested the underlying statistical assumptions of this approach, and performed simple image subtraction to validate the reduction of master-slave differences using invariant locations. In addition I tested the possibility of reducing between-sensor differences by applying simple linear regression to comparable bands of MSS and TM sensors. Image subtraction showed decreases in master-slave differences as a result of the standardization process, and the process behaved appropriately when there should be no difference between master and slave images (adjacent, but temporally identical imagery). I also found that comparable bands between MSS and TM sensors are similar enough that linear regression may not significantly reduce between-sensor differences.

The Mississippi Institute for Forest Inventory (MIFI) is the only cost-effective large-scale forest inventory system in the United States with sufficient precision for producing reliable volume/weight/biomass estimates for small working circle areas (procurement areas). When forest industry is recruited to Mississippi, proposed working circles may overlap existing boundaries of bordering states leaving a gap of inventory information, and a remote sensing-based system for augmenting missing ground inventory data is desirable. The feasibility of obtaining acceptable cubic foot volume estimates from a Landsat-derived volume estimation model (Wilkinson 2011) was assessed by: 1) an initial study to temporally validate Landsat-derived cubic foot volume outside bark to a pulpwood top estimates in comparison with MIFI ground truth inventory plot estimates at two separate time periods, and 2) re-developing a regression model based on remotely sensed imagery in combination with available MIFI plot data. Initial results failed to confirm the relationships shown in past research between radiance values and volume estimation. The complete lack of influence of radiance values in the model led to a re-assessment of volume estimation schemes. Data outlier trimming manipulation was discovered to lead to false relationships with radiance values reported in past research. Two revised volume estimation models using age, average stand height, and trees per-acre and age and height alone as independent variables were found sufficient to explain variation of volume across the image. These results were used to develop a procedure for other remote sensing technologies that could produce data with sufficient precision for volume estimation where inventory data are sparse or non-existent.

The Mississippi and Alabama barrier islands are sensitive landforms that are affected by hurricanes, longshore currents, and available sediment, yet these effects are difficult to quantify with traditional ground-based surveying. In this study, Landsat satellite imagery was used to evaluate changes in barrier island area and centroid position from 1990 and 2005. When hurricanes are infrequent (1999?2003), barrier islands generally increased in total area and showed only moderate repositioning of their centroid locations. However, when hurricanes were frequent (1994?1999 and 2004?2005), barrier islands showed substantial decreases in area and dramatic repositioning of their island centroid locations. This was especially true following Hurricane Katrina (2005). From 1990 to 2005, the general movement of barrier islands was westerly and most islands experienced an overall reduction in area (-18%). The results of this research are similar to findings reported in the literature and illustrate the suitability of using Landsat imagery to study geomorphic changes.

No description available.

Deep-Learning (DL) methods have been widely used for Remote Sensing (RS) applications in the last few years, and they allow improving the analysis of the temporal information in bi-temporal and multi-temporal RS images. DL methods use RS data to classify geographical areas or find changes occurring over time. DL methods exploit multi-sensor or multi-temporal data to retrieve results more accurately than single-source or single-date processing. However, the State-of-the-Art DL methods exploit the heterogeneous information provided by these data by focusing the analysis either on the spatial information of multi-sensor multi-resolution images using multi-scale approaches or on the time component of the image time series. Most of the DL RS methods are supervised, so they require a large number of labeled data that is challenging to gather. Nowadays, we have access to many unlabeled RS data, so the creation of long image time series is feasible. However, supervised methods require labeled data that are expensive to gather over image time series. Hence multi-temporal RS methods usually follow unsupervised approaches. In this thesis, we propose DL methodologies that handle these open issues. We propose unsupervised DL methods that exploit multi-resolution deep feature maps derived by a Convolutional Autoencoder (CAE). These DL models automatically learn spatial features from the input during the training phase without any labeled data. We then exploit the high temporal resolution of image time series with the high spatial information of Very-High-Resolution (VHR) images to perform a multi-temporal and multi-scale analysis of the scene. We merge the information provided by the geometrical details of VHR images with the temporal information of the image time series to improve the RS application tasks. We tested the proposed methods to detect changes over bi-temporal RS images acquired by various sensors, such as Landsat-5, Landsat-8, and Sentinel-2, representing burned and deforested areas, and kinds of pasture impurities using VHR orthophotos and Sentinel-2 image time series. The results proved the effectiveness of the proposed methods.