Articles containing the keyword 'Sentinel-2'

Physically-based reflectance models offer a robust and transferable method to assess biophysical characteristics of vegetation in remote sensing. Forests exhibit explicit structure at many scales, from shoots and branches to landscape patches, and hence present a specific challenge to vegetation reflectance modellers. To relate forest reflectance with its structure, the complexity must be parametrised leading to an increase in the number of reflectance model inputs. The parametrisations link reflectance simulations to measurable forest variables, but at the same time rely on abstractions (e.g. a geometric surface forming a tree crown) and physically-based simplifications that are difficult to quantify robustly. As high-quality data on basic forest structure (e.g. tree height and stand density) and optical properties (e.g. leaf and forest floor reflectance) are becoming increasingly available, we used the well-validated forest reflectance and transmittance model FRT to investigate the effect of the values of the “uncertain” input parameters on the accuracy of modelled forest reflectance. With the state-of-the-art structural and spectral forest information, and Sentinel-2 Multispectral Instrument imagery, we identified that the input parameters influencing the most the modelled reflectance, given that the basic forestry variables are set to their true values and leaf mass is determined from reliable allometric models, are the regularity of the tree distribution and the amount of woody elements. When these parameters were set to their new adjusted values, the model performance improved considerably, reaching in the near infrared spectral region (740–950 nm) nearly zero bias, a relative RMSE of 13% and a correlation coefficient of 0.81. In the visible part of the spectrum, the model performance was not as consistent indicating room for improvement.

• Halme, VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT Espoo, Finland https://orcid.org/0000-0002-7517-6663 E-mail: eelis.halme@vtt.fi
• Mõttus, VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT Espoo, Finland https://orcid.org/0000-0002-2745-1966 E-mail: matti.mottus@vtt.fi

Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed.

• Marcelli, University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems, Viterbo, Italy; Fondazione Edmund Mach, Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, Italy E-mail: agnese.marcelli@student.unisi.it
• Mattioli, University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems, Viterbo, Italy; CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: walter.mattioli@crea.gov.it
• Puletti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: nicola.puletti@crea.gov.it
• Chianucci, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: fchianucci@gmail.com
• Gianelle, Fondazione Edmund Mach, Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, Italy E-mail: damiano.gianelle@fmach.it
• Grotti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy; University of Roma La Sapienza, Department of Architecture and Design, Rome, Italy E-mail: mirkogrotti@gmail.com
• Chirici, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: gherardo.chirici@unifi.it
• D' Amico, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: giovanni.damico@unifi.it
• Francini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy; University of Molise, Department of Agricultural, Environmental and Food Sciences, Campobasso, Italy E-mail: saverio.francini@gmail.com
• Travaglini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: davide.travaglini@unifi.it
• Fattorini, University of Siena, Department of Economics and Statistics, Siena, Italy E-mail: lorenzo.fattorini@unisi.it
• Corona, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: piermaria.corona@crea.gov.it

We investigated if coarse-resolution satellite data from the MODIS sensor can be used for regional monitoring of insect disturbances in Fennoscandia. A damage detection method based on z-scores of seasonal maximums of the 2-band Enhanced Vegetation Index (EVI2) was developed. Time-series smoothing was applied and Receiver Operating Characteristics graphs were used for optimisation. The method was developed in fragmented and heavily managed forests in eastern Finland dominated by Scots pine (Pinus sylvestris L.) (pinaceae) and with defoliation of European pine sawfly (Neodiprion sertifer Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly (Diprion pini L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch (Betula pubescens ssp. Czerepanovii N.I. Orlova) forests in northern Sweden, infested by autumnal moth (Epirrita autumnata Borkhausen) and winter moth (Operophtera brumata L.). In Finland, detection accuracies were fairly low with 50% of the damaged stands detected, and a misclassification of healthy stands of 22%. In areas with long outbreak histories the method resulted in extensive misclassification. In northern Sweden accuracies were higher, with 75% of the damage detected and a misclassification of healthy samples of 19%. Our results indicate that MODIS data may fail to detect damage in fragmented forests, particularly when the damage history is long. Therefore, regional studies based on these data may underestimate defoliation. However, the method yielded accurate results in homogeneous forest ecosystems and when long-enough periods without damage could be identified. Furthermore, the method is likely to be useful for insect disturbance detection using future medium-resolution data, e.g. from Sentinel‑2.

• Olsson, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: per-ola.olsson@nateko.lu.se
• Kantola, Texas A & M University, Knowledge Engineering Laboratory, Department of Entomology, College Station, TX, USA E-mail: tuula.kantola@helsinki.fi
• Lyytikäinen-Saarenmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: paivi.lyytikainen-saarenmaa@helsinki.fi
• Jönsson, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: anna_maria.jonsson@nateko.lu.se
• Eklundh, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: lars.eklundh@nateko.lu.se