Multi-scale analysis of urban wetland changes using satellite remote sensing techniques

Title from PDF of title page, viewed on January 10, 2012Dissertation advisor: Wei JiVitaIncludes bibliographic references (p.146-157)Thesis (Ph.D.)--Dept of Geosciences and School of Computing and Engineering. University of Missouri--Kansas City, 2011This study investigates urban wetland-cover changes in the Kansas City metropolitan area with analyses at various spatial and temporal scales. Not many studies fully addressed multi-scale urban wetland-cover dynamics in both the temporal and spatial dimension. The objective was to understand how major driving factors - human disturbances and climate variation - impacted urban wetlands as determined by the scale effects of observing land-cover changes. To address this objective, multi-year and multi-season SPOT satellite images were acquired and digitally classified to generate wetland and related land-cover data over various temporal ranges. To detect long term changes of urban wetland, the study examined the landscape changes between 1992 and 2008. Furthermore, for a short term analysis over a period between 2008 and 2010, the study analyzed seasonal land-cover variation among the autumn, spring, and summer. These multi-temporal land-cover data were analyzed at various spatial scales - the metropolitan region, watersheds, sub-watersheds, specific wetland areas, and particular urban development zones. The results show that over the 16-year period, both wetland and impervious surfaces gained in area at the metropolitan level. However, the wetland change patterns were varied at other spatial scales of analysis, which were related to the dominant site-specific development activities. Further, the wetland change patterns differed if large surface water bodies (> 8ha) were excluded from the class of wetlands. The study also revealed that the seasonal change patterns of urban wetlands were likely correlated with short term precipitation conditions; but this effect may be varied depending on sampling area sizes. The study suggests that the effects of spatial and temporal scales should be considered in remote sensing detection of urban wetlands as they influence the interpretation of remotely sensed land-cover changes and correlation of driving factors. In conclusion, understanding the complex human-climate coupling factors affecting urban wetland-cover requires a multi-scale and multi-faceted analysis.Introduction -- Literature review -- Methodology -- Remote sensing analysis -- Quantifying land cover data using geospatial modelling -- Discussion and conclusion -- Appendix A. Lake inflows: historical and actual -- Appendix B. Source code for the geo-processing model -- Appendix C. Precipitation received before a satellite imaging dates -- Surface water cover analysis at Kansas City Metropolitan, watershed and sub-watershed scale

From imagery to ecology: Leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics

https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.24Published versio

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode

Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study

A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin