Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis

Mardian, Jacob; Berg, Aaron; Daneshfar, Bahram

doi:10.1016/j.rse.2021.112292

Cited by 24 publications

(12 citation statements)

References 54 publications

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“…At the end we obtained for each month after the storm maps of damaged and undamaged area that were used to assess the accuracy of forest windstorm detection (Section 3.2) and to assess the damaged forest areas using the probability-based stratified estimators (Section 3. [40,46,57] for a complete description of the BEAST algorithm.…”

Section: Breaks For Additive Seasonal and Trend Iterative Algorithmmentioning

confidence: 99%

“…In addition, these algorithms are able to split the time series into three adaptative components (i.e., trend, seasonal, and remainder) [42,44,45] for each investigated year. Many of these studies use dense Landsat or MODIS TS as input data [40,46], while, the use of S2 and CCDC algorithm to map forest areas damaged by windstorms is limited [37]. Many recent studies on VAIA windstorm underlined that S2 is adequate to detect windstorm since S2 program offers innovative features for forest remote sensing by combining high spatial resolution (i.e., 13 bands, from 0.443 to 2.190 µm with the visible (i.e., R, G, B,) and the near infrared bands at 10-m spatial resolution and four red-edge bands at 20-m spatial resolution), wide coverage and a quick revisit time (i.e., every 5 days after the launch of Sentinel-2B satellite in 2017) [15,18,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Giannetti

Pecchi

Travaglini

et al. 2021

Forests

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Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019 – October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE< 3725.3 ha , SE% < 9.69 , OA > 89.7, PA and UA > 0.87, gmean > 0.83).

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Section: Breaks For Additive Seasonal and Trend Iterative Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Giannetti

Pecchi

Travaglini

et al. 2021

Forests

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“…M ULTITEMPORAL change detection (MTCD) in SAR images captured at different dates over the same geographical location is a significant application in image processing field [1]- [3]. In the past few years, MTCD has attracted widespread interest due to lots of real-world applications in diverse academic disciplines, such as environmental monitoring, damage assessment, agricultural surveys and others [4]- [6]. The goal of MTCD is to generate an accurate change map which represents the changed and unchanged regions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Active Contour Model With Markov Random Field for Change Detection

et al. 2022

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The traditional active contour models are sensitive to the speckle noise in the synthetic aperture radar (SAR) images. In this paper, the Markov random field (MRF) theory is incorporated into the fuzzy active contour model to detect the changes of multitemporal SAR images. In the proposed method, neighboring information is considered to modify the pointwise prior probability for exploiting the mutual and spatial information. In addition, we incorporate MRF into the fuzzy active contour model and get the resulting MRF-based energy function. Finally, we drive the associated first variation of the energy function to compute the fuzzy membership. Due to the introduction of MRF, the proposed MRF-based fuzzy active contour model is robust to the speckle noise in the SAR images and can achieve accurate change detection results. Experiments on four SAR image datasets demonstrate that the proposed MRF-based fuzzy active contour model is able to accurately segment the difference image and has better performance in comparison with other change detection techniques. INDEX TERMS fuzzy active contour model, Markov random field, change detection, synthetic aperture radar

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“…Remote sensing provides an appropriate data source for large-scale study ( Huang et al, 2020 , Liu et al, 2021 , Reichstein et al, 2019 ), such as environment study ( Gray et al, 2020 , Liu et al, 2020 , Su et al, 2021 , Xu et al, 2021 ), land-use/land-cover study ( Gao and O’Neill, 2020 , Jin et al, 2013 , Yang et al, 2018 ), vegetation monitoring ( Axelsson et al, 2021 , Mardian et al, 2021 , Taubert et al, 2018 ). Nowadays, the high-resolution remote sensing images show significant potentials to distinguish cars from the road, and make an objective statistic for counting the vehicle number on the road throughout the whole city ( Audebert et al, 2017 , Chen et al, 2016 , Eikvil et al, 2009 , Ji et al, 2020 , Leitloff et al, 2010 , Li et al, 2019 , Tang et al, 2017a , Tang et al, 2017b , Tanveer et al, 2020 , Tao et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

An investigation of traffic density changes inside Wuhan during the COVID-19 epidemic with GF-2 time-series images

Guo

et al. 2021

International Journal of Applied Earth Observation and Geoinfor

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In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow. Therefore, in this paper, we collected time-series high-resolution remote sensing images with the resolution of 1m acquired before, during and after Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and counted for the statistics of traffic density to reflect the changes of human transmissions in the whole period of Wuhan lockdown. Open Street Map was used to obtain observation road surfaces, and a vehicle detection method combing morphology filter and deep learning was utilized to extract vehicles with the accuracy of 62.56%. According to the experimental results, the traffic density of Wuhan dropped with the percentage higher than 80%, and even higher than 90% on main roads during city lockdown; after lockdown lift, the traffic density recovered to the normal rate. Traffic density distributions also show the obvious reduction and increase throughout the whole study area. The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.

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Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis

Cited by 24 publications

References 54 publications

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Fuzzy Active Contour Model With Markov Random Field for Change Detection

An investigation of traffic density changes inside Wuhan during the COVID-19 epidemic with GF-2 time-series images

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