A Simple Transformation for Visualizing Non-seasonal Landscape Change From Dense Time Series of Satellite Data

Hird, Jennifer N.; Castilla, Guillermo; McDermid, Gregory J.; Bueno, Inácio Thomaz

doi:10.1109/jstars.2015.2419594

Cited by 19 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of land surface phenology, the analysis is performed for each phenometric. Currently implemented analyses are linear trend analysis to derive long-term changes [53,54] and an extended change, aftereffect, trend (CAT) transform [55] with full trend parameters for the three parts of the time series (example: Figure 11).…”

Section: Time Series Analysis/level 4 Highly Analysis Ready Data+mentioning

confidence: 99%

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Frantz

2019

Remote Sensing

207

View full text Add to dashboard Cite

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

show abstract

Section: Time Series Analysis/level 4 Highly Analysis Ready Data+mentioning

confidence: 99%

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Frantz

2019

Remote Sensing

207

View full text Add to dashboard Cite

show abstract

“…We transformed the geostatistical and spectral features time series into two summary variables: Change and Trend, adapted from Reference [16]. As described by Reference [51], change is the maximum interannual absolute difference in the time series data within the period 2003-2016 (Equation (2)).…”

Section: Spatio-temporal Metrics (Stm)mentioning

confidence: 99%

“…Remotely sensed data have been widely recognized as essential data sources for comprehensive mapping and quantification of land cover changes [8][9][10][11][12][13][14]. Studies have used the NDVI (Normalized Difference Vegetation Index) time series from moderate resolution sensors (that is, MODIS) for land cover change detection benefitting from their frequent revisit time [15,16]. However, images from medium spatial resolution sources such as Landsat offer more detailed spatial information, providing insights at smaller spatial scales over a longer period of time.…”

Section: Introductionmentioning

confidence: 99%

“…When images from different seasons are acquired, changes caused by phenological differences are present and can result in the mapping of a change in condition, rather than a change in land cover or land use [26]. To reduce the seasonal variations captured by remotely sensed images, techniques for de-seasoning time series have been used [27] on the assumption that seasonal patterns can be identified and removed [15,16,26,28,29]. However, this assumption may not always hold if the time series is for satellite images which are not acquired at regular intervals or have gaps due to the presence of clouds [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

et al. 2018

View full text Add to dashboard Cite

In forested areas that experience strong seasonality and are undergoing rapid land cover conversion (e.g., Brazilian savannas), the accuracy of remote sensing change detection is affected by seasonal changes that are erroneously classified as having changed. To improve the quality and consistency of regionally important forest change maps, we aim to separate process related change (for example, spectral variability due to phenology) from changes related to deforestations or fires. Seasonal models are typically used to account for seasonality, but fitting a model is difficult when there are insufficient data points in the time series. In this research, we utilize remotely sensed data and related spectral trends and the spatial context at the object level to evaluate the performance of geostatistical features to reduce the impact of seasonality from the NDVI (Normalized Difference Vegetation Index) of Landsat time series. The study area is the São Romão municipality, totaling 2440 km 2 , and is part of the Brazilian savannas biome. We first create image objects via multiresolution segmentation, basing the objects on the characteristics found in the first image (2003) of the 13-year time series. We intersected the objects with the NDVI images in order to extract semivariogram indices, the RVF (Ratio Variance-First lag) and AFM (Area First lag-First Maximum), and spectral information (average and standard deviation of NDVI values) to generate the time series from these features and to derive Spatio-Temporal Metrics (change and trend) to train a Random Forest (RF) algorithm. The NDVI spatial variability, captured by the AFM semivariogram index time series produced the best result, reaching 96.53% of the overall accuracy (OA) to separate no-change from forest change, while the greatest inter-class confusion occurred using the average of the NDVI values time series (OA = 63.72%). The spatial context approach we presented is a novel approach for the detection of forest change events that are subject to seasonality (and possible miss-classification of change) and mitigating the effects of forest phenology without the need for specific de-seasoning models.

show abstract

“…Bitemporal glacier change detection has long been the most common way to track glacier area change (e.g., [7]). Multitemporal change detection approaches that capture both sudden and gradual land-cover changes have been applied for vegetation and land-cover studies [52], [67]. In the future, it should be possible to exploit the seasonal band ratio pattern over glaciers described above for consecutive years to detect changes, but for this purpose, higher temporal resolution than currently available is needed.…”

Section: Glacier Change Analysismentioning

confidence: 99%

Regional Glacier Mapping Using Optical Satellite Data Time Series

Winsvold

Kääb

Nuth

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The first of two Sentinel-2 satellites, launched mid-2015, has similar characteristics as the Landsat TM/ETM + /OLI satellites. Together, these satellites will produce a tremendous quantity of optical images worldwide for glacier mapping, with increasing temporal coverage toward the more glacierized higher latitudes due to convergence of near-polar orbits. To exploit the potential of such near-future dense time series, methods for mapping glaciers from space should be revisited. Currently, snow and ice are typically classified from an optical satellite image using a multispectral band ratio. For each scene, mapping conditions will vary (e.g., snow, ice, and clouds) and not be equally optimal over the entire scene. The increasing amount of images makes it difficult to manually select the best glacier mapping scene as is the current practice. This work is based on the above robust image ratio method for exploiting the dense temporal image coverage. Four application scenarios using time series of Landsat type data for glacier mapping are presented. First, we synthesize an optimal band ratio image from a stack of images within one season to compensate for regional differences. The second application scenario introduces robust methods to improve automatic glacier mapping by exploiting the seasonal variation in spectral properties of snow. Third, we explore the spatio-temporal variation of glacier surface types. Finally, we show how the synthesized band ratio images from the first application scenario can be used for automatic glacier change detection. In summary, we explore automatic algorithms for glacier mapping applications that exploit the temporal signatures in the satellite data time series.

show abstract

A Simple Transformation for Visualizing Non-seasonal Landscape Change From Dense Time Series of Satellite Data

Cited by 19 publications

References 39 publications

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

Regional Glacier Mapping Using Optical Satellite Data Time Series

Contact Info

Product

Resources

About