Spatiotemporal changes of glacier and seasonal snow fluctuations over the Namcha Barwa–Gyala Peri massif using object-based classification from Landsat time series

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

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Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Remote Sensing ◽

10.3390/rs11050570 ◽

2019 ◽

Vol 11 (5) ◽

pp. 570 ◽

Cited By ~ 6

Author(s):

Inacio Bueno ◽

Fausto Acerbi Júnior ◽

Eduarda Silveira ◽

José Mello ◽

Luís Carvalho ◽

...

Keyword(s):

Time Series ◽

Change Detection ◽

Seasonal Changes ◽

Vegetation Indices ◽

Detection Methods ◽

Object Based Image Analysis ◽

Spectral Bands ◽

Object Based ◽

Shortwave Infrared ◽

Spectral Channels

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

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Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

Remote Sensing ◽

10.3390/rs12223798 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3798

Author(s):

Lei Ma ◽

Michael Schmitt ◽

Xiaoxiang Zhu

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Data ◽

Land Cover Classification ◽

Series Data ◽

Spectral Bands ◽

Object Based ◽

Spatio Temporal ◽

Optical Satellite Images ◽

The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

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A new analysis approach for long‐term variations of forest loss, fragmentation, and degradation resulting from road‐network expansion using Landsat time‐series and object‐based image analysis

Land Degradation and Development ◽

10.1002/ldr.3530 ◽

2020 ◽

Vol 31 (12) ◽

pp. 1462-1481 ◽

Cited By ~ 2

Author(s):

Zeinab Shirvani ◽

Omid Abdi ◽

Manfred F. Buchroithner

Keyword(s):

Time Series ◽

Image Analysis ◽

Road Network ◽

Analysis Approach ◽

Forest Loss ◽

Network Expansion ◽

Object Based Image Analysis ◽

Object Based ◽

Long Term Variations

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Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands

Remote Sensing ◽

10.3390/rs10091467 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1467 ◽

Cited By ~ 7

Author(s):

Meghan Halabisky ◽

Chad Babcock ◽

L. Moskal

Keyword(s):

Time Series ◽

Image Analysis ◽

Classification Accuracy ◽

Sampling Design ◽

Landsat Imagery ◽

Temporal Dimension ◽

Substantial Impact ◽

Object Based Image Analysis ◽

Temporal Domain ◽

Object Based

Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristic. When multiple images are used in object-based image analysis, it is often constrained to a specific number of images which are selected because they cover the perceived range of temporal variability of the features of interest. Here, we provide a method to identify wetlands using a time series of Landsat imagery by building a Random Forest model using each image observation as an explanatory variable. We tested our approach in Douglas County, Washington, USA. Our approach exploiting the temporal domain classified wetlands with a high level of accuracy and reduced the number of spectrally similar false positives. We explored how sampling design (i.e., random, stratified, purposive) and temporal resolution (i.e., number of image observations) affected classification accuracy. We found that sampling design introduced bias in different ways, but did not have a substantial impact on overall accuracy. We also found that a higher number of image observations up to a point improved classification accuracy dependent on the selection of images used in the model. While time series analysis has been part of pixel-based remote sensing for many decades, with improved computer processing and increased availability of time series datasets (e.g., Landsat archive), it is now much easier to incorporate time series into object-based image analysis classification.

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Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative

10.5194/egusphere-egu2020-19228 ◽

2020 ◽

Author(s):

Gabriele Schwaizer ◽

Lars Keuris ◽

Thomas Nagler ◽

Chris Derksen ◽

Kari Luojus ◽

...

Keyword(s):

Time Series ◽

Satellite Data ◽

Snow Water Equivalent ◽

Climate Model ◽

Process Models ◽

Climate Data ◽

Seasonal Snow ◽

Snow Water

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

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