Identification of cultivated land change trajectory and analysis of its process characteristics using time-series Landsat images: A study in the overlapping areas of crop and mineral production in Yanzhou City, China

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.

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Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984–2010 through PALSAR and time series Landsat images

Remote Sensing of Environment ◽

10.1016/j.rse.2016.12.025 ◽

2017 ◽

Vol 190 ◽

pp. 233-246 ◽

Cited By ~ 32

Author(s):

Jie Wang ◽

Xiangming Xiao ◽

Yuanwei Qin ◽

Jinwei Dong ◽

George Geissler ◽

...

Keyword(s):

Time Series ◽

Eastern Redcedar ◽

Landsat Images

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Identification of Urban Expansion Patterns in Bangkok Metropolitan Region Through Time Series of Landsat Images and Landscape Metrics

Springer Geography - Geoinformatics for Sustainable Development in Asian Cities ◽

10.1007/978-3-030-33900-5_4 ◽

2019 ◽

pp. 32-45

Author(s):

Chudech Losiri ◽

Masahiko Nagai

Keyword(s):

Time Series ◽

Landscape Metrics ◽

Urban Expansion ◽

Metropolitan Region ◽

Landsat Images

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Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v25i8.25 ◽

2021 ◽

Vol 25 (8) ◽

pp. 1449-1452

Author(s):

P.A. Ukoha ◽

S.J. Okonkwo ◽

A.R. Adewoye

Keyword(s):

Time Series ◽

Change Point ◽

Vegetation Index ◽

Google Earth ◽

Change Point Detection ◽

Mcmc Methods ◽

Landsat Images ◽

Southwestern Nigeria ◽

Forest Reserve ◽

Point Detection

This study uses satellite acquired vegetation index data to monitor changes in Akure forest reserve. Enhanced Vegetation Index (EVI) time series datasets were extracted from Landsat images; extraction was performed on the Google Earth Engine (GEE) platform. The datasets were analyzed using Bayesian Change Point (BCP) to monitor the abrupt changes in vegetation dynamics associated with deforestation. The BCP shows the magnitude of changes over the years, from the posterior data obtained. BCP focuses on changes in the long‐range using Markov Chain Monte Carlo (MCMC) methods, this returns posterior probability at > 0.5% of a change point occurring at each time index in the time series. Three decades of Landsat data were classified using the random forest algorithm to assess the rate of deforestation within the study area. The results shows forest in 2000 (97.7%), 2010 (89.4%), 2020 (84.7%) and non-forest increase 2000 (2.0%), 2010 (10.6%), 2020 (15.3%). Kappa coefficient was also used to determine the accuracy of the classification.

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Time series analysis of characteristics of spectral reflectance for developing a method for detecting arakashi (Quercus glauca) stands using Landsat images

Japanese Journal of Forest Planning ◽

10.20659/jjfp.52.1_7 ◽

2018 ◽

Vol 52 (1) ◽

pp. 7-14

Author(s):

Minami Okamoto ◽

Yasushi Mitsuda ◽

Miho Oda ◽

Tsuyoshi Kajisa

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Spectral Reflectance ◽

Landsat Images ◽

Series Analysis ◽

Quercus Glauca

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Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2020.03.014 ◽

2020 ◽

Vol 163 ◽

pp. 312-326 ◽

Cited By ~ 9

Author(s):

Xinxin Wang ◽

Xiangming Xiao ◽

Zhenhua Zou ◽

Luyao Hou ◽

Yuanwei Qin ◽

...

Keyword(s):

Time Series ◽

Coastal Wetlands ◽

Google Earth ◽

Landsat Images ◽

Google Earth Engine

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Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

Remote Sensing ◽

10.3390/rs11020181 ◽

2019 ◽

Vol 11 (2) ◽

pp. 181 ◽

Cited By ~ 8

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.

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Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

Remote Sensing ◽

10.3390/rs12223826 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3826 ◽

Cited By ~ 1

Author(s):

Yuhong He ◽

Jian Yang ◽

Xulin Guo

Keyword(s):

Time Series ◽

Vegetation Cover ◽

Driving Forces ◽

Spectral Unmixing ◽

Landsat Images ◽

Green Vegetation ◽

Arid Grasslands ◽

Spectral Mixture ◽

The Impact ◽

Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

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