Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Campón, Laura Fragoso; Quirós, Elia; Mora, Julián; Gutiérrez, José Antonio; Durán‐Barroso, Pablo

doi:10.3390/proceedings2201280

Cited by 11 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The studies on the implementation of Sentinel-2 in land cover/use monitoring have been reported in many countries across the world, including Europe, Asia, America, and Africa. Many studies that have been reported in developed countries, especially in Europe, integrated Sentinel-2 data with contemporary datasets such as LiDAR and UAVs datasets [86,117,138], while studies, based in developing countries, combined Sentinel-2 data with other open-source data such as Sentinel-1 SAR data and Landsat [139,140].…”

Section: Specific Applications Of Sentinel-2 In Land Cover/land Use Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

View full text Add to dashboard Cite

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

show abstract

Section: Specific Applications Of Sentinel-2 In Land Cover/land Use Monitoringmentioning

confidence: 99%

“…For example, Puliti, et al [146] reported the use of Sentinel-2 in forest inventories in Norway. The increasing combination of UAVs [146,147] and LiDAR [86,117] datasets with Sentinel-2 has shown promise and was highly accurate in describing different forest attributes.…”

Section: Sentinel-2 For Forest Monitoringmentioning

confidence: 99%

Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Among the vast array of kinds of remotely sensed data, multispectral optical images are still considered the most feasible and cost-effective option for monitoring extensive forest areas [14]. In recent years, multiple approaches for mapping forest dynamics have been developed using time series (TS) of multispectral optical satellite data that are freely available online such as in Landsat, MODIS, and, since 2015, Sentinel-2 data [14][15][16][17][18][19]. The analysis of changes in spectral signatures from TS of satellite images is well-documented as an efficient method for mapping temporal changes in forest environments [18,20].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series

et al. 2020

View full text Add to dashboard Cite

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.

show abstract

“…The studies based on forest species classification involving Sentinel-2 images are still recent [23,33,[83][84][85][86]. The classification with S2 images of forest tree species in southwestern France obtained an overall accuracy above 90%, for plantations such as aspen (Populus tremula) and red oak (Quercus rubra).…”

Section: Maps Of Forest Lclu and Accuracy Assessmentmentioning

confidence: 99%

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Isbaex¹,

Coelho²

2021

Forest Biomass - From Trees to Energy

View full text Add to dashboard Cite

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

show abstract

Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Cited by 11 publications

References 16 publications

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Sentinel-2 Data for Land Cover/Use Mapping: A Review

A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Contact Info

Product

Resources

About