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Dense Image Time Series Analysis for Ecosystem Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 42685

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Dr. Jan Verbesselt

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Guest Editor

Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
Interests: change monitoring and characterization; measuring ecosystem resilience; time series analysis; trend analysis and break detection; terrestrial ecosystems, forests, human climate interaction

Dr. Rogier De Jong

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Guest Editor

Remote Sensing Laboratories, Dept. of Geography, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
Interests: land-system dynamics; time-series analysis and spatial-temporal modeling; terrestrial ecosystems: vegetation, climate and human impact

Dr. David Small

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Guest Editor

Remote Sensing Laboratories, Dept. of Geography, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
Interests: geometric calibration; radiometric calibration; composite products; wet snow dynamics; time series analysis

Dr. Johannes Reiche

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Guest Editor

Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, 6708 PB Wageningen, The Netherlands
Interests: radar remote sensing of forest dynamics
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Dr. Kirsten de Beurs

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Co-Guest Editor

Department of Geography and Environmental Sustainability, The University of Oklahoma, 100 East Boyd, St. SEC Suite 566, Norman, OK 73019, USA
Interests: remote sensing; phenology; land cover and land use change; urban
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of Sentinel 1 and 2 satellites, together with the Landsat constellation, dense optical and RADAR satellite image time series with a high spatial resolution, up to 10 m, are available today. Methods for analyzing full and dense time series, which were previously only applicable to medium and coarse spatial resolution time series, are becoming applicable on satellite image time series that provide high spatial details. This offers a great opportunity to explore the full potential of time series analysis for ecosystem monitoring, including, but not limited to, resilience, functional biodiversity and land cover and land use monitoring. This opportunity comes with challenges and requires new methods that can efficiently handle dense satellite image time series that enable temporal analysis while accounting for a spatial context. This would enable the monitoring of and surface dynamics, ecosystem resilience, phenology, breaks, extremes and outliers with unprecedented detail.

We welcome contributions for this Special Issue on dense satellite image time series analysis in the following domains and application fields:

●        Ecosystem resilience: Recently several papers have been published where ecosystem resilience and stability measures have been derived from dense time series of coarse spatial resolution images. Large challenges, however, remain when deriving ecosystem resilience from dense high spatial resolution images like Sentinel 1, 2 and Landsat. These time series are often highly nonlinear, capture severe climate and human induced disturbances while the seasonality is difficult to model. Being able to derive ecosystem resilience from satellite image time series with high spatial and temporal resolution (up to 10 m and near-daily) while dealing with disturbances and seasonality however is needed to make resilience more practically measurable as an operational tool, for example, for policy makers, management of national parks, and land owners.
●        Functional biodiversity: Dynamics of terrestrial ecosystems, in particular of vegetation, have been extensively studied using medium to coarse spatial-resolution time series. Examples using intra-annual dynamics include, but are not limited to, land-surface phenology (LSP) and ecosystem extent. Such metrics are essential indicators of changes in biodiversity but coarse spatial resolutions largely prevent linking changes to species or even to functional groups. The current availability of dense and high-spatial resolution time series holds the promise of disentangling the general pattern into such groups. However, this requires novel analysis and validation approaches; for instance using in situ observation networks.
●        Dynamic land cover and use mapping: New methods are needed to create dynamic land cover and use maps that are being updated only when a land change occurs. Current land cover and use maps are often static and typically updated on a yearly basis. However, these maps need to be temporally consistent while providing accurate information with increasing need for more information on land use (forest disturbances, crop and pasture use patterns, wetlands and water bodies) and with specific requirements on estimation and related accuracy. There is a need for novel methods that enable land monitoring, provide error measures and make use of multi-sensor time series for both land cover characterization, and determination of drivers of change.
●        Snow Dynamics and multi-temporal RADAR challenges: RADAR time series can be interferometric stacks, or backscatter values acquired from either a single or multiple tracks/sensors. RADAR images are not subject to clouds, but are affected by topography in many ways - both their geometry and radiometry require correction to enable combinations with independent data, such as optical reflectances or vegetation indices. As more and more varied RADAR data is becoming available from multiple sensors at multiple frequencies, often acquired each in an individual geometry, combining data from all sensors into a single analysis is a challenge. As such, monitoring wet snow dynamics benefits from the integration of as many sensors as possible to drive down the revisit interval and maximize the temporal resolution.

Dense time series analysis methods are expected to be developed for high spatial resolution imagery or be generic with the potential to be applied at or further developed towards

●     resilience monitoring and measuring
●     space-time anomaly detection
●     data exploration and data visualization
● phenological metrics extraction and analysis
●     change monitoring and characterization
●     combining unevenly spaced time series with data gaps time from multiple sensors with diverse geometries

Authors are required to check and follow the specific Instructions to authors, https://www.mdpi.com/journal/remotesensing/instructions.

Sensors: Sentinel-1, 2 and Landsat, among others providing dense image time series

Dr. Jan Verbesselt
Dr. Rogier de Jong
Dr. David Small
Dr. Johannes Reiche
Dr. Kirsten de Beurs
Guest Editors

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Published Papers (5 papers)

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Research

20 pages, 7591 KiB

Open AccessEditor’s ChoiceArticle

A Self-Calibrated Non-Parametric Time Series Analysis Approach for Assessing Insect Defoliation of Broad-Leaved Deciduous Nothofagus pumilio Forests

by Roberto O. Chávez, Ronald Rocco, Álvaro G. Gutiérrez, Marcelo Dörner and Sergio A. Estay

Remote Sens. 2019, 11(2), 204; https://doi.org/10.3390/rs11020204 - 21 Jan 2019

Cited by 27 | Viewed by 6279

Abstract

Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches rely on parametric functions to describe the natural annual phenological cycle of the forest, from which anomalies are calculated and used to assess defoliation. Quantification of the natural variability of the annual phenological baseline is limited in parametric approaches, which is critical to evaluating whether an observed anomaly is “true” defoliation or only part of the natural forest variability. We present here a fully self-calibrated, non-parametric approach to reconstruct the annual phenological baseline along with its confidence intervals using the historical frequency of a vegetation index (VI) density, accounting for the natural forest phenological variability. This baseline is used to calculate per pixel (1) a VI anomaly per date and (2) an anomaly probability flag indicating its probability of being a “true” anomaly. Our method can be self-calibrated when applied to deciduous forests, where the winter VI values are used as the leafless reference to calculate the VI loss (%). We tested our approach with dense time series from the MODIS enhanced vegetation index (EVI) to detect and map a massive outbreak of the native Ormiscodes amphimone caterpillars which occurred in 2015–2016 in Chilean Patagonia. By applying the anomaly probability band, we filtered out all pixels with a probability <0.9 of being “true” defoliation. Our method enabled a robust spatiotemporal assessment of the O. amphimone outbreak, showing severe defoliation (60–80% and >80%) over an area of 15,387 ha of Nothofagus pumilio forests in only 40 days (322 ha/day in average) with a total of 17,850 ha by the end of the summer. Our approach is useful for the further study of the apparent increasing frequency of insect outbreaks due to warming trends in Patagonian forests; its generality means it can be applied in deciduous broad-leaved forests elsewhere. Full article

(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)

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23 pages, 10302 KiB

Open AccessFeature PaperArticle

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

by Marius Rüetschi, David Small and Lars T. Waser

Remote Sens. 2019, 11(2), 115; https://doi.org/10.3390/rs11020115 - 10 Jan 2019

Cited by 73 | Viewed by 9935

Abstract

Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer’s accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user’s accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user’s needs. Full article

(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)

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20 pages, 9182 KiB

Open AccessArticle

Detection of Cropland Change Using Multi-Harmonic Based Phenological Trajectory Similarity

by Jiage Chen, Jun Chen, Huiping Liu and Shu Peng

Remote Sens. 2018, 10(7), 1020; https://doi.org/10.3390/rs10071020 - 26 Jun 2018

Cited by 24 | Viewed by 5057

Abstract

Accurate information on cropland changes is critical for food production and security, sustainable cropland management, and global change studies. The common change detection methods bi-temporal based, using remotely sensed imagery easily generate pseudo changes due to phenological or seasonal differences. Cropland exhibits a distinctive phenological trajectory that has strong periodic characteristics and seasonal paths. This paper proposes the use of phenological trajectory similarity to search for the overall changes between two time-series images instead of single change events between two dates of imagery. Due to the complex spectral–temporal characteristic of cropland, a phenological trajectory was constructed using a multi-harmonic model for capturing intra-annual variations. Then, phenological trajectory similarity was measured using coefficient vector difference (CVD), and used for detecting change/no-change areas when considering both the amplitude and phase difference. Finally, instead of the traditional classification method based on original images, we used the coefficient ratio vector (CRV) as the input for change type discrimination. The distance between the coefficient ratio vector (CRV) of the change pixel and of the reference change type was calculated to identify the exactly changed types. The performance of this proposed approach was tested using two sets of Landsat time-series images from 2010 and 2015. Moreover, the change area detection results of three other methods, namely, the continuous change detection and classification (CCDC), change vector analysis (CVA), and post-classification comparison (PCC), were also calculated for comparison and analysis. The results indicated that the proposed approach acquired the highest accuracy with an overall accuracy of 98.58% and a kappa coefficient of 0.82, which demonstrated that the method provides the capacity to detect real changes and estimate pseudo changes caused by season differences. Full article

(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)

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18066 KiB

Open AccessArticle

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

by Sanne Diek, Fabio Fornallaz, Michael E. Schaepman and Rogier De Jong

Remote Sens. 2017, 9(12), 1245; https://doi.org/10.3390/rs9121245 - 1 Dec 2017

Cited by 159 | Viewed by 12545

Abstract

Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making. Full article

(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)

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1652 KiB

Open AccessArticle

Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation

by Mailys Lopes, Mathieu Fauvel, Annie Ouin and Stéphane Girard

Remote Sens. 2017, 9(10), 993; https://doi.org/10.3390/rs9100993 - 25 Sep 2017

Cited by 42 | Viewed by 6874

Abstract

Grasslands represent a significant source of biodiversity that is important to monitor over large extents. The Spectral Variation Hypothesis (SVH) assumes that the Spectral Heterogeneity (SH) measured from remote sensing data can be used as a proxy for species diversity. Here, we argue the hypothesis that the grassland’s species differ in their phenology and, hence, that the temporal variations can be used in addition to the spectral variations. The purpose of this study is to attempt verifying the SVH in grasslands using the temporal information provided by dense Satellite Image Time Series (SITS) with a high spatial resolution. Our method to assess the spectro-temporal heterogeneity is based on a clustering of grasslands using a robust technique for high dimensional data. We propose new SH measures derived from this clustering and computed at the grassland level. We compare them to the Mean Distance to Centroid (MDC). The method is experimented on 192 grasslands from southwest France using an intra-annual multispectral SPOT5 SITS comprising 18 images and using single images from this SITS. The combination of two of the proposed SH measures—the within-class variability and the entropy—in a multivariate linear model explained the variance of the grasslands’ Shannon index more than the MDC. However, there were no significant differences between the predicted values issued from the best models using multitemporal and monotemporal imagery. We conclude that multitemporal data at a spatial resolution of 10 m do not contribute to estimating the species diversity. The temporal variations may be more related to the effect of management practices. Full article

(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)

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