Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

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Multi-scale analysis of landslide occurrence and evolution using optical and radar time series

10.5194/egusphere-egu2020-20702 ◽

2020 ◽

Author(s):

Sigrid Roessner ◽

Robert Behling ◽

Mahdi Motagh ◽

Hans Ulrich-wetzel

Keyword(s):

Remote Sensing ◽

Time Series ◽

High Resolution ◽

Intense Rainfall ◽

Large Area ◽

Landslide Occurrence ◽

Multi Scale ◽

Multi Temporal ◽

Hazard And Risk

Landslides represent a worldwide natural hazard and often occur as cascading effects related to triggering events, such as earthquakes and hydrometeorological extremes. Recent examples are the Kaikoura earthquake in New Zealand (November 2016), the Gorkha earthquake in Nepal (April/May 2015), and the Typhoon Morakot in Taiwan (August 2009) as well as less intense rainfall events persisting over unusually long periods of time as observed for Central Asia (spring 2017) and Iran (spring 2019). Each of these events has caused thousands of landslides that account substantially to the primary disaster&#8217;s impact. Moreover, their initial failure usually represents the onset of long-term progressing slope destabilization leading to multiple reactivations and thus to long-term increased hazard and risk. Therefore, regular systematic high-resolution monitoring of landslide prone regions is of key importance for characterization, understanding and modelling of spatiotemporal landslide evolution in the context of different triggering and predisposing settings. Because of the large extent of the affected areas of up to several ten thousands km2, the use of multi-temporal and multi-scale remote sensing methods is of key importance for large area process analysis. In this context, new opportunities have opened up with the increasing availability of satellite remote sensing data of suitable spatial and temporal resolution (Sentinels, Planet) as well as the advances in UAV based very high resolution monitoring and mapping.During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of landslide prone regions. These developments include automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope evolution.&#160; Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to evolution of landslide prone slopes at different spatial and temporal scales. &#160;In this context, we additionally integrate UAV-based observation for deriving volumetric changes also related to globally available DEM products, such as SRTM and ALOS. &#160;We present results for selected settings comprising large area co-seismic landslide occurrence related to the Kaikoura 2016 and the Nepal 2015 earthquakes. For the latter one we also analyzed annual pre- and post-seismic monsoon related landslide activity contributing to a better understanding of the interplay between these main triggering factors. Moreover, we report on ten years of large area systematic landslide monitoring in Southern Kyrgyzstan resulting in a multi-temporal regional landslide inventory of so far unprecedented spatiotemporal detail and completeness forming the basis for further analysis of the obtained landslide concentration patterns. We also present first results of our analysis of landslides triggered by intense rainfall and flood events in spring of 2019 in the North of Iran. We conclude that in all cases, the obtained results are crucial for improved landslide prediction and reduction of future landslide impact. Thus, our methodological developments represent an important contribution towards improved hazard and risk assessment as well as rapid mapping and early warning

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TIME SERIES ANALYSIS: NONSTATIONARY AND NONINVERTIBLE DISTRIBUTION THEORY

Econometric Theory ◽

10.1017/s0266466698144055 ◽

1998 ◽

Vol 14 (4) ◽

pp. 511-516

Author(s):

Graham Elliott

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Main Part ◽

Basic Skills ◽

Distribution Theory ◽

Large Area ◽

Series Analysis ◽

Potential Contributor ◽

Solid Foundation

Professor Tanaka has written a useful and unique book. Despite the main part of the title, the book does not seek to be a generalist work but rather has specific goals in mind and is focused on a single aspect of the large area of time series. This is not a criticism; there have been a large number of books that provide overviews of the field or components (Banerjee, Dolado, Galbraith, and Hendry, 1993; Hamilton, 1994; Hatanaka, 1996; Fuller, 1996). In directing his attention at a single subtopic, Tanaka has been able to do an excellent job of both covering the area in enough detail for the reader to obtain a solid foundation as a user of and potential contributor to this field and also to take time to include an extensive development of the basic skills necessary for the reader to understand the material at this level.

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Obtaining rubber plantation age information from very dense Landsat TM & ETM + time series data and pixel-based image compositing

Remote Sensing of Environment ◽

10.1016/j.rse.2017.04.003 ◽

2017 ◽

Vol 196 ◽

pp. 89-100 ◽

Cited By ~ 35

Author(s):

Philip Beckschäfer

Keyword(s):

Time Series ◽

Time Series Data ◽

Landsat Tm ◽

Series Data ◽

Rubber Plantation ◽

Image Compositing ◽

Plantation Age

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AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-475-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 475-479 ◽

Cited By ~ 2

Author(s):

Nicolas Champion

Keyword(s):

Time Series ◽

Satellite Images ◽

Satellite Image ◽

Region Growing ◽

Shadow Detection ◽

The Other ◽

Landsat 8 ◽

Cloud Detection ◽

Large Area ◽

The Difference

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

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Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

Remote Sensing ◽

10.3390/rs11222616 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2616 ◽

Cited By ~ 4

Author(s):

Stefan Mayr ◽

Claudia Kuenzer ◽

Ursula Gessner ◽

Igor Klein ◽

Martin Rutzinger

Keyword(s):

Remote Sensing ◽

Time Series ◽

Optical Sensors ◽

Land Surface ◽

Global Scale ◽

Observation Time ◽

Large Area ◽

Validation Data ◽

Information Products ◽

Global Coverage

Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.

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Creating a Robust Reference Dataset for Large Area Time Series Disturbance Classification

Remote Sensing Time Series Image Processing ◽

10.1201/9781315166636-8 ◽

2018 ◽

pp. 157-171

Author(s):

Mariela Soto-Berelov ◽

Andrew Haywood ◽

Simon Jones ◽

Samuel Hislop ◽

Trung H. Nguyen

Keyword(s):

Time Series ◽

Reference Dataset ◽

Large Area

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Use of passive-microwave data to monitor spatial and temporal variations of snow cover at tree line near Churchill, Manitoba, Canada

Annals of Glaciology ◽

10.3189/172756402781817590 ◽

2002 ◽

Vol 34 ◽

pp. 58-64 ◽

Cited By ~ 8

Author(s):

Frédérique C. Pivot ◽

Claude Kergomard ◽

Claude R. Duguay

Keyword(s):

Time Series ◽

Snow Cover ◽

Snow Water Equivalent ◽

Spatial Scales ◽

Climatic Variability ◽

Passive Microwave ◽

Spatial And Temporal Variability ◽

Large Area ◽

Spectral Difference ◽

Microwave Data

AbstractWe evaluated the contribution of Special Sensor Microwave/Imager (SSM/I) passive-microwave data to the monitoring of spatial and temporal variability of snow cover in the Churchill area, Manitoba, Canada. Because of the coarse spatial resolution of current passive-microwave sensors, the estimation of snow water equivalent using empirical equations with these instruments is largely compromised in complex areas such as Churchill (forest–tundra ecotone). However, with its high frequency of observations and the availability of a long time series (1988–99), passive-microwave data from the SSM/I radiometer remain a very valuable tool for monitoring the temporal evolution of snow cover at various spatial scales. Through winter 1997/98, we first examined the passive-microwave signatures at the local scale and we identified the major stages of the snow period. Principal-component analysis (PCA) applied on spectral-difference (Tb(19H) - Tb(37H))time series (1988–99) enabled us to identify spatio-temporal effects over a large area. PCA also permitted the extraction of indices of relevance for monitoring climatic variability and climate change (annual snow-cover duration, dates of snow-cover appearance and disappearance).

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Development of digital gamma-activation autoradiography for analysis of samples of large area

Proceedings in Radiochemistry ◽

10.1524/rcpr.2011.0052 ◽

2011 ◽

Vol 1 (1) ◽

pp. 299-303

Author(s):

V. P. Kolotov ◽

D. S. Grozdov ◽

N. N. Dogadkin ◽

V. I. Korobkov

Keyword(s):

Time Series ◽

Computer Modelling ◽

Precious Metals ◽

The Other ◽

Local Analysis ◽

Large Area ◽

Gamma Activation ◽

Thin Sections ◽

Large Size ◽

Activation Autoradiography

Abstract Gamma-activation autoradiography is a prospective method for screening detection of inclusions of precious metals in geochemical samples. Its characteristics allow analysis of thin sections of large size (tens of cm 2 ), that favourably distinguishes it among the other methods for local analysis. At the same time, the activating field of the accelerator bremsstrahlung, displays a sharp intensity decrease relative to the distance along the axis. A method for activation dose “equalization” during irradiation of the large size thin sections has been developed. The method is based on the usage of a hardware-software system. This includes a device for moving the sample during the irradiation, a program for computer modelling of the acquired activating dose for the chosen kinematics of the sample movement and a program for pixel-by pixel correction of the autoradiographic images. For detection of inclusions of precious metals, a method for analysis of the acquired dose dynamics during sample decay has been developed. The method is based on the software processing pixel by pixel a time-series of coaxial autoradiographic images and generation of the secondary meta-images allowing interpretation regarding the presence of interesting inclusions based on half-lives. The method is tested for analysis of copper-nickel polymetallic ores. The developed solutions considerably expand the possible applications of digital gamma-activation autoradiography.

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