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Articles containing the keyword 'Sentinel-2'

Category: Research article

article id 10247, category Research article
Agnese Marcelli, Walter Mattioli, Nicola Puletti, Francesco Chianucci, Damiano Gianelle, Mirko Grotti, Gherardo Chirici, Giovanni D' Amico, Saverio Francini, Davide Travaglini, Lorenzo Fattorini, Piermaria Corona. (2020). Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information. Silva Fennica vol. 54 no. 2 article id 10247. https://doi.org/10.14214/sf.10247
Keywords: national forest inventories; Sentinel-2; design-based inference; first-phase tessellation stratified sampling; regression estimator; second-phase stratified sampling; simulation study
Highlights: A two-phase sampling for large-scale assessment of fast-growing forest crops is developed; Vegetation indices from Sentinel-2 are exploited in a linear regression estimator; The linear regression estimator turns out to be better than the estimator based on the sole sample information; The approach represents a reference for supporting outside-forest resource monitoring and assessment.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 (email)
  • 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
article id 1495, category Research article
Per-Ola Olsson, Tuula Kantola, Päivi Lyytikäinen-Saarenmaa, Anna Maria Jönsson, Lars Eklundh. (2016). Development of a method for monitoring of insect induced forest defoliation – limitation of MODIS data in Fennoscandian forest landscapes. Silva Fennica vol. 50 no. 2 article id 1495. https://doi.org/10.14214/sf.1495
Keywords: remote sensing; insect defoliation detection; coarse-resolution; EVI2; z-score; Sentinel-2
Highlights: We developed and tested a method to monitor insect induced defoliation in forests based on coarse-resolution satellite data (MODIS); MODIS data may fail to detect defoliation in fragmented landscapes, especially if defoliation history is long. More homogenous forests results in higher detection accuracies; The method may be applied to future coarse and medium-resolution satellite data with high temporal resolution.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 (email)
  • 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

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