| |
Records |
Links |
|
Author |
Baselli, G.; Contreras, F.; Lillo, M.; Marin, M.; Carrasco, R.A. |
|
| |
Title |
Optimal decisions for salvage logging after wildfires |
Type |
|
| |
Year |
2020 |
Publication |
Omega-International Journal Of Management Science |
Abbreviated Journal |
Omega-Int. J. Manage. Sci. |
|
| |
Volume |
96 |
Issue |
|
Pages |
9 pp |
|
| |
Keywords |
Salvage logging; Forest harvesting; Wildfires; Workforce allocation |
|
| |
Abstract |
Strategic, tactical, and operational harvesting plans for the forestry and logging industry have been widely studied for more than 60 years. Many different settings and specific constraints due to legal, environmental, and operational requirements have been modeled, improving the performance of the harvesting process significantly. During the summer of 2017, Chile suffered from the most massive wildfires in its history, affecting almost half a million hectares, of which nearly half were forests owned by medium and small forestry companies. Some of the stands were burned by intense crown fires, which always spread fast, that burned the foliage and outer layer of the bark but left standing dead trees that could be salvage harvested before insect and decay processes rendered them unusable for commercial purposes. Unlike the typical operational programming models studied in the past, in this setting, companies can make insurance claims on part or all of the burnt forest, whereas the rest of the forest needs to be harvested before it loses its value. This problem is known as the salvage logging problem. The issue also has an important social component when considering medium and small forestry and logging companies: most of their personnel come from local communities, which have already been affected by the fires. Harvesting part of the remaining forest can allow them to keep their jobs longer and, hopefully, leave the company in a better financial situation if the harvesting areas are correctly selected. In this work, we present a novel mixed-integer optimization-based approach to support salvage logging decisions, which helps in the configuration of an operational-level harvesting and workforce assignment plan. Our model takes into account the payment from an insurance claim as well as future income from harvesting the remaining trees. The model also computes an optimal assignment of personnel to the different activities required. The objective is to improve the cash position of the company by the end of the harvest and ensure that the company is paying all its liabilities and maintaining personnel. We show how our model performs compared to the current decisions made by medium and small-sized forestry companies, and we study the specific case of a small forestry company located in Cauquenes, Chile, which used our model to decide its course of action. (C) 2019 Elsevier Ltd. All rights reserved. |
|
| |
Address |
[Baselli, Gianluca; Contreras, Felipe; Lillo, Matias; Marin, Magdalena; Carrasco, Rodrigo A.] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: gbaselli@alumnos.uai.cl; |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Pergamon-Elsevier Science Ltd |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0305-0483 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000541944700003 |
Approved |
|
|
| |
Call Number |
UAI @ eduardo.moreno @ |
Serial |
1186 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miranda, A.; Mentler, R.; Moletto-Lobos, I.; Alfaro, G.; Aliaga, L.; Balbontin, D.; Barraza, M.; Baumbach, S.; Calderon, P.; Cardenas, F.; Castillo, I.; Contreras, G.; de la Barra, F.; Galleguillos, M.; Gonzalez, M.E.; Hormazabal, C.; Lara, A.; Mancilla, I.; Munoz, F.; Oyarce, C.; Pantoja, F.; Ramirez, R.; Urrutia, V. |
|
| |
Title |
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile |
Type |
|
| |
Year |
2022 |
Publication |
Earth System Science Data |
Abbreviated Journal |
Earth Syst. Sci. Data |
|
| |
Volume |
14 |
Issue |
8 |
Pages |
3599-3613 |
|
| |
Keywords |
TIME-SERIES; LAND-USE; ALGORITHM; WILDFIRES; IMPACTS; RDNBR |
|
| |
Abstract |
Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic databases. High-quality information can help to transform evidence into decision-making in the context of rapidly changing landscapes, particularly considering that geographical and temporal patterns of fire regimes and their trends vary locally over time. Global fire scar products at low spatial resolutions are available, but high-resolution wildfire data, especially for developing countries, are still lacking. Taking advantage of the Google Earth Engine (GEE) big-data analysis platform, we developed a flexible workflow to reconstruct individual burned areas and derive fire severity estimates for all reported fires. We tested our approach for historical wild-fires in Chile. The result is the Landscape Fire Scars Database, a detailed and dynamic database that reconstructs 8153 fires scars, representing 66.6% of the country's officially recorded fires between 1985 and 2018. For each fire event, the database contains the following information: (i) the Landsat mosaic of pre- and post-fire images; (ii) the fire scar in binary format; (iii) the remotely sensed estimated fire indexes (the normalized burned ratio, NBR, and the relative delta normalized burn ratio, RdNBR); and two vector files indicating (iv) the fire scar perimeter and (v) the fire scar severity reclassification, respectively. The Landscape Fire Scars Database for Chile and GEE script (JavaScript) are publicly available. The framework developed for the database can be applied anywhere in the world, with the only requirement being its adaptation to local factors such as data availability, fire regimes, land cover or land cover dynamics, vegetation recovery, and cloud cover. The Landscape Fire Scars Database for Chile is publicly available in https://doi.org/10.1594/PANGAEA.941127 (Miranda et al., 2022). |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1866-3508 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000838024900001 |
Approved |
|
|
| |
Call Number |
UAI @ alexi.delcanto @ |
Serial |
1667 |
|
| Permanent link to this record |
