scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8/OLI AND SENTINEL-2/MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY

2021 ◽  
Vol V-3-2021 ◽  
pp. 271-277
Author(s):  
R. F. B. Marujo ◽  
J. G. Fronza ◽  
A. R. Soares ◽  
G. R. Queiroz ◽  
K. R. Ferreira
Keyword(s):  
Atmospheric Correction ◽  
Open Data ◽  
Image Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Continental Scale ◽  
The Impact ◽  
Sentinel 2

Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.

scholarly journals A LANDSAT TIME-SERIES STACKS MODEL FOR DETECTION OF CROPLAND CHANGE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 693-697
Author(s):  
J. Chen ◽  
J. Chen ◽  
J. Zhang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Surface ◽  
Spectral Band ◽  
Main Idea ◽  
Landsat 8 ◽  
Continuous Change ◽  
Landsat Images ◽  
Detection Techniques

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the “true change” without overestimating the “false” one, while CVA pointed out “true change” pixels with a large number of “false changes”. The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

scholarly journals Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

2021 ◽  
Vol 13 (10) ◽  
pp. 1927
Author(s):  
Fuqin Li ◽  
David Jupp ◽  
Thomas Schroeder ◽  
Stephen Sagar ◽  
Joshua Sixsmith ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Correction Method ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Correction Algorithm ◽  
Simple Method ◽  
North East ◽  
The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

Persistent Homology for Land Cover Change Detection

2021 ◽  
Author(s):  
Djamel Bouchaffra ◽  
Faycal Ykhlef
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Homology Theory ◽  
Urban Morphology ◽  
Detection Methods ◽  
Landsat Images ◽  
Data Repositories ◽  
Land Cover Change Detection

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

scholarly journals Change Detection Analysis Using Surveying and Geoinformatics Techniques

2018 ◽  
Vol 2 (1) ◽  
pp. 28-38
Author(s):  
I. C. Onuigbo ◽  
J. Y. Jwat
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Research Work ◽  
Temporal Data ◽  
Landsat Images ◽  
Detection Analysis ◽  
Ground Truthing ◽  
Landsat Satellite ◽  
Geoinformatics Techniques

The study was on change detection using Surveying and Geoinformatics techniques. For effective research study, Landsat satellite images and Quickbird imagery of Minna were acquired for three periods, 2000, 2005 and 2012. The research work demonstrated the possibility of using Surveying and Geoinformatics in capturing spatial-temporal data. The result of the research work shows a rapid growth in built-up land between 2000 and 2005, while the periods between 2005 and 2012 witnessed a reduction in this class. It was also observed that change by 2020 may likely follow the trend in 2005 – 2012 all things being equal. Built up area may increase to 11026.456 hectares, which represent 11% change. The study has shown clearly the extent to which MSS imagery and Landsat images together with extensive ground- truthing can provide information necessary for land use and land cover mapping. Attempt was made to capture as accurate as possible four land use and land cover classes as they change through time.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

2019 ◽  
Vol 11 (2) ◽  
pp. 181 ◽  
Author(s):  
Daniel Sousa ◽  
Christopher Small
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Mixture Models ◽  
Upper Bound ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Spatial Extent ◽  
Landsat Images ◽  
Rice Agriculture ◽  
Field Station

Rice is the staple food for more than half of humanity. Accurate prediction of rice harvests is therefore of considerable global importance for food security and economic stability, especially in the developing world. Landsat sensors have collected coincident thermal and optical images for the past 35+ years, and so can provide both retrospective and near-realtime constraints on the spatial extent of rice planting and the timing of rice phenology. Thermal and optical imaging capture different physical processes, and so provide different types of information for phenologic mapping. Most analyses use only one or the other data source, omitting potentially useful information. We present a novel approach to the mapping and monitoring of rice agriculture which leverages both optical and thermal measurements. The approach relies on Temporal Mixture Models (TMMs) derived from parallel Empirical Orthogonal Function (EOF) analyses of Landsat image time series. Analysis of each image time series is performed in two stages: (1) spatiotemporal characterization, and (2) temporal mixture modeling. Characterization evaluates the covariance structure of the data, culminating in the selection of temporal endmembers (EMs) representing the most distinct phenological cycles of either vegetation abundance or surface temperature. Modeling uses these EMs as the basis for linear TMMs which map the spatial distribution of each EM phenological pattern across study area. The two metrics we analyze in parallel are (1) fractional vegetation abundance (Fv) derived from spectral mixture analysis (SMA) of optical reflectance, and (2) land surface temperature (LST) derived from brightness temperature (Tb). These metrics are chosen on the basis of being straightforward to compute for any (cloud-free) Landsat 4-8 image in the global archive. We demonstrate the method using a 90 × 120 km area in the Sacramento Valley of California. Satellite Tb retrievals are corrected to LST using a standardized atmospheric correction approach and pixelwise fractional emissivity estimates derived from SMA. LST and Tb time series are compared to field station data in 2016 and 2017. Uncorrected Tb is observed to agree with the upper bound of the envelope of air temperature observations to within 3 °C on average. As expected, LST estimates are 3 to 5 °C higher. Soil T, air T, Tb and LST estimates can all be represented as linear transformations of the same seasonal cycle. The 3D temporal feature spaces of Fv and LST clearly resolve 5 and 7 temporal EM phenologies, respectively, with strong clustering distinguishing rice from other vegetation. Results from parallel EOF analyses of coincident Fv and LST image time series over the 2016 and 2017 growing seasons suggest that TMMs based on single year Fv datasets can provide accurate maps of crop timing, while TMMs based on dual year LST datasets can provide comparable maps of year-to-year crop conversion. We also test a partial-year model midway through the 2018 growing season to illustrate a potential real-time monitoring application. Field validation confirms the monitoring model provides an upper bound estimate of spatial extent and relative timing of the rice crop accurate to 89%, even with an unusually sparse set of usable Landsat images.

scholarly journals In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

2019 ◽  
Vol 11 (2) ◽  
pp. 118 ◽  
Author(s):  
Valérie Demarez ◽  
Florian Helen ◽  
Claire Marais-Sicre ◽  
Frédéric Baup
Keyword(s):  
Time Series ◽  
Shuttle Radar Topography Mission ◽  
Irrigation Scheduling ◽  
Landsat 8 ◽  
Radar Images ◽  
Optical Images ◽  
Multi Temporal ◽  
Irrigated Crops ◽  
Elevation Model ◽  
The Impact

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome.

scholarly journals Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis

2020 ◽  
Vol 12 (23) ◽  
pp. 4001
Author(s):  
Ebrahim Ghaderpour ◽  
Tijana Vujadinovic
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Change Detection ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Stationary Time Series ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Temporal Segmentation ◽  
Jump Detection

Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings.

scholarly journals Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 398 ◽  
Author(s):  
Nety Nurda ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Satellite Remote Sensing ◽  
Land Suitability ◽  
Landsat 8 ◽  
Suitability Analysis ◽  
Land Suitability Analysis ◽  
Expert Opinions ◽  
Detection Analysis ◽  
The Government

The objective of this research was to detect changes in forest areas and, subsequently, the potential forest area that can be extended in the South Sumatra province of Indonesia, according to the Indonesian forest resilience classification zones. At first, multispectral satellite remote sensing datasets from Landsat 7 ETM+ and Landsat 8 OLI were classified into four classes, namely urban, vegetation, forest and waterbody to develop Land Use/Land Cover (LULC) maps for the year 2003 and 2018. Secondly, criteria, namely distance from rivers, distance from roads, elevation, LULC and settlements were selected and the reclassified maps were produced from each of the criteria for the land suitability analysis for forest extension. Thirdly, the Analytical Hierarchy Process (AHP) was incorporated to add expert opinions to prioritize the criteria referring to potential areas for forest extension. In the change detection analysis, Tourism Recreation Forest (TRF), Convertible Protection Forest (CPF) and Permanent Production Forest (PPF) forest zones had a decrease of 20%, 13% and 40% in area, respectively, in the forest class from 2003 to 2018. The Limited Production Forest (LPF) zone had large changes and decreased by 72% according to the LULC map. In the AHP method, the influential criteria had higher weights and ranked as settlements, elevation, distance from roads and distance from rivers. CPF, PPF and LPF have an opportunity for extension in the highly suitable classification (30%) and moderately suitable classification (41%) areas, to increase coverage of production forests. Wildlife Reserve Forests (WRFs) have potential for expansion in the highly suitable classification (30%) and moderately suitable classification (52%) areas, to keep biodiversity and ecosystems for wildlife resources. Nature Reserve Forests (NRFs) have an opportunity for extension in the highly suitable classification (39%) and moderately suitable classification (48%) areas, to keep the forests for nature and biodiversity. In case of TRF, there is limited scope to propose a further extension and is required to be managed with collaboration between the government and the community.

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