Search Results (10)

The National Forestland “One Map” applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in “One Map”, taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for “One Map” (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in “One Map” and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. Full article

The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products. Full article

Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional, spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure for a selected North Carolina region, a 2.3 km² area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data, and two publicly available, open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD), are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative, widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large-scale assessments of woody biomass in the region. Full article

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r² = 0.31, r² = 0.29, and r² = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions. Full article

National Forest Inventories (NFIs) collect and provide a large amount of information regarding the forest volume, carbon stocks, vitality, biodiversity, non-wood forest products and their changes. Forest stands variables data are paramount to understanding their composition, especially on those related with understory characteristics and the coverage of species according to canopy layers; they are essential to assess biodiversity and to support forest management. At the same time, these inventories allow the development of harmonized forest descriptions beyond the national scale. This study aims to develop a homogeneous characterization of the Iberian Peninsula’s forests, in order to classify and identify the forest types. For this purpose, harmonized data from NFIs of Portugal and Spain were used to assess the composition of species, dominance and the percentage of cover for each species in a vertical space defined by seven canopy layers. Using the “K-means” clustering algorithm, a set of clusters was identified and georeferenced using forest polygons from land use and cover maps of both countries. The interpretation and description of the clusters lead to the establishment of 28 forest types that characterize all of the Iberian Peninsula forests. Each forest area has been described through one of the forest types and their relation with other ecological characteristics of the stands was analyzed. Shrubs formations are generally widely distributed in the forest area of the Iberian Peninsula, however their abundance in terms of cover is lower in comparison with tree species. Around 71% of the forest types are dominated by trees, mainly species from the genera Pinus and Quercus, and 21% are dominated by shrub formations with species of Ulex spp., Cytisus spp., and Cistus spp. The Quercus ilex s.l. L. and Pinus pinaster Aiton are the common species of importance for both NFIs. The results represent a powerful and homogenous multi-use tool describing the Iberian Peninsula’s forestlands with applications on landscape analysis, forest management and conservation. This information can be used for comparisons at larger scales, allowing cross-border analysis in relation to various aspects, such as hazards and wildfires, as well as management and conservation of forest biodiversity. The developed method is adaptable to an updated dataset from more recent NFIs and to other study areas. Full article

National monitoring of forestlands and the processes causing canopy cover loss, be they abrupt or gradual, partial or stand clearing, temporary (disturbance) or persisting (deforestation), are necessary at fine scales to inform management, science and policy. This study utilizes the Landsat archive and an ensemble of disturbance algorithms to produce maps attributing event type and timing to >258 million ha of contiguous Unites States forested ecosystems (1986–2010). Nationally, 75.95 million forest ha (759,531 km²) experienced change, with 80.6% attributed to removals, 12.4% to wildfire, 4.7% to stress and 2.2% to conversion. Between regions, the relative amounts and rates of removals, wildfire, stress and conversion varied substantially. The removal class had 82.3% (0.01 S.E.) user’s and 72.2% (0.02 S.E.) producer’s accuracy. A survey of available national attribution datasets, from the data user’s perspective, of scale, relevant processes and ecological depth suggests knowledge gaps remain. Full article

This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels. Full article

Research Highlights: The European Union’s last large intact forest landscapes along the Scandinavian Mountain range in Sweden offer unique opportunities for conservation of biodiversity, ecological integrity and resilience. However, these forests are at a crossroad between intensified wood production aimed at bio-economy, and rural development based on multi-functional forest landscapes for future-oriented forest value chains. Background and Objectives: We (1) estimate the area of near-natural forests potentially remaining for forest harvesting and wood production, or as green infrastructure for biodiversity conservation and human well-being in rural areas, (2) review how forest and conservation policies have so far succeeded to reduce the loss of mountain forests, and (3) discuss what economic, socio-cultural and ecological values that are at stake, as well as different governance and management solutions. Materials and Methods: First, we estimated the remaining amount of intact mountain forests using (1) the Swedish National Forest Inventory, (2) protected area statistics, (3) forest harvest permit applications and actually harvested forests, (4) remote sensing wall-to-wall data on forests not subject to clear-felling since the mid-1950s, (5) mapping of productive and non-productive forestland, and (6) estimates of mean annual final felling rate. Second, we review policy documents related to the emergence of land use regulation in north Sweden, including the mountain forest border, and illustrate this with an actual case that has had significant policy implementation importance. Results: There is a clear difference between the proportions of formally protected productive forestland above the mountain forest border (52.5%) and north Sweden in general (6.3%). A total of 300,000 ha of previously not clear-felled mountain forest outside protected areas remain, which can support novel value chains that are not achievable elsewhere. Conclusions: The mountain forests in Sweden provide unique conservation values in the European Union. Since the beginning of the 1990s, policy regulations have been successful in limiting forest harvesting. Currently, however, mountain forests are a battle ground regarding intensification of forest use, including logging of forests that have never been subject to clear-felling systems vs. nature conservation and wilderness as a base for rural development. The ability of mountain municipalities to encourage sustainable rural forest landscapes must be strengthened. Full article

With about 107 million hectares of moist forest, the Democratic Republic of Congo (DRC) is a perfect paradox of a natural resources endowed country caught in repeated economic and socio-political crises. Democratic Republic of Congo possesses about 60% of the Congo basin’s forest on which the majority of its people rely for their survival. Even if the national forest land in the countryside is mainly exploited by local populations based on customary rights, they usually do not have land titles due to the fact that the state claims an exclusive ownership of all forest lands in the Congo basin including in DRC. The tragedy of “bad governance” of natural resources is often highlighted in the literature as one of the major drivers of poverty and conflicts in DRC. In the forest domain, several studies have demonstrated that state bureaucracies cannot convincingly improve the governance of forestland because of cronyism, institutional weaknesses, corruption and other vested interests that govern forest and land tenure systems in the country. There are however very few rigorous studies on the role of traditional leaders or chiefdoms in the governance of forests and land issues in the Congo basin. This research aimed at addressing this lack of knowledge by providing empirical evidence through the case study of Yawalo village, located around the Yangambi Biosphere Reserve in the Democratic Republic of Congo. From a methodological perspective, it used a mixed approach combining both qualitative (field observations, participatory mapping, interviews, focal group discussions, and desk research,) and quantitative (remote sensing and statistics) methods. The main findings of our research reveal that: (i) vested interests of traditional rulers in the DRC countryside are not always compatible with a sustainable management of forestland; and (ii) influential users of forestland resources at the local level take advantage of traditional leaders’ weaknesses—lack of autonomy and coercive means, erratic recognition of customary rights, and poor legitimacy—to impose illegal hunting and uncontrolled forest exploitation. Full article

Forests are one of the most important components of the global biosphere and have critical influences on the Earth’s ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data. Based on the differences of histograms among different forests, we obtained the 2001–2011 forests distribution for China at a spatial resolution of 500-m × 500-m. The overall accuracy of validation was 80.4%, an increase of 12.8% relative to that obtained using MODIS LC-1 data. The 2001–2011 forestland pure and mixed pixels of China accounted for an average of 33.72% of all pixels. There is a gradual increase in China’s forestland coverage during 2001–2011; however, the relationship is not statistically significant. Full article