Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-off image for LULC classification in a heterogeneous landscape of West Africa

Asare, Yaw Mensah; Forkuo, Eric Kwabena; Forkuor, Gerald; Thiel, Michael

doi:10.1080/01431161.2019.1693076

Cited by 17 publications

(15 citation statements)

References 53 publications

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“…The later Landsat-8 mission collected over 3. 35 Petabyte of scenes over the course of a single year in 2017 [12]. These data collections hold great potential to improve our monitoring efforts of mangrove ecosystems and study changes over time.…”

Section: Introductionmentioning

confidence: 99%

“…However, in discrete land cover classification exercises, this practice remains less common, including in combination with the GEE platform [33,34]. Current studies tend to focus on gap-filling based on spatially neighboring pixels [35,36], spectral similarity, and/or multi-sensor (source) data fusion [34,37,38], rather than temporal integration. As such, few land use studies have taken full advantage of temporal dependencies to reduce both information gaps and inconsistent land use transitions [13,[39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

et al. 2020

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Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

et al. 2020

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“…In this context, the ability to supplement satellite data by means of other datasets is also of paramount importance. One way to fill the gaps left by cloud coverage is to complement satellite data using other sensors [51]. Therefore, conversion functions need to be developed to convert spectral data from Landsat 8, Sentinel 2, and other sensors to mitigate inconsistencies in surface reflectance and vegetation indices due to spatial resolution and spectra configuration [52].…”

Section: Methods For Improving the Usability The Sentinel 2 And Landsat 8 Imagesmentioning

confidence: 99%

Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

et al. 2021

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Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.

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“…At present, the most commonly used method of classification accuracy evaluation is the confusion matrix method, which is an error matrix, which is expressed by the number of correct classifications and the number of misclassifications in the experimental samples. In the confusion matrix, the overall classification accuracy (OA) and Kappa coefficient are mainly used to verify the classification results and evaluate the accuracy [23,29].…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Ecological Security Assessment Based on Remote Sensing and Landscape Ecology Model

Liu

Zhang

et al. 2021

Journal of Sensors

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The various ecological processes of human beings are not only restricted by the landscape pattern on the regional scale but also affect the local and regional landscape together with global climate change. To date, most of the research on ecological security is based on the pressure-state-response (PSR) model, while there were a few studies based on the landscape ecology model approach. In addition, there has been little literature focus on the dynamic change process of ecological security, especially the simulation and prediction of the future development trend of ecological security. The purpose of this research is to establish a landscape ecological security evaluation method based on grid division, be aimed at breaking the inherent drawbacks of the administrative region as a unit mode approach, anticipated to better reflect the landscape ecological security status of the study area. A complex framework was constructed by integrating random forest algorithm, Fishnet model, landscape ecology model, and CA-Markov model. Multitemporal remote sensing data were selected as a data source, and land use maps of the study area were obtained through the random forest machine learning algorithm firstly. And then, the study area is divided into 307 grids of 2 km × 2 km using the Fishnet model. Next, the landscape disturbance index, landscape vulnerability index, and landscape loss index are used on the grid scale to establish a landscape ecological security evaluation model. Finally, ecological security assessment of Zhengzhou city was carried out, and the distribution map of the landscape ecological status in 1986, 1996, 2006, 2016, and predicted for 2026 was obtained. The results of the study showed that, as time goes by, the areas with high ecological safety gradually decrease. It is predicted that by 2026, the ecological security level of Zhengzhou will be dominated by lower ecological security areas. The research results can provide basic information and decision support for government agencies and land use planners to ensure responsible and sustainable development of the urban environment and ecology.

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Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-off image for LULC classification in a heterogeneous landscape of West Africa

Cited by 17 publications

References 53 publications

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

Ecological Security Assessment Based on Remote Sensing and Landscape Ecology Model

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