Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery

Kaplan, Gordana; Avdan, Uğur

doi:10.5194/isprs-annals-iv-4-w4-271-2017

Cited by 79 publications

(51 citation statements)

References 29 publications

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“…The Normalized Difference Vegetation Index (NDVI), as well as similar vegetation indices have been developed as indicators of vegetation greenness or health in remotely-sensed imagery (Rouse et al 1974) and have been used to delineate vegetative from non-vegetative land covers (Jackson and Huete 1991;Kaplan and Avdan 2017). Even though these indices monitor forest states and canopy processes (Jinguo and Wei 2004;Huete 2012), none has been found to consistently aid in distinguishing between forest cover and other vegetative land cover classes.…”

Section: Index-based Approachesmentioning

confidence: 99%

Forest Cover Index for tree cover detection using Landsat-7 multispectral imagery

Feliciano-Cruz¹,

Becker²,

Lasko³

et al. 2019

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Forest cover monitoring using satellite imagery is important to U.S. military terrain analysis. Mobility models, cover and concealment, and precise surface feature delineation all rely on an accurate forest/nonforest cover layer. However, the challenge remains in distinguishing trees from other vegetative land covers when relying on single date imagery. A Forest Cover Index (FCI) algorithm was previously developed on Worldview-2 imagery and was designed to separate forest cover from nonforest covered areas. In this research, the FCI algorithm was applied to Landsat-7 imagery using the analogous red (636-673 nanometers (nm)) and near-infrared bands (851-878 nm) from a peak summer image on 16 August 2012. The results obtained with Landsat-7 imagery proved satisfactory with an overall accuracy (tree cover versus non-tree cover) of > 83 percent, according to testing with two different accuracy assessment tests. The application of the FCI to Landsat-7 imagery broadens the applicability of the FCI to freely available imagery.

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Section: Index-based Approachesmentioning

confidence: 99%

Forest Cover Index for tree cover detection using Landsat-7 multispectral imagery

Feliciano-Cruz¹,

Becker²,

Lasko³

et al. 2019

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“…Most previous studies have primarily focused on grass chlorophyll and nitrogen content [40], assessing rangeland quality [37], discriminating C 3 and C 4 grass species [38], and mapping and monitoring wetlands [41] and tree canopy cover [42], and have succeeded in showing the potential of Sentinel-2. However, to the best of our knowledge, its usefulness has not yet been evaluated for forest AGB estimation and mapping and neither evaluation has yet been conducted in the challenging and complex forest regions of Nepal, where saturation is a serious problem.…”

Section: Introductionmentioning

confidence: 99%

Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data

2018

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Accurate assessment of above-ground biomass (AGB) is important for the sustainable management of forests, especially buffer zone (areas within the protected area, where restrictions are placed upon resource use and special measure are undertaken to intensify the conservation value of protected area) areas with a high dependence on forest products. This study presents a new AGB estimation method and demonstrates the potential of medium-resolution Sentinel-2 Multi-Spectral Instrument (MSI) data application as an alternative to hyperspectral data in inaccessible regions. Sentinel-2 performance was evaluated for a buffer zone community forest in Parsa National Park, Nepal, using field-based AGB as a dependent variable, as well as spectral band values and spectral-derived vegetation indices as independent variables in the Random Forest (RF) algorithm. The 10-fold cross-validation was used to evaluate model effectiveness. The effect of the input variable number on AGB prediction was also investigated. The model using all extracted spectral information plus all derived spectral vegetation indices provided better AGB estimates (R 2 = 0.81 and RMSE = 25.57 t ha −1 ). Incorporating the optimal subset of key variables did not improve model variance but reduced the error slightly. This result is explained by the technically-advanced nature of Sentinel-2, which includes fine spatial resolution (10, 20 m) and strategically-positioned bands (red-edge), conducted in flat topography with an advanced machine learning algorithm. However, assessing its transferability to other forest types with varying altitude would enable future performance and interpretability assessments of Sentinel-2.

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“…Scatter plot of the spectral space NDVI is similar to that of the spectral space between LST and NDVI, by in-deep analysis (Yao, Qin, Zhu, & Yang, 2008). In comparison with the latest Landsat OLI/TIRS, Sentinel-2 has a better spatial resolution, better spectral resolution in the near infrared region, but does not offer thermal data (Kaplan & Avdan, 2017). The LST was produced from Landsat-8 OLI to better define forest coverage.…”

Section: Introductionmentioning

confidence: 90%

Estimation of Forest Coverage in Northern Region of Mongolia Using Sentinel and Landsat Data

Norovsuren

Renchin

Tseveen

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper aims to apply Forest Index (FI) and to determine forest coverage in the study area. The study area (49° 15ʹ to 49° 10ʹ N and 104° 05ʹ to 104° 15ʹ E) is located in the northern region of Mongolia and consist of mixed forest. Larch forest (86.12%) is dominating in the study area. The Sentinel-2 satellite data for the years 2015–2019 were used in the research. The land surface temperature (LST) was produced from Landsat-8 OL. FI methodology was applied for the Sentinel data in order to estimate larch forest coverage. The output map of forest coverage was compared with ground truth measurements and thematic map. The agreement between FI map and ground measurement was 85%. LST from Landsat and FI from Sentinel were sampled in to same size. The relationship between LST (Landsat-8) and FI (Sentinel-2) was reasonable (R = 0.5). FI index and LST is applicable for different forest type in the region.

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Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery

Cited by 79 publications

References 29 publications

Forest Cover Index for tree cover detection using Landsat-7 multispectral imagery

Forest Cover Index for tree cover detection using Landsat-7 multispectral imagery

Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data

Estimation of Forest Coverage in Northern Region of Mongolia Using Sentinel and Landsat Data

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