High resolution lidar mapping of Belgian historical forests reveals unique pre-historic and Roman landscape features

The constellation of two Sentinel-1 satellites provides an unprecedented coverage of Synthetic Aperture Radar (SAR) data at high spatial (20 m) and temporal (2 to 6 days over Europe) resolution. The availability of dense time series enables the analysis of the SAR temporal signatures and exploitation of these signatures for classification purposes. Frequent backscatter observations allow derivation of temporally filtered time series that reinforce the effect of changes in vegetation phenology by limiting the influence of short-term changes related to environmental conditions. Recent studies have already shown the potential of multitemporal Sentinel-1 data for forest mapping, forest type classification (coniferous or broadleaved forest) as well as for derivation of phenological variables at local to national scales. In the present study, we tested the viability of a recently published multi-temporal SAR classification method for continental scale forest mapping by applying it over Europe and evaluating the derived forest type and tree cover density maps against the European-wide Copernicus High Resolution Layers (HRL) forest datasets and national-scale forest maps from twelve countries. The comparison with the Copernicus HRL datasets revealed high correspondence over the majority of the European continent with overall accuracies of 86.1% and 73.2% for the forest/non-forest and forest type maps, respectively, and a Pearson correlation coefficient of 0.83 for tree cover density map. Moreover, the evaluation of both datasets against the national forest maps showed that the obtained accuracies of Sentinel-1 forest maps are almost within range of the HRL datasets. The Sentinel-1 forest/non-forest and forest type maps obtained average overall accuracies of 88.2% and 82.7%, respectively, as compared to 90.0% and 87.2% obtained by the Copernicus HRL datasets. This result is especially promising due to the facts that these maps can be produced with a high degree of automation and that only a single year of Sentinel-1 data is required as opposed to the Copernicus HRL forest datasets that are updated every three years.

Fernerkundliche Waldflächenerfassung im Kontext internationaler Umweltabkommen

Building on the positive experiences with open forest map data in Scandinavia, it is evident that extending a similar solution globally has the potential to revolutionize forest management and business on a worldwide scale. While forest management in the Nordic countries can certainly be enhanced, the most rapid solution for climate change mitigation involves providing other nations with opportunities akin to those that have benefited the forestry sector in Sweden during the initial stages of digitalization. In the proposed project, we aim to create a novel hierarchical decision-making system for efficient forest mapping, leveraging a diverse range of remote sensing data sources with varying resolutions. This hierarchical system will be developed using state-of-the-art AI methods, complemented by results from traditional computer vision techniques such as texture analysis, saliency, and probabilistic object representation. A significant strength of the project lies in using the forest data and maps of Sweden and Finland as test beds to benchmark the methodology developed. We are confident that this project will make substantial contributions to climate change mitigation, biodiversity enhancement, and other societal values. Moreover, it aims to foster the creation of new business models by developing an innovative methodology for the next generation of forest maps. Our vision is to adapt the success story of open forest map data from the Nordic region globally, harnessing the power of advanced AI technology and integrated use of remote sensing and field data.

The present work was carried out in the framework of the Forest Territorial Plans (PFT) of Piemonte region (North-western Italy). These Plans considered the different components of the territory (forests, grasslands, natural vegetation) analysed by means of a forest inventory and land cover maps at 1:25.000 scale. The general aim of the project is to integrate the traditional survey with the Earth Observation techniques, developing a tool useful to produce land cover and forest maps and providing a reference base for monitoring the future changes. A part of this project focused on the possible use of high-resolution satellite images (Ikonos II) for verifying and updating the forest maps. In order to achieve such a goal a multispectral Ikonos II image was acquired on the study area, an hilly terrain with highly fragmented forest cover. A first phase of ground truth definition by means of specific surveys aiming at recognising the different spectral classes was then carried out. The following steps were the developing of a visual interpretation of the image and a supervised classification applying an object-based approach. The results of these procedures were compared with the forest map defined by means of aerial photos interpretation and ground survey. The experimental application of high-resolution satellite images to forest mapping showed that the different homogeneous areas in the image corresponded to the main differences in cover density and dominant tree species composition (oak, chestnut, etc.), but sometimes they also reflected differences in the average age and sylvicultural system (coppice, high forest) of the forest stands. Another important conclusion of the study is that per pixel classification methods are not suitable for automated analysis of Ikonos images and therefore a new approach, based on a preliminary segmentation of the image, is needed.

On mountain slopes, forest can play an important role to protect human lives and facilities against rockfalls. Silvicultural strategies and interventions to maintain or improve protection forest structures are of first interest. Up to now, regional mapping of protection forest does not exist in France. The objectives of the study are to develop decision support tools for rockfall protection forest management. This paper present a Geographic Information Systems (GIS) based model which automatically maps forests with a protection function against rockfall hazard. This model, called RollFree, is a statistical based model which calculated maximum run out zone of rockfall based on the energy line principle. RollFree presents the advantages of a fast computational time and need only few input parameters such as a DEM, a map of the issues and a map of the forest cover. In the French region of Rhone-Alpes, results showed that forests with a protection function against rockfall can represent up to 30 % of all the forests in the district. These maps can be used as a decisional tool by practitioner to develop management strategies in order to define priority zones for more accurate rockfall mapping and adapted protection forest management.

The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D), spatially accurate information from forest resources using active RS methods. In practical applications, mainly 3D information produced by airborne laser scanning (ALS) has opened up groundbreaking potential in natural resource mapping and monitoring. In addition to ALS, new satellite radars are also capable of acquiring spatially accurate 3D information. The main objectives of the present study were to develop 3D RS methodologies for large-area forest mapping and monitoring applications. In substudy I, we aim to map harvesting sites, while in substudy II, we monitor changes in the forest canopy structure. In studies III-V, efficient mapping and monitoring applications were developed and tested. In substudy I, we predicted plot-level thinning maturity within the next 10-year planning period. Stands requiring immediate thinning were located with an overall accuracy of 83%-86% depending on the prediction method applied. The respective prediction accuracy for stands reaching thinning maturity within the next 10 years was 70%-79%. Substudy II addressed natural disturbance monitoring that could be linked to forest management planning when an ALS time series is available. The accuracy of the damaged canopy cover area estimate varied between -16.4% to 5.4%. Substudy II showed that changes in the forest canopy structure can be monitored with a rather straightforward method by contrasting bi-temporal canopy height models. In substudy III, we developed a RS-based forest inventory method where single-tree RS is used to acquire modelling data needed in area-based predictions. The method uses ALS data and is capable of producing accurate stand variable estimates even at the sub-compartment level. The developed method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized. The method is especially suitable for largearea biomass or stem volume mapping. Based on substudy IV, the use of stereo synthetic aperture radar (SAR) satellite data in the prediction of plotlevel forest variables appears to be promising for large-area applications. In the best case, the plot-level stem volume (VOL) was predicted with a relative error (RMSE%) of 34.9%. Typically, such a high level of prediction accuracy cannot be obtained using spaceborne RS data. Then, in substudy V, we compared the aboveground biomass and VOL estimates derived by radargrammetry to the ALS estimates. The difference between the estimation accuracy of ALS–based and TerraSAR X–based features was smaller than in any previous study in which ALS and different kinds of SAR materials have been compared. In this thesis, forest mapping and monitoring applications using active 3D RS were developed. Spatially accurate 3D RS enables the mapping of harvesting sites, the monitoring of changes in the canopy structure and even the making of a fully RS-based forest inventory. ALS is carried out at relatively low altitudes, which makes it relatively expensive per area unit, and other RS materials are still needed. Spaceborne stereo radargrammetry proved to be a promising technique to acquire additional 3D RS data efficiently as long as an accurate digital terrain model is available as a ground-surface reference.

Accurately monitoring forest dynamics in the tropical regions is essential for ecological studies and forest management. In this study, images from phase-array L-band synthetic aperture radar (PALSAR), PALSAR-2, and Landsat in 2006–2010 and 2015 were combined to identify tropical forest dynamics on Hainan Island, China. Annual forest maps were first mapped from PALSAR and PALSAR-2 images using structural metrics. Those pixels with a high biomass of sugarcane or banana, which are widely distributed in the tropics and subtropics and have similar structural metrics as forests, were excluded from the SAR-based forest maps by using phenological metrics from time series Landsat imagery. The optical–SAR-based forest maps in 2010 and 2015 had high overall accuracies (OA) of 92–97% when validated with ground reference data. The resultant forest map in 2010 shows good spatial agreement with public optical-based forest maps (OA = 88–90%), and the annual forest maps (2007–2010) were spatiotemporally consistent and more accurate than the PALSAR-based forest map from the Japan Aerospace Exploration Agency (OA = 82% in 2010). The areas of forest gain, loss, and net change on Hainan Island from 2007 to 2015 were 415 000 ha (+2.17% yr–1), 179 000 ha (–0.94% yr –1), and 236 000 ha (+1.23% yr–1), respectively. About 95% of forest gain and loss occurred in those areas with an elevation less than 400 m, where deciduous rubber, eucalyptus plantations, and urbanization expanded rapidly. This study demonstrates the potential of PALSAR/PALSAR-2/Landsat image fusion for monitoring annual forest dynamics in the tropical regions.

A national forest policy requires a solid information base on forests for three reasons. First, to fulfils international standards for the monitoring of forests, especially for carbon storage and biodiversity (Helsinki criteria, Kyoto protocol); second, to identify areas that require political action, including monitoring of the success of measures taken; and third, to provide a sound and reliable information base for private decision-makers about the expected wood supply. Presently, the Turkish national forest service is launching an initiative towards a national forest inventory (NFI) initiated by Prof. Asan, University of Istanbul. During an exchange programme, some initial considerations for an NFI system an NFI were worked up and will be presented in this paper. These initial considerations focus on sampling design, the use of remote sensing and the use of the available information on Turkish forests. First, an overview on the present inventory methods on the forest management level will be given in order to analyse (i) our present knowledge and deficiencies of the information on Turkish forests and (ii) what use could be made of this information in the framework of a National Forest Inventory concept. Furthermore, natural conditions and the possibility of using remote sensing will be analysed. Initial concepts to use the regional forest maps (generalised maps in the scale 1:100,000, based on maps from forest management inventories) and remote sensing will be presented. The regional forest maps and mapping based on remote sensing using optical high-reso.lution satellite data (TM/SPOT/IRS1/ASTER) are to be used with the statistical pre-stratification method to enable different sampling densities and identify non-forest sampling plots. A forest map based on remote sensing is included in the concept to supplement the statistical information. The use of very high-resolution satellite data (e.g. IKONOS) will be discussed. Finally, open questions for preparatory studies will be identified.

Abstract. Annual forest maps at a high spatial resolution are necessary for forest management and conservation. Large uncertainties remain in existing forest maps because of different forest definitions, satellite datasets, in situ training datasets, and mapping algorithms. In this study, we generated annual maps of forest and evergreen forest at a 30 m resolution in the contiguous United States (CONUS) during 2015–2017 by integrating microwave data (Phased Array type L-band Synthetic Aperture Radar – PALSAR-2) and optical data (Landsat) using knowledge-based algorithms. The resultant PALSAR-2/Landsat-based forest maps (PL-Forest) were compared with five major forest datasets from the CONUS: (1) the Landsat tree canopy cover from the Global Forest Watch dataset (GFW-Forest), (2) the Landsat Vegetation Continuous Field dataset (Landsat VCF-Forest), (3) the National Land Cover Database 2016 (NLCD-Forest), (4) the Japan Aerospace Exploration Agency forest maps (JAXA-Forest), and (5) the Forest Inventory and Analysis (FIA) data from the U.S. Department of Agriculture (USDA) Forest Service (FIA-Forest). The forest structure data (tree canopy height and canopy coverage) derived from the lidar observations of the Geoscience Laser Altimetry System (GLAS) on board NASA's Ice, Cloud, and land Elevation Satellite (ICESat-1) were used to assess the five forest cover datasets derived from satellite images. Using the forest definition of the Food and Agricultural Organization (FAO) of the United Nations, more forest pixels from the PL-Forest maps meet the FAO's forest definition than the GFW-Forest, Landsat VCF-Forest, and JAXA-Forest datasets. Forest area estimates from PL-Forest were close to those from the FIA-Forest statistics, higher than GFW-Forest and NLCD-Forest, and lower than Landsat VCF-Forest, which highlights the potential of using both the PL-Forest and FIA-Forest datasets to support the FAO's Global Forest Resources Assessment. Furthermore, the PALSAR-2/Landsat-based annual evergreen forest maps (PL-Evergreen Forest) showed reasonable consistency with the NLCD product. The comparison of the most widely used forest datasets offered insights to employ appropriate products for relevant research and management activities across local to regional and national scales. The datasets generated in this study are available at https://doi.org/10.6084/m9.figshare.21270261 (Wang, 2024). The improved annual maps of forest and evergreen forest at 30 m over the CONUS can be used to support forest management, conservation, and resource assessments.

With over a quarter of its territory covered by forest, Romania stands out as a European country with a medium forest area extension. Despite the fact that the forest area has increased since 2008, there are counties that are affected by forest loss caused by outlawed forest cutting and other factors. This calls for improved knowledge and critical spatial planning of the forest area at county level. The aim of this study is to assess the forest area preservation level of Romania’s 41 counties using fuzzy and classical Multi-Criteria Decision-Making methods. The paper includes inferences regarding the distribution of the illegal forest cutting cases at county level, that link forest management issues with the forest area preservation level. Fuzzy Analytic Hierarchy Process (FAHP) is applied to weigh factors related to the changes of forest provisions, forest loss and forest regeneration processes, dimensions of forest exploitation and illegal forest cutting cases using forest data referring to 1990-2017. The alternatives are represented by the 41 counties of Romania and are evaluated by the use of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results are integrated with GIS and the choropleth map may serve as a powerful visual and practical tool for identifying the counties with pressing forest loss issues. Results show the counties with the lowest levels of forest area preservation were Argeș, Prahova and Gorj. Harghita, Brăila and Suceava counties recorded the highest levels of forest area preservation. While some of the counties that rank among the least in terms of forest area preservation are also altered by massive illegal forest cutting, there are others that serve as counter-examples. The discrepancies are explained by the provisional character of the illegal forest cutting data. Our study shows significant practical importance, pointing out the administrative units of Romania that need to take urgent action in order to mitigate the problem of forest loss and to better their forest management.
 
 Keywords: forest area preservation, Fuzzy Analytic Hierarchy Process, Technique for Order of Preference by Similarity to Ideal Solution, forest management, illegal forest cutting, Romania.

<strong class="journal-contentHeaderColor">Abstract.</strong> Annual forest maps at a high spatial resolution are necessary for forest management and conservation. Large uncertainties remain among the existing forest maps, because of different forest definitions, satellite datasets, in-situ training datasets, and mapping algorithms. In this study, we generated annual forest maps and evergreen forest maps at a 30-m resolution in the Contiguous United States (CONUS) during 2015&ndash;2017 by integrating microwave data (Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)) and optical data (Landsat) using Knowledge-based algorithms. The resultant PALSAR-2/Landsat-based forest maps (PL-Forest) were compared with five major forest datasets in the CONUS: (1) the Landsat tree canopy cover from Global Forest Watch datasets (GFW-Forest), (2) the Landsat Vegetation Continuous Field datasets (Landsat VCF-Forest), (3) the National Land Cover Database 2016 (NLCD-Forest), (4) the Japan Aerospace Exploration Agency (JAXA) forest maps (JAXA-Forest), and (5) the Forest Inventory and Analysis (FIA) data from the USDA Forest Service (FIA-Forest). The forest structure data (tree canopy height and canopy coverage) derived from the lidar observations of the Geoscience Laser Altimetry System (GLAS) onboard of NASA's Ice, Cloud, and land Elevation Satellite (ICESat-1) were used to assess the five forest datasets derived from satellite images. Using the forest definition by the Food and Agricultural Organization (FAO) of the United Nations, more forest pixels from the PL-Forest maps meet the FAO&rsquo;s forest definitions than the GFW-, Landsat VCF-, and JAXA-Forest datasets. Forest area estimates from the PL-Forest were close to those from the FIA-Forest statistics but higher than the GFW-Forest, NLCD-Forest and lower than the Landsat VCF-Forest, which highlights the potential of using both PL-Forest and FIA-Forest datasets to support the FAO's Global Forest Resources Assessment. Furthermore, the PL-based annual evergreen forest maps (PL-Evergreen Forest) showed reasonable consistency with the NLCD product. Together with our previous work in South America and monsoon Asia, this study further demonstrates the potential of integrating PALSAR and Landsat images for developing annual forest maps and forest-type maps at high spatial resolution across the scales from region to the globe, which could be used to support FAO Global Forest Resources Assessments. The PL-Forest and PL-Evergreen Forest datasets are publicly available at <a href="https://doi.org/10.6084/m9.figshare.21270261" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.21270261</a> (Wang et al., 2022).

<strong class="journal-contentHeaderColor">Abstract.</strong> Annual forest maps at a high spatial resolution are necessary for forest management and conservation. Large uncertainties remain among the existing forest maps, because of different forest definitions, satellite datasets, in-situ training datasets, and mapping algorithms. In this study, we generated annual forest maps and evergreen forest maps at a 30-m resolution in the Contiguous United States (CONUS) during 2015&ndash;2017 by integrating microwave data (Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)) and optical data (Landsat) using Knowledge-based algorithms. The resultant PALSAR-2/Landsat-based forest maps (PL-Forest) were compared with five major forest datasets in the CONUS: (1) the Landsat tree canopy cover from Global Forest Watch datasets (GFW-Forest), (2) the Landsat Vegetation Continuous Field datasets (Landsat VCF-Forest), (3) the National Land Cover Database 2016 (NLCD-Forest), (4) the Japan Aerospace Exploration Agency (JAXA) forest maps (JAXA-Forest), and (5) the Forest Inventory and Analysis (FIA) data from the USDA Forest Service (FIA-Forest). The forest structure data (tree canopy height and canopy coverage) derived from the lidar observations of the Geoscience Laser Altimetry System (GLAS) onboard of NASA's Ice, Cloud, and land Elevation Satellite (ICESat-1) were used to assess the five forest datasets derived from satellite images. Using the forest definition by the Food and Agricultural Organization (FAO) of the United Nations, more forest pixels from the PL-Forest maps meet the FAO&rsquo;s forest definitions than the GFW-, Landsat VCF-, and JAXA-Forest datasets. Forest area estimates from the PL-Forest were close to those from the FIA-Forest statistics but higher than the GFW-Forest, NLCD-Forest and lower than the Landsat VCF-Forest, which highlights the potential of using both PL-Forest and FIA-Forest datasets to support the FAO's Global Forest Resources Assessment. Furthermore, the PL-based annual evergreen forest maps (PL-Evergreen Forest) showed reasonable consistency with the NLCD product. Together with our previous work in South America and monsoon Asia, this study further demonstrates the potential of integrating PALSAR and Landsat images for developing annual forest maps and forest-type maps at high spatial resolution across the scales from region to the globe, which could be used to support FAO Global Forest Resources Assessments. The PL-Forest and PL-Evergreen Forest datasets are publicly available at <a href="https://doi.org/10.6084/m9.figshare.21270261" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.21270261</a> (Wang et al., 2022).

<strong class="journal-contentHeaderColor">Abstract.</strong> Annual forest maps at a high spatial resolution are necessary for forest management and conservation. Large uncertainties remain among the existing forest maps, because of different forest definitions, satellite datasets, in-situ training datasets, and mapping algorithms. In this study, we generated annual forest maps and evergreen forest maps at a 30-m resolution in the Contiguous United States (CONUS) during 2015&ndash;2017 by integrating microwave data (Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)) and optical data (Landsat) using Knowledge-based algorithms. The resultant PALSAR-2/Landsat-based forest maps (PL-Forest) were compared with five major forest datasets in the CONUS: (1) the Landsat tree canopy cover from Global Forest Watch datasets (GFW-Forest), (2) the Landsat Vegetation Continuous Field datasets (Landsat VCF-Forest), (3) the National Land Cover Database 2016 (NLCD-Forest), (4) the Japan Aerospace Exploration Agency (JAXA) forest maps (JAXA-Forest), and (5) the Forest Inventory and Analysis (FIA) data from the USDA Forest Service (FIA-Forest). The forest structure data (tree canopy height and canopy coverage) derived from the lidar observations of the Geoscience Laser Altimetry System (GLAS) onboard of NASA's Ice, Cloud, and land Elevation Satellite (ICESat-1) were used to assess the five forest datasets derived from satellite images. Using the forest definition by the Food and Agricultural Organization (FAO) of the United Nations, more forest pixels from the PL-Forest maps meet the FAO&rsquo;s forest definitions than the GFW-, Landsat VCF-, and JAXA-Forest datasets. Forest area estimates from the PL-Forest were close to those from the FIA-Forest statistics but higher than the GFW-Forest, NLCD-Forest and lower than the Landsat VCF-Forest, which highlights the potential of using both PL-Forest and FIA-Forest datasets to support the FAO's Global Forest Resources Assessment. Furthermore, the PL-based annual evergreen forest maps (PL-Evergreen Forest) showed reasonable consistency with the NLCD product. Together with our previous work in South America and monsoon Asia, this study further demonstrates the potential of integrating PALSAR and Landsat images for developing annual forest maps and forest-type maps at high spatial resolution across the scales from region to the globe, which could be used to support FAO Global Forest Resources Assessments. The PL-Forest and PL-Evergreen Forest datasets are publicly available at <a href="https://doi.org/10.6084/m9.figshare.21270261" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.21270261</a> (Wang et al., 2022).

Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height &gt; 2 meters . Here, we report a new forest cover data product that used the FAO forest definition ( tree cover &gt; 10 % and tree height &gt; 5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.