Treefall Gap Mapping Using Sentinel-2 Images

Iván, Barton; Király, Gergely; Czimber, Kornél; Hollaus, Markus; Pfeifer, Norbert

doi:10.3390/f8110426

Cited by 14 publications

(7 citation statements)

References 41 publications

(50 reference statements)

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“…Optical satellite sensors have been widely used to estimate and model forest attributes, such as forest area and character [11,12], volume [13][14][15], and above ground biomass (AGB) See survey by [16] due to their sensitivity to vegetation response observed in multispectral bands. However, optical sensors are obstructed by atmospheric conditions and cloud cover, which reduces their potential operational use especially in cloudy prone areas.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

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Section: Introductionmentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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“…However, there is no literature analyzing small plot sizes and their significance. Some studies analyzing medium-sized plots (28.4 ha) report overestimation [79]. Moreover, the methodologies are tedious and complicated, even combining three types of remote sensing data [80], and are hardly applicable by the average forest user.…”

Section: Discussionmentioning

confidence: 99%

Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control

López-Amoedo¹,

Álvarez

Lorenzo

et al. 2021

Remote Sensing

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Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.

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“…It is a tool available to analyze satellite information for free, specialized for the Sentinel series, has a performance comparable to that attained with other software's (e.g., QGIS or ENVI) [42], and has a discussion forum for consultations [43]. For these reasons, it seems to be justified to explore the potential of Sentinal-2 data for studies in forest monitoring [24] such as forest succession [2] and wildfire control [44], forest classification [11], forest management [45], and biomass estimation [46].…”

Section: The Potential Of Image Classification and The Sentinel-2 Satmentioning

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

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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.

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Treefall Gap Mapping Using Sentinel-2 Images

Cited by 14 publications

References 41 publications

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control

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

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