Mapping eucalyptus species using worldview 3 and random forest

Recent advances in remote sensing technologies have allowed the development of new innovative methodologies to obtain geospatial information about Eucalyptus genus distribution. This is an important task for forest stakeholders due to the high presence of this genus in forest plantations worldwide. Therefore, the next step in research should focus on exploring remote sensing possibilities to discern between Eucalytpus species. It would be an important step forward in forest management since different Eucalyptus species present different characteristics and properties that imply different management plans and industrial usages. This study accomplish the classification of E. nitens and E. globulus, the most common Eucalyptus species in the Iberian Peninsula. Worldview-3 images and random forest are used in a forest area placed in Galicia (Northwest of Spain). The differentiation of Eucalyptus species resulted in a producer’s accuracy of 84% and a users’ accuracy of 70% for E. nitens, while for E. globulus accuracy metrics did not reach 70%. The most important bands in the classification were the coastal blue and the blue, followed by the red related ones. The resulting unequal accuracy metrics might be caused by an imbalanced presence of both species in the selected study area. Therefore, further studies might be developed in different locations

Automatic forest change detection through a bi-annual time series of satellite imagery: toward production of an integrated land cover map

Land cover mapping is fundamental for national and international agencies to monitor forest resources. However, monitoring forest disturbances by direct comparison of these maps poses several difficulties and challenges. As a result, different methodologies have been explored to detect forest disturbances. However, most of them cannot be fully integrated with land cover map production since they require additional input data, while others are not suitable for monitoring small land parcels. This study presents a methodology that fulfils the need to integrate land cover mapping with land cover change detection. Specifically, this methodology was designed to complement the Sentinel-2-based land cover mapping used in Galicia, northwest Spain, a region characterized by small land parceling. First, two previously obtained land cover maps from 2019 and 2020 were compared to identify all the pixels with potential land cover changes using QGIS. The behavior of spectral indexes in a time series were then analyzed to identify which of the previously identified pixels correspond to forest disturbances. This step was implemented in the software R. Using the Normalized Difference Vegetation Index (NDVI) to detect different land cover changes it was obtained an overall accuracy of 82%, considering the existence of varying phenologies, diverse topographic conditions, and areas with a high level of stand fragmentation. This study could help agencies that have already developed their own land cover maps to easily advance the integration of their maps with land cover change detection, since this technique can be applied with any land cover mapping methodology based on multitemporal analysis of satellite images, without the need for additional input data.Ministerio de Universidades | Ref. FPU19/02054Agencia Estatal de Investigación | Ref. PID2019-111581RB-I00Xunta de GaliciaUniversidade de Vigo/CISU

Forest cover mapping and Pinus species classification using very high-resolution satellite images and random forest

Advances in remote sensing technologies are generating new perspectives concerning the role of and methods used for National Forestry Inventories (NFIs). The increase in computation capabilities over the last several decades and the development of new statistical techniques have allowed for the automation of forest resource map generation through image analysis techniques and machine learning algorithms. The availability of large-scale data and the high temporal resolution that satellite platforms provide mean that it is possible to obtain updated information about forest resources at the stand level, thus increasing the quality of the spatial information. However, photointerpretation of satellite and aerial images is still the most common way that remote sensing information is used for NFIs or forest management purposes. This study describes a methodology that automatically maps the main forest covers in Galicia (Eucalyptus spp., conifers and broadleaves) using Worldview-2 and the random forest classifier. Furthermore, the method also evaluates the separate mapping of the three most abundant Pinus tree species in Galicia (Pinus pinaster, Pinus radiata and Pinus sylvestris). According to the results, Worldview-2 multispectral images allow for the efficient differentiation between the main forest classes that are present in Galicia with a very high degree of accuracy (91%) and ample spatial detail. Pinus species can also be efficiently differentiated (83%).Xunta de GaliciaAgencia Estatal de Investigación | Ref. PID2019-111581RB-I00Universidade de Vig

Challenges in automatic forest change reporting through land cover mapping

Up-to-date knowledge about changes in forest resources and their spatial distribution is essential for sustainable forest management. Therefore, monitoring of forest evolution is increasingly demanded by national and international agencies to design forestry policies and to track their progress. Annually updated land cover maps based on open access satellite imagery may serve as a primary tool for monitoring forest surface evolution over time. Spatially detailed information about forest change might be obtained by comparing land cover maps over time. This study aims to better understand the processes underlying pixels whose land cover changes from 1 year’s map to the next and to understand why errors occur. In this study, two annual land cover maps were produced using Sentinel-2 images and afterwards they were compared. The comparison was performed in terms of total surface occupied in each map by each of the classes (net comparison) and in terms of spatial agreement, comparing the results pixel to pixel. The study was performed for the entire region of Galicia (in the Northwest of Spain) for the years 2019 and 2020. Land cover maps obtained had overall accuracies of 82 and 85 per cent. Differences in the total surface of change were encountered when performing the net comparison and spatial agreement comparison. The detailed analysis performed in this study helps to better understand the processes underlying the maps’ discrepancies revealing the processes leading to wrongly identified forest changes. Future studies could aim to integrate this knowledge into the monitoring system to improve the ultimate usability of land cover maps to retrieve information about forest changes.Ministerio de Universidades | Ref. FPU19/02054Agencia Estatal de Investigación | Ref. PID2019-111581RB-I00Universidade de Vigo/CISUGXunta de Galici

Automatic tree detection and attribute characterization using portable terrestrial lidar

Currently, the implementation of portable laser scanners (PLS) in forest inventories is being studied, since they allow for significantly reduced field-work time and costs when compared to the traditional inventory methods and other LiDAR systems. However, it has been shown that their operability and efficiency are dependent upon the species assessed, and therefore, there is a need for more research assessing different types of stands and species. Additionally, a few studies have been conducted in Eucalyptus stands, one of the tree genus that is most commonly planted around the world. In this study, a PLS system was tested in a Eucalyptus globulus stand to obtain different metrics of individual trees. An automatic methodology to obtain inventory data (individual tree positions, DBH, diameter at different heights, and height of individual trees) was developed using public domain software. The results were compared to results obtained with a static terrestrial laser scanner (TLS). The methodology was able to identify 100% of the trees present in the stand in both the PLS and TLS point clouds. For the PLS point cloud, the RMSE of the DBH obtained was 0.0716, and for the TLS point cloud, it was 0.176. The RMSE for height for the PLS point cloud was 3.415 m, while for the PLS point cloud, it was 10.712 m. This study demonstrates the applicability of PLS systems for the estimation of the metrics of individual trees in adult Eucalyptus globulus stands.Agencia Estatal de Investigación | Ref. PID2019-111581RB-I00Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/02054Universidade de Vigo/CISU

Mapping feasibility for wood supply: a high-resolution geospatial approach to enhance sustainable forest management in Galicia (NW Spain)

The forest value chain is key to the European transition to a climate-neutral economy. Sustainable forest management is essential for this task. To plan sustainable forest management, it is essential to track forest resources in relation to their feasibility for wood supply. This means considering the constraints that may limit the incorporation of these resources into the forest value chain. Maps adapted to specific regional constraints and to the characteristics of specific forests are essential for performing sustainable forest management at a local scale. This study presents a methodology for the integrated analysis of geospatial data focused on classifying the land and the forest resources of a region according to their feasibility for wood supply. It produces maps of the feasibility for wood supply in an area and of the existing forest resources at a 10 m spatial resolution. This was done by integrating information about the legal and technical constraints present in the area according to decision rules. The land was classified into three classes: favorable, intermediate or unfavorable. Additionally, updated forest-oriented land cover maps were produced to analyze the feasibility for wood supply of the forest resources present in the region. It was found that 42% of the Eucalyptus spp., 48% of the conifers and 30% of the broadleaves in the study area were located in favorable areas. These maps would help in the quest for more sustainable forest management in the region and aid in boosting the competitiveness of the regional forest value chain.Agencia Estatal de Investigación | Ref. PID2019-111581RB-I00Ministerio de Universidades | Ref. FPU19/02054Universidade de Vig

Workflow to improve the forest management of Eucalyptus globulus stands affected by Gonipterus scutellatus in Galicia (Spain) using remote sansing and GIS

In Spain there are more than 500.000 ha. of Eucalyptus plantations. There represent 3,5 % of the national forest and the 25 % of the timber harvested. Galicia monocultures of Eucalyptus globulus Labill. plantations cover 177.679 ha. and mixed stands of eucalyptus cover 200.000 ha. more. This high productivity has been powered by the absence of pests and pathogens. However, since 1991 the health and productivity of these stands has been threatened by the Eucalyptus snout beetle, which causes a severe defoliation to eucalyptus stands in Galicia

Automatic differentiation of Eucalyptus species through Sentinel-2 images, Worldview-3 images and LiDAR data

Eucalyptus constitutes one of the most common tree genera used in forest plantations worldwide. In Europe, Eucalyptus trees are especially common in the Northwest of the Iberian Peninsula, E. nitens and E. globulus being the most commonly cultivated species. Each species presents particularities that lend to different exploitation strategies and industrial usages. Therefore, updated knowledge about the abundance and spatial distribution of the different species is important for forest planning. This is a special challenge for areas where forest land is highly fragmented. Remote sensing has been used to efficiently monitor the distribution of the Eucalyptus genera, however little research has been able to map specific Eucalyptus species. This study evaluates the efficiency of Sentinel-2 data, Worldview-3 images, and Airborne LiDAR data in the differentiation of E. nitens and E. globulus. Supervised classifications were performed using neural networks for these data sets both individually and in combination. The highest accuracies were obtained when using Sentinel-2 data in combination with LiDAR point clouds and when using Sentinel-2 data in a multitemporal approach. The best time of year to differentiate between the two species is during the emergence of spring shoots. Worldview-3 images have a moderate capacity to differentiate between the two species, although this is increased when textural metrics are included. This study can serve as the basis for generating Eucalyptus species distribution maps, which will allow for improved forest management and planning.Xunta de GaliciaAgencia Estatal de Investigación | Ref. PID2019-111581RB-I00Universidade de Vigo/CISU

Study Causation of Fires in Galicia: A Socioeconomic Perspective

Texto dispoñible en galego e españolMoitos traballos relacionan a acción do lume coas aptitudes físicas ou meteorolóxicas da zona, pero non inciden naqueles condicionantes de tipo socioeconómico que afectan á ocorrencia de incendios. A raíz deste oco na literatura, este traballo analiza o impacto de variables socioeconómicas que afectan á propagación dos incendios como son, por exemplo, a estrutura poboacional ou os usos agrícolas e gandeiros. Para isto, emprégase unha regresión lineal a partir de datos municipais, corrixindo os erros estándar dos coeficientes pola heteroxeneidade de cada municipio en Galicia, xunto cunha regresión Poisson. Entre os resultados obtidos máis relevantes destaca a importancia do papel da densidade poboacional, xunto cos diferentes usos agrarios e gandeiros da terra, así como a importancia da efectividade da organización xeográfica das medidas contra os lumes forestais, así como as dotacións económicas destinadas aos servizos preventivosMuchos trabajos relacionan la acción del fuego con las aptitudes físicas o meteorológicas de la zona, pero no inciden en aquellos condicionantes de tipo socioeconómicos que afectan a la ocurrencia de incendios. A raíz de este hueco en la literatura, este trabajo analiza el impacto de variables socioeconómicas que afectan a la propagación de los incendios como son, por ejemplo, la estructura poblacional o los usos agrícolas y ganaderos. Para ello, se emplea una regresión lineal a partir de datos municipales, corrigiendo los errores estándar de los coeficientes por la heterogeneidad de cada municipio en Galicia, junto con una regresión Poisson. Entre los resultados obtenidos más relevantes destaca la importancia del papel de la densidad poblacional, junto con los diferentes usos agrarios y ganaderos de la tierra, así como la importancia de la efectividad de la organización geográfica de las medidas contra los incendios, así como las dotaciones económicas destinadas a los servicios preventivosMany research papers analyze forest fires with respect to physical or meteorological characteristics of the burned areas, although not many pay attention to the socio-economic factors that affect the occurrence of forest fires. To fill in this gap in the literature, this work analyzes the impact of socio-economic variables that affect fires occurrence. In order to do so, we employ a baseline linear regression model correcting for heterogeneity across municipalities in Galicia and a Poisson regression. The most remarkable results show the importance of demographic variables, such as population dispersion, as well as the different agricultural land uses. In addition, we show the importance of the geographical component and organization of extinction activities, as well as the economic resources employed in preventionS

Study of land cover of Monte Forgoselo using Landsat Thematic Mapper 5 images (Galicia, NW Spain)

The aim of this study was to carry out a land cover classification of Monte Forgoselo. Remote sensing as a planning tool has been used, specifically, a satellite image Landsat 5. We perform a supervised digital classification of the maximum likelihood, minimum distance and parallelepipeds. The result has been the division of Monte Forgoselo into categories of types of vegetation, particularly in pine forest, scrub and grassland, reaching a Kappa coefficient of 0.9762 and an overall accuracy of 98.3586%, which means good reliability. In addition, we found that the use of such techniques can get to make a planing of this small rural area, focusing on forestry management. Furthermore, the use of remote sensing is considered viable in the performance of work with more consideration