scholarly journals Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

2021 ◽  
Vol 14 (1) ◽  
pp. 172
Author(s):  
Zhipeng Tang ◽  
Giuseppe Amatulli ◽  
Petri K. E. Pellikka ◽  
Janne Heiskanen
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Land Cover ◽  
Temporal Information ◽  
Data Reconstruction ◽  
Large Area ◽  
Landsat Images ◽  
Accurate Performance ◽  
Filling Method ◽  
One Year

The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.

scholarly journals 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

2019 ◽  
Vol 11 (5) ◽  
pp. 489 ◽  
Author(s):  
Tengfei Long ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Weili Jiao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Google Earth ◽  
Burned Area ◽  
Landsat 8 ◽  
Landsat Images ◽  
Burned Areas ◽  
Random Decision Forests ◽  
Burned Area Mapping ◽  
Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

scholarly journals An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2779 ◽  
Author(s):  
Katie Awty-Carroll ◽  
Pete Bunting ◽  
Andy Hardy ◽  
Gemma Bell
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Land Cover ◽  
Change Detection ◽  
Seasonal Changes ◽  
Detection Methods ◽  
Continuous Change ◽  
Validation Data ◽  
Simulated Time ◽  
Simulated Time Series

Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work.

scholarly journals A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

2016 ◽  
Vol 8 (4) ◽  
pp. 312 ◽  
Author(s):  
Michael Schmidt ◽  
Matthew Pringle ◽  
Rakhesh Devadas ◽  
Robert Denham ◽  
Dan Tindall
Keyword(s):  
Time Series ◽  
Large Area ◽  
Landsat Images

scholarly journals Surface Tradeoffs and Elevational Shifts at the Largest Italian Glacier: A Thirty-Years Time Series of Remotely-Sensed Images

2021 ◽  
Vol 13 (1) ◽  
pp. 134
Author(s):  
Nicola Alessi ◽  
Camilla Wellstein ◽  
Duccio Rocchini ◽  
Gabriele Midolo ◽  
Klaus Oeggl ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Biodiversity Loss ◽  
Remotely Sensed ◽  
Alpine Grassland ◽  
Series Data ◽  
Alpine Grasslands ◽  
Landsat Images ◽  
Land Cover Types

Biodiversity loss occurring in mountain ecosystems calls for integrative approaches to improve monitoring processes in the face of human-induced changes. With a combination of vegetation and remotely-sensed time series data, we quantitatively identify the responses of land-cover types and their associated vegetation between 1987 and 2016. Fuzzy clustering of 11 Landsat images was used to identify main land-cover types. Vegetation belts corresponding to such land-cover types were identified by using species indicator analysis performed on 80 vegetation plots. A post-classification evaluation of trends, magnitude, and elevational shifts was done using fuzzy membership values as a proxy of the occupied surfaces by land-cover types. Our findings show that forests and scrublands expanded upward as much as the glacier retreated, i.e., by 24% and 23% since 1987, respectively. While lower alpine grassland shifted upward, the upper alpine grassland lost 10% of its originally occupied surface showing no elevational shift. Moreover, an increase of suitable sites for the expansion of the subnival vegetation belt has been observed, due to the increasing availability of new ice-free areas. The consistent findings suggest a general expansion of forest and scrubland to the detriment of alpine grasslands, which in turn are shifting upwards or declining in area. In conclusion, alpine grasslands need urgent and appropriate monitoring processes ranging from the species to the landscape level that integrates remotely-sensed and field data.

scholarly journals Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data

2021 ◽  
Vol 10 (8) ◽  
pp. 513
Author(s):  
Saeid Zare Naghadehi ◽  
Milad Asadi ◽  
Mohammad Maleki ◽  
Seyed-Mohammad Tavakkoli-Sabour ◽  
John Lodewijk Van Genderen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Large Area ◽  
Landsat Images ◽  
Spectral Angle Mapper ◽  
Artificial Neural ◽  
Land Cover Maps

A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), and Support Vector Machines (SVMs) algorithms are used as the representatives for parametric, non-parametric and subpixel capable methods for change detection and change prediction of Urmia City (Iran) and its suburbs. Landsat images of 2000, 2010, and 2020 have been used to provide land cover information. The results demonstrated 0.93–0.94 overall accuracies for MLC and SVMs’ algorithms, but it was around 0.79 for the SAM algorithm. The MLC performed slightly better than SVMs’ classifier. Cellular Automata Artificial neural network method was used to predict land cover changes. Overall accuracy of MLC was higher than others at about 0.94 accuracy, although, SVMs were slightly more accurate for large area segments. Land cover maps were predicted for 2030, which demonstrate the city’s expansion from 5500 ha in 2000 to more than 9000 ha in 2030.

scholarly journals ANNUAL URBAN BUILT-UP CHANGE AREA ONLINE EXTRACTION USING LANDSAT TIME SERIES DATA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 735-739
Author(s):  
J. Zhang ◽  
H. Wu ◽  
C. Cai
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Series Data ◽  
Landsat Images ◽  
Area Change ◽  
Land Cover Types ◽  
Online Processing ◽  
Online Extraction ◽  
Trajectory Modelling

Abstract. Urban built-up area change information in multiple periods is a pivotal factor in global climate change application and sustainable development research. Due to spatial-temporal expression of land cover types, processing speed and operability, built-up area change information extraction using Landsat time series data is still a challenging task. To provide insights into the inter-annual dynamic of land use change, focusing on how time series characteristics improves recognition of urban change and how much online extraction convenience is facilitated, this paper presents a new methodology to built-up change area extraction using inter-annual time series of Landsat images. The central premise of the approach is that time series characteristics are firstly expressed by spectral index. The logistic algorithm is then used in time series trajectory modelling of land cover types for annual urban built-up change area extraction. Finally, the individual steps of the whole process, including image selection, time series trajectory modelling and results display, are converted to web service for online processing. The further comparison is also conducted between the proposed method and post-classification comparison method. Results show that the online processing mode has strengths regarding the provision of functionality to user-end, the automation of recurring tasks or the sharing of workflows. Results also demonstrate that the proposed method improves the accuracy of annual urban built-up change area extraction.

scholarly journals Application of Time Series Landsat Images to Examining Land-use/Land-cover Dynamic Change

2012 ◽  
Vol 78 (7) ◽  
pp. 747-755 ◽  
Author(s):  
Dengsheng Lu ◽  
Scott Hetrick ◽  
Emilio Moran ◽  
Guiying Li
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Dynamic Change ◽  
Land Use Land Cover ◽  
Landsat Images

Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data

2015 ◽  
Vol 41 (4) ◽  
pp. 293-314 ◽  
Author(s):  
Steven E. Franklin ◽  
Oumer S. Ahmed ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
Txomin Hermosilla ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Time Series Data ◽  
Series Data ◽  
Large Area ◽  
Land Cover Dynamics

scholarly journals Three Decades after: Landscape Dynamics in Different Colonisation Models Implemented in the Brazilian Legal Amazon

2021 ◽  
Vol 13 (22) ◽  
pp. 4581
Author(s):  
Valdir Moura ◽  
Ranieli dos Anjos de Souza ◽  
Erivelto Mercante ◽  
Jonathan Richetti ◽  
Jerry Adriani Johann
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Tropical Rainforest ◽  
Road Construction ◽  
Landscape Dynamics ◽  
The State ◽  
Secondary Forests ◽  
Rapid Loss ◽  
Landsat Images

Several colonisation projects were implemented in the Brazilian Legal Amazon in the 1970s and 1980s. Among these colonisation projects, the most prominent were those with the “fishbone” and “topographic” models. Within this scope, the settlements known as Anari and Machadinho stand out because they are contiguous areas with different models and structures of occupation and colonisation. The main objective of this work was to evaluate the dynamics of Land-Use and Land-Cover (LULC) in two different colonisation models, implanted in the State of Rondônia in the 1980s. The fishbone and topographic or Disorganised Multidirectional models were implemented in the Anari and Machadinho settlements, respectively. A 36-year time series of Landsat images (1984–2020) was used to evaluate the rates and trends in the LULC process in the different colonisation models. In the analysed models, a rapid loss of primary and secondary forests (anthropized areas) was observed, mainly due to the dynamics of its use, established by the Agriculture/Pasture relation with a heavy dependence on road construction. Understanding these two forms of occupation can help the future programs and guidelines of the Brazilian Legal Amazon and any tropical rainforest across the globe.

scholarly journals Perceived Human-Induced Causes of Landslide in Chattogram Metropolitan Area in Bangladesh

2021 ◽  
Author(s):  
Zia Ahmed ◽  
A H M Belayeth Hussain ◽  
Mufti Nadimul Quamar Ahmed ◽  
Rafiul Alam ◽  
Hafiz-Al Rezoan ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Metropolitan Area ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Weak Soil ◽  
Landsat Images ◽  
Level Of Education ◽  
Steep Hill

Abstract This study investigates Land Use Land Cover changes in the Chattogram metropolitan area, the second largest city in Bangladesh. Using a questionnaire survey of 150 local inhabitants, the study explores perceived human-induced causes of landslides. Using time series Landsat images this study also analyzes Land Use Land Cover changes from 1990 to 2020. The analysis reveals built-up area extended rapidly during 1990 to 2020. In 1990, total built up area was 82.13km², which in 30 years, stood at 451.34km². Conversely, total vegetative area decreased rapidly. In 1990, total vegetation area was 364.31km², which reduced to 130.44 km² in 2020. The survey respondents identified extensive rainfall, hill cutting, steep hill, and weak soil texture as several reasons for landslide. Findings show that age and experience of facing landslide are two significant predictors to explain whether excessive hill cutting is solely responsible for landslide. Level of education and experience of facing landslide are statistically significant in explaining building infrastructure as solitary cause to landslide. Gender, age and income of the respondents significantly explain deforestation as the only responsible for landslide. Finally, gender, level of education, and income of the respondents significantly explain only excessive sand collection causes landslide.

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