Journal articles on the topic 'Reforestation. Aerial photography. Forest management'

Abstract Wildfire poses a serious risk in several regions of the world threatening urban, agricultural areas and natural ecosystems as well. Nature conservation has important role to be prepared for the management of postfire environmental degradation and restoration for protected areas preserving valuable ecosystems. The improving temporal and spatial resolution of remote sensing and GIS methods significantly contributes to map the changes for accelerating management steps of restoration. In this study a severe wildfire and its impacts were assessed in case of a protected area of the Kiskunság National Park in Hungary, which was partly burnt down in 2012. The aim of this research was to efficiently and accurately assess the damages and to plan and execute the restoration works using remote sensing tools. Aerial data collection was performed one month, and one year after the fire. In 2014 the regenerated vegetation was surveyed and mapped in the field. Using the aerial photographs and the field data, the degree and extent of the fire damages, the types and the state of the vegetation and the presence and proportion of the invasive species were determined. Semi-automatic methods were used for the classification of completely, partially damaged and undamaged areas. Based on the results, the reforestation of the burnt area is suggested to prevent the overspreading of white poplar against common junipers and to clean the area from the most frequent invasive species. To monitor the regeneration of the vegetation and the spreading of the invasive species, further aerial photography and field campaigns are planned.

Land-use change is considered the main disturbance in landscape structure and composition, directly affecting faunal distribution and species richness worldwide. Wildfires and natural reforestation alter habitat structure in terms of vegetation cover and also in soil composition and moisture; these processes hence trigger habitat transformations that act as opposing forces at small spatial scales. We have explored the contrasting effects of wildfires and natural reforestation on two land-snail species of the genus Xerocrassa, which are endemic in the western Mediterranean. Snails were sampled in pine and Holm oak forest, stony bare slopes and burnt sites. Both species followed a similar pattern: they were present in more than 75% of the stony bare slope sites and around 50% of the burnt sites, but were almost absent in Holm oak forests. The comparison of aerial photographs from 1956 and 2003 showed that stony bare slopes were significantly larger in 1956, this indicating that the natural reforestation might close these habitats, and consequently threaten the viability of the Xerocrassa populations. Given their limited mobility, the presence of Xerocrassa at burnt sites suggests that these species live in small and cryptic populations within the forest, surviving fire and expanding their distribution due to the appearance of adequate habitats. Our study shows that natural reforestation and fire play opposing roles in conserving Xerocrassa populations. The preservation of stony bare slopes as well as other open areas is a key management guideline to maintain landscape mosaics and help future conservation of species of open habitats such as these vulnerable endemic gastropods.

Though on a global scale, for ecological reasons, increased forest cover is universally regarded as positive, on a local scale, the reforestation of arable land may pose threats to cultural landscapes by removing characteristic landscape features. Particularly vulnerable are marginal rural areas, e.g., mountain regions, where most traditional land use systems have survived and which are subject to the most spectacular land use change. The purpose of this article is to draw attention to the issue of the management of forest cover in historical cultural landscapes in mountain territories in Poland within the context of widespread land use change in Eastern Europe. Land cover data were obtained from historical and contemporary aerial photographs, as well as topographic maps from five time points between 1824 and 2016. The study was conducted by means of spatio-temporal forest cover trajectory analysis (LCTA), transition and time–depth analysis, and land cover change calculations that were made by means of ArcGIS. Our research indicates that the rate of change has risen considerably in the last two decades, and the current share of forest cover is much bigger than that reflected in the official data. Eight principal forest cover trajectory types were identified. The biggest area is occupied by woodland of long-term stability. Another large group is constituted by forests created on the basis of arable land and grassland as a result of simple conversion at one point in time, mainly in the years 1824–1886 and 1939–1994. At the same time, a sizeable group is made up by areas that have been subject to unplanned cyclical or dynamic changes during various periods. A very important group is comprised new forests that were created in 1994–2016, predominantly as a result of natural succession, that are often not included in official land classifications. The constant expansion of woodlands has led to a shrinking of historical former coppice woodlands. This indicates that the current landscape management mechanisms in Poland are inadequate for protecting the cultural landscape. The barriers include the lack of intersectoral cooperation and the overlooking of the historical context of landscapes. The present situation calls not only for verification of the existing forest policy but also for increasing the role and engagement of local communities, as well as making comprehensive local development plans, all of which may be helped by the findings of our study and of similar research.

Studies attempting to calibrate vegetation attributes from aerial photography with field data are reviewed in detail. It is concluded that aerial photography has considerable advantages over satellite-based data because of its capacity to assess the vertical dimension of vegetation and the longer time period the record spans. Limitations of using the aerial photo record as digital data include standardising image contrast and rectification. Some of these problems can be circumvented by manual techniques, but problems of crown exaggeration that varies with photo scale and variation in contrast between the textures of tree crowns and the ground remain. Applications of aerial photography for assessing vegetation change are also reviewed and include deforestation, reforestation, changes in vegetation boundaries, tree density, community composition and crown dieback. These changes have been assessed at scales ranging from individual tree crowns to regional landscapes. In Australia, aerial photography has provided a clear demonstration of deforestation rates and the expansion and contraction of forest and woodland, which is generally attributed to changes in grazing and fire regimes. It is suggested that manual techniques with point-based sampling, digital processing of data for complete spatial coverages and the application of photogrammetric measurements with stereo-plotters are all techniques with great promise for utilising this underrated medium for assessment of vegetation dynamics.

Modern forest management presents ever increasing demands for accurate and up-to-date forest inventory information. The process of inventory update is critical. Inventory update in Canada is examined including update for harvest, burns, insect and disease, silviculture, roads and other changes. The magnitude and requirements of the update task are documented. The procedures used are described and summarized by province in table form. Usage, advantages and disadvantages of current methods (e.g. conventional 9 × 9 aerial photography, supplemental aerial photography, satellite imagery, and aerial reconnaissance) are examined, new methods discussed and trends highlighted. Also outlined are issues related to the incorporation of silviculture and insect and disease information into inventories and the structure and responsibilities for update. Key words: forest inventory, inventory update, harvest, burns, insect and disease, blowdown, silviculture, aerial photography, satellite imagery, Global Positioning System, aerial reconnaissance, video

Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (~93,431 ha) geo-referenced to ~4.7 ± 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area.

National Fire Danger Rating System fuel models were predicted from 1:80,000 scale aerial photographs with a greater than 90% accuracy using combinations of three variables: pine composition, pine basal area and total crown closure. These variables were measured from aerial photos and field checked using techniques common to forest and natural resource management. Graphical presentation from discriminate function analysis indicated good group separation. Misclassification of stands requiring field checking represented 3.3% of the total stand classification. The common use of both the fuel models and aerial photos make the procedures described in this study an easy method for classifying fuel models in the pine and pine-hardwood forests of East Texas.

Large-scale aerial photography is a tool that can provide relevant, timely and cost-effective information on forest quantities and conditions to improve planning and management. Accordingly, Canadians have pioneered two different photographic approaches: the first uses synchronized twin vertical cameras with a fixed airbase; the second approach uses a single camera with tilt recorder and a radar or laser altimeter. The paper discusses the development of these approaches for forest sampling in Canada.

Historic information regarding forest status is essential to management and conservation. Manual interpretation of aerial photography has long been the standard for forest inventory; however, manual interpretation can be subjective, inconsistent, and labor-intensive. This research compares automated techniques with manual interpretation results. First, we used an automated process (called segmentation) to delineate homogeneous stands of forests (or objects), analogous to the goal of manually delineating of polygons. Second, we used classification and regression tree (CART) analysis to classify polygons into the forest and terrain schemes used in British Columbia. Most characteristics of objects created via segmentation were similar to manually delineated polygons, as >70% of attributes were statistically similar across local, polygon, and landscape-level comparisons. Using manual interpretations for comparative reference, automated classifications produced overall accuracies ranging from 62% to 86% with per-class accuracies ranging from 0% to 96%. Automated methods yielded classifications meeting provincial overlap accuracy targets and helped identify classifications most suited to automation. Automated procedures have potential for aiding swift utilization of extensive historical photography archives with several caveats for future consideration. While automated techniques may never replicate all aspects of forest inventory classification, automated techniques may be valuable in assisting different phases of the process.

Abstract. Fire serves as a successional initiation in jack pine (Pinus banksiana) forests of North America, as jack pine reproduce using seratonous cones that open only in intense heat. Jack pine seedling resilience after fire is characterized by high numbers of mortality. The estimation of sapling survivability and density is useful for understanding dynamics of carbon sequestration, forest structure and dynamic, and supporting management of the landscape. Most studies concerning the interaction of forest disturbances occurs at moderate spatial resolution. These moderate resolution data analyses do not adequately capture the fine scale spatial variation of the landscape after fire for understanding sapling survival. Thus, high-resolution data, such as aerial photography may provide more detailed information to support decision-making. A key to the types of spatial patterns that emerge in these early years is the pre-fire stand condition. In heavily managed areas, the mosaic of forest patches may include extensive variety in disturbance conditions. In this current research we address the problem of scale in relation to understanding the influence of pre-fire condition on post-fire early recovery patterns. To do this, we combine data output from the LandTrendr algorithm in Google Earth Engine with spectral data from aerial photography collected by airplane and Unmanned Aerial System to perform a random forest classification. The result is a finer scale resolution map of forest conditions of varying sapling density.

Ground-based forest inventory surveys can provide highly accurate measurements of tree and stand characteristics, but these are expensive to carry out. Aerial photography has been used for several decades as a tool in forest management and inventory. However, conventional methods of interpretation are both time-consuming and costly, with results varying among interpreters. With continuing development of personal computer technology, aerial photographs have become more accessible for digital analysis. This paper presents the potential operational use of digitized aerial photographs for the estimation of tree and stand characteristics of two forest plantations of Sitka spruce (Picea sitchensis (Bong.) Carrière) in Scotland. The digitized aerial photographs were processed using softcopy photogrammetry, and image analysis techniques were used for individual tree crown delineation. For the first site the estimations of stand top height, basal area, volume, biomass, and density (–23.7%) were similar to the ground-measured stand characteristics (±10%), whereas for the second site the estimations were less accurate mainly because of the nonoptimal illumination conditions during the acquisition of the aerial photographs.

Abstract Airborne video data in digital form provides an inexpensive alternative to aerial photography to provide up-to-date information on the size, kinds, and distribution of forest types. Its capability to be incorporated into a geographic information system can augment the value of information produced during analysis. In Maine, Landmark Applied Technologies has developed and is using a system which includes acquiring the video imagery, extracting scenes in digital form, analyzing these data, and incorporating them into an Intergraph GIS to provide a mechanism for rapid updating of spatial data bases. North. J. Appl. For. 5:117-120, June 1988.

Forest managers require accurate and timely data that describe vegetation conditions on cutover areas to assess vegetation development and prescribe actions necessary to achieve forest regeneration objectives. Needs for such data are increasing with current emphasis on ecosystem management, escalating silvicultural treatment costs, evolving computer-based decision support tools, and demands for greater accountability. Deficiencies associated with field survey methods of data acquisition (e.g. high costs, subjectivity, and low spatial and temporal coverage) frequently limit decision-making effectiveness. The potential for remotely sensed data to supplement field-collected forest vegetation management data was evaluated in a problem analysis consisting of a comprehensive literature review and consultation with remote sensing and vegetation management experts at a national workshop. Among curently available sensors, aerial photographs appear to offer the most suitable combination of characteristics, including high spatial resolution, stereo coverage, a range of image scales, a variety of film, lens, and camera options, capability for geometric correction, versatility, and moderate cost. A flexible strategy that employs a sequence of 1:10,000-, 1:5,000-, and 1:500-scale aerial photographs is proposed to: 1) accurately map cutover areas, 2) facilitate location-specific prescriptions for silvicultural treatments, sampling, buffer zones, wildlife areas, etc., and 3) monitor and document conditions and activities at specific points during the regeneration period. Surveys that require very detailed information on smaller plants (<0.5-m tall) and/or individual or rare plant species are not likely to be supported by current remote sensing technologies. Recommended areas for research include : 1) digital frame cameras, or other cost-effective digital imagers, as replacements for conventional cameras, 2) computer-based classification and interpretation algorithms for digital image data, 3) relationships between image measures and physical measures, such as leaf-area index and biomass, 4) imaging standards, 5) airborne video, laser altimeters, and radar as complementary sensors, and 6) remote sensing applications in partial cutting systems. Key words: forest vegetation management, regeneration, remote sensing, aerial photography

In forest management planning and forestry decision-making there is a continuous need for higher quality information on forest resources. The aim of this study was to improve the quality of forest resource information acquired by airborne laser scanning by combining it with aerial images and current stand-register data. A k-MSN (most similar neighbor) application was constructed for the prediction of the plot and stand volumes of standing trees. The application constructed used various data sources, including laser scanner data, aerial digital photographs, class variables describing a stand, and updated old stand volumes. The ability of these data sources to predict stem volume was tested together and separately. In the airborne laser scanner data based k-MSN application, characteristics of canopy quantiles were used as independent variables. The results show that with respect to individual plot and stand volume estimation approaches, the laser-based technique is a superior one. The results were improved further when other information sources were used together with the laser scanner data. Using a combination of laser scanner data, aerial images, and class variables (on the grounds of the current forest database) improved the root mean square error (RMSE) of the estimated plot volume by 15% (from 16% to 13%) as compared to using laser scanner data on their own. When the results were averaged at the stand level, the accuracy improved considerably, but the use of other information sources together with airborne laser scanner data did not further improve the results as it did at the plot level. The RMSE of stand volume was about 6% in all data combinations where airborne laser scanning information was used. One conclusion is that making use of additional available data sources together with laser material improves the reliability of plot volume estimates. As these additional data typically mean no extra material costs (since they are available in any case), making combined use of these data and laser scanner data improves the cost efficiency of a forest inventory.

BIOPRESS (‘Linking Pan-European Land Cover Change to Pressures on Biodiversity’), a European Commission funded ‘Global Monitoring for Environment and Security’ project, produced land cover change information (1950—2000) for Europe from aerial photographs and tested the suitability of this for monitoring habitats and biodiversity. The methods and results related to the land cover change work are summarized. Changes in land cover were established through 73 window and 59 transect samples distributed across Europe. Although the sample size was too small and biased to fully represent the spatial variability observed in Europe, the work highlighted the importance of method consistency, the choice of nomenclature and spatial scale. The results suggest different processes are taking place in different parts of Europe: the Boreal and Alpine regions are dominated by forest management; abandonment and intensification are mainly encountered in the Mediterranean; urbanization and drainage are more characteristic of the Continental and Atlantic regions.

Stratified ground-truthing was undertaken within an area of approximately 30 km2 of tropical savanna across an abrupt sandstone escarpment in the monsoon tropics of Australia. Comparison of aerial photographs from 1941 and 1994 had previously revealed a landscape-wide expansion of closed forest and contraction of grassland patches. Good congruence between field measurements and the vegetation classifications from the 1994 aerial photography supported the authenticity of the vegetation changes. The relative abundance of rainforest and non-rainforest tree species also concurred with mapped vegetation transitions. Changes in individual size classes of rainforest species, which are relatively fire sensitive, were consistent with the primacy of fire in controlling the distribution of the closed-forest formation. Fire scars previously mapped from satellite imagery were used to derive a fire activity index for contrasting vegetation transitions. Savannas that had converted to closed forest had lower fire activity than did stable savannas. Conversely, closed forests that converted to savanna had the highest fire activity index. The landscape-wide expansion of rainforest is associated with the cessation of Aboriginal fire management, possibly in conjunction with elevated CO2 and increasing annual rainfall.

Canadian forest management has had a long history of developing and implementing remote sensing technology and is a major user of remote sensing. Despite difficulties in developing and implementing new digital remote sensing techniques, several key developments in Canadian forest management and in remote sensing and computer technology make the development and implementation of new remote sensing techniques at this time feasible and appropriate. Integration of different remote sensing technologies, remote sensing data with other information sources through geographic information systems, and remote sensing interpretations with forest management systems and practices are critical. Current capabilities and new advances in remote sensing technology for forest survey (excluding forest damage assessment) are discussed. Satellite imagery is a cost-effective tool for broad forest type mapping. New satellite systems improve this capability, but their major impact will be in inventories for new clear-cut and burned areas. Advances in linear array imager technology and lidar systems may lead to development of an end to end inventory mapping system. This system would provide an alternative to aerial photography and current mapping methods and could revolutionize the way forests are inventoried. Imaging spectrometry is a new technology with applications in damage assessment, but as yet has limited potential for assisting in other forest surveys. Spaceborne imaging radar systems are being developed for the 1990s. These systems can produce imagery under cloudy conditions. Their major impact on forestry will be to provide an alternative to visible-infrared satellite data for inventory update.

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

We used General Land Office survey data (1860–1890) and interpreted aerial photography from the 1930s, 1970s, and 1990s to quantify forest disturbance frequency and spatial patterns for four time periods in the Mixed Forest Province of Minnesota. The study region included eight subsections within the Mixed Forest Province of Minnesota’s Ecological Classification System. Presettlement disturbance and spatial pattern estimates varied across the eight subsections indicating a strong relationship to soil and landform characteristics. Land surveyors primarily recorded higher severity disturbances that resulted in significant tree mortality. The 1900–1940 era was characterized by a short-term increase in fire frequency that was relatively uniform across the study region, in contrast to the variability of the presettlement (1860–1890) landscape. In the postsettlement period (1940–1995), timber harvest replaced fire as the dominant disturbance factor. Similar management practices among subsections created similar harvest rates throughout the study region. These management practices imposed a more homogeneous pattern dominated by small (10–25 ha) patches. Management practices now have a greater influence than natural processes in the generation of landscape pattern in the Mixed Forest Province of Minnesota. Information on presettlement forest conditions and subsequent changes can be used by land managers to restore spatial pattern variability in managed forest landscapes.

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity.

<p><strong>Abstract.</strong> Currently, satellite-based systems and UAVs are very popular in the investigation of natural disasters. Both systems have their justification and advantages &amp;ndash; but one should not forget the airborne remote sensing technology. The presentation shows with three examples very clearly how airborne remote sensing is still making great progress and in many cases represents the optimal method of data acquisition.</p> <p>The airborne detection of forest damages (especially currently the bark beetle in spruce stands) can determine the pest attack using CIR aerial images in combination with ALS and hyperspectral systems &amp;ndash; down to the individual tree. Large forest areas of 100 sqkm and more can be recorded from planes on one day (100 sqkm with 10cm GSD on one day).</p> <p>Flood events &amp;ndash; such as on the Elbe in 2013 &amp;ndash; were recorded by many satellites. However, many evaluations require highresolution data (GSD 10cm), e.g. to clarify insurance claims. Here the aircraft system, which was able to fly below the cloud cover and was constantly flying at the height level of the flood peak, proved to be unbeatable.</p> <p>The phenomenon of urban flash floods is one of the consequences of climate change. Cities are not in a position to cope with the water masses of extreme rain events and so are confronted with major damages. In Germany, a number of cities are already preparing to manage short-term but extreme water masses. The complicated hydrographic and hydraulic calculations and simulations require above all one thing &amp;ndash; a precise data basis. This involves, for example, the height of kerbstones and the recording of every gully and every obstacle. Such city-wide data can only be collected effectively by photogrammetric analysis of aerial photography (GSD 5 to 10cm).</p>

The forest ecosystems of Mexico experience soil degradation mainly due to water erosion, which causes low vegetation regeneration. One of the strategies to diminish soil loss is through the construction of stone bunds (SB)—hand-made structures to trap sediment and store water for longer periods. However, little is known about their effects on pine establishment. The objectives of this study were to evaluate the effect of SB on the survival and growth of individual P. engelmannii Carr. specimens with respect to the distance of their planted. The study additionally sought to analyse how SB would affect changes in the production of aerial phytomass, herbaceous vegetation cover and soil characteristics in a reforested area of Durango State in north-central Mexico. Three treatments were evaluated by planting pine trees at three distances with respect to the SB: 80 cm upslope bund (UB), 80 cm downslope bund (DB) and between upper and lower bunds (BB). The variables analysed were the following: The survival and growth of reforestation, aerial coverage and the production of herbaceous plants, and the physicochemical characteristics of the soil. Survival showed significant differences (p < 0.05) among treatments, UB (80%), DB (27%) and BB (30%). The production of aerial phytomass did not show significant differences between treatments UB (1651 kg ha−1) and DB (1058 kg ha−1), although these two were different (p < 0.05) to BB (600 kg ha−1). On the other hand, the vegetation cover and soil characteristics did not show statistical differences. These results highlight the importance of the effect of SB on the survival of P. engelmannii Carr. and the growth of herbaceous vegetation.

In this paper, the possibilities of using stereophotogrammetry methods for measurements using unmanned aerial vehicles (UAVs) for the conditions of a mature pine stand with uneven density are examined. Here, we carried out a comparison of measurements using altimeters and remote sensing data collected with a UAV optical camera. In particular, the height of growing trees was estimated by three different field-based devices and applying the three methods of data collection and processing with UAVs. Specifically, one method implied the direct measurements using on-board UAV equipment. The following two methods are based on the data provided by the stereophotogrammetrical approach, while the aerial images for that were collected using a UAV optical camera. In particular, there was a modeling of the points cloud from one-sided vertical shooting of sample trees and determination of height of trees from digital canopy height model (CHM) from data of aerial photography of horizontal spans over a stand. Our investigation confirmed the highest accuracy of laser measuring tools among the ground measuring devices used in research. Respective value of the average random measurement error was less than 3 % (0.88 m). Among the results obtained from the analysis of the original data collected by UAVs, the best method was to utilize the CHM, namely, the average random error was less than 2% (0.64 m). This exceeds the accuracy of laser altimeter measurements 33 %. Thus, this method of measuring height in pine stands meets the standards of accuracy in determining the height for production assessment, according to the “Inventory guidelines for the forest fund of Ukraine”, and can be used for survey, inventory, forest management and other works related to forestry and monitoring the changes in forest ecosystems.

There is a prevailing paradigm that woody vegetation is expanding at the expense of grassland with reduced burning under pastoralism in the Mulga Lands biogeographic region in eastern Australia. This raises the possibility that the region is acting as a carbon sink. Vegetation boundaries were precisely positioned from rail survey plans dating from 1895 to 1900. This baseline was compared with the position of boundaries on 1952 aerial photography and 2010 Google Earth imagery. The conversion of forest to non-forest by mechanical clearing was also mapped from satellite imagery. There was no consistent trend in the direction of boundary movement for mulga (Acacia aneura F.Muell. ex Benth.), gidgee (Acacia cambagei R.T. Baker) forest or miscellaneous other forest types. The stability of the boundaries, despite the transition from aboriginal management to rangeland pastoralism, contrasts with dramatic declines in tree cover resulting from mechanical clearing. Mapping of forest cover from satellite imagery reveals that conversion of forest to non-forest has reduced mulga forest to 74%, gidgee forest to 30% and miscellaneous forest types to 82% of their original area. Annual clearing rates for the period between 1997 and 2005 were 0.83, 0.95 and 0.43% for those forest types, respectively. Clearing has declined substantially in the period 2005–09 since the advent of recent regulations in Queensland. The area remains a source of carbon emissions but this situation may reverse if restoration of mulga dry forest becomes an attractive land use with an emerging carbon market.

Information needs associated with forest management and reporting requires data with a steadily increasing level of detail and temporal frequency. Remote sensing satellites commonly used for forest monitoring (eg, Landsat, SPOT) typically collect imagery with sufficient temporal frequency, but lack the requisite spatial and categorical detail for some forest inventory information needs. Aerial photography remains a principal data source for forest inventory; however, information extraction is primarily accomplished through manual processes. The spatial, categorical, and temporal information requirements of large-area forest inventories can be met through sample-based data collection. Opportunities exist for very high spatial resolution (VHSR; ie, <1 m) remotely sensed imagery to augment traditional data sources for large-area, sample-based forest inventories, especially for inventory update. In this paper, we synthesize the state-of-the-art in the use of VHSR remotely sensed imagery for forest inventory and monitoring. Based upon this review, we develop a framework for updating a sample-based, large-area forest inventory that incorporates VHSR imagery. Using the information needs of the Canadian National Forest Inventory (NFI) for context, we demonstrate the potential capabilities of VHSR imagery in four phases of the forest inventory update process: stand delineation, automated attribution, manual interpretation, and indirect attribute modelling. Although designed to support the information needs of the Canadian NFI, the framework presented herein could be adapted to support other sample-based, large-area forest monitoring initiatives.

Characterizing and measuring the extent of change at forest edges is important for making management decisions, especially in the face of climate change, but is difficult due to the large number of factors that can modify the response. Unmanned aerial systems (UAS) imagery may serve as a tool to detect and measure the forest response at the edge quickly and repeatedly, thus allowing a larger amount of area to be covered with less work. This study is a preliminary attempt to utilize UAS imagery to detect changes in canopy cover, known to exhibit changes due to edge influences, across forest edges in a New England forest. Changes in canopy cover with increasing distance from the forest edge were measured on the ground using digital cover photography and from photogrammetric point clouds and imagery-based maps of canopy gaps produced with UAS imagery. The imagery-based canopy gap products were significantly more similar to ground estimates for canopy cover (p value > 0.05) than the photogrammetric point clouds, but still suffered overestimation (RMSE of 0.088) due to the inability to detect small canopy openings. Both the ground and UAS data were able to detect a decrease in canopy cover to between 45–50 m from the edge, followed by an increase to 100 m. The UAS data had the advantage of a greater sampling intensity and was thus better able to detect a significant edge effect of minimal magnitude effect in the presence of heavy variability.

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

Wildfires in forest ecosystems produce landscape mosaics that include relatively unaffected areas, termed fire refugia. These patches of persistent forest cover can support fire-sensitive species and the biotic legacies important for post-fire forest recovery, yet little is known about their abundance and distribution within fire perimeters. Readily accessible 30-m resolution satellite imagery and derived burn severity products are commonly employed to characterize post-fire landscapes; however, coarse image resolution, generalized burn severity thresholds, and other limitations can constrain accurate representation of fire refugia. This study quantifies the abundance and pattern of fire refugia within 10 fires occurring in ponderosa pine and dry mixed-conifer forests between 2000 and 2003. We developed high-resolution maps of post-fire landscapes using semi-automated, object-based classification of 1-m aerial imagery, conducted imagery- and field-based accuracy assessments, and contrasted these with Landsat-derived burn severity metrics. Fire refugia area within burn perimeters ranged from 20% to 57%. Refugia proportion generally decreased with increasing Landsat-derived burn severity, but still accounted for 3–12% of areas classified as high severity. Patch size ranged from 1-m2 isolated trees to nearly 8000 ha, and median patch size was 0.01 ha—substantially smaller than a 30-m Landsat pixel. Patch size was negatively related to burn severity; distance to fire refugia from open areas was positively related to burn severity. Finally, optimized thresholds of 30-m post-fire normalized burn ratio (NBR) and relative differenced normalized burn ratio (RdNBR) delineated fire refugia with an accuracy of 77% when validated against the 1-m resolution maps. Estimations of fire refugia abundance based on Landsat-derived burn severity metrics are unlikely to detect small, isolated fire refugia patches. Finer-resolution maps can improve understanding of the distribution of forest legacies and inform post-fire management activities including reforestation and treatments.

Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.

Measurement of tree attributes is important to collect information for forest management. Close-range photogrammetry with spherical panoramas has seen very little development and applications compared with aerial photography. This study develops methods to extract azimuth, horizontal distance, diameter at breast height, and upper stem diameters of individual trees from spherical panoramas based on (i) the trigonometry principle (TRIGO), (ii) the TRIGO corrected for terrain slope (TRIGOSLP), and (iii) the pinhole camera model (PINHOLE). Twenty-three horizontal point sample plots were randomly established in plantations in Taiwan, for a sample size of 486 trees. Results showed that tree azimuth was accurately and precisely estimated. TRIGO performed the worst in accuracy and precision for all other tree attributes. TRIGOSLP improved the results of TRIGO but had large estimation errors. PINHOLE achieved the best overall precision for all other tree attributes but was slightly inaccurate for estimating upper stem diameters. PINHOLE requires approaching a tree to attach a target of known size but has the ability to extract an almost continuous set of upper stem diameters from the tree, which could improve estimation of tree volume. Thus, PINHOLE could potentially be an alternative measurement system for hard-to-measure tree attributes.

Background and Objectives: The existence of common ash (Fraxinus excelsior) in Europe is severely endangered by ash dieback. To support its future sustainability, it is essential to improve the natural ash regeneration. The main aim of this study was to investigate the influence of light conditions, conceivably influenced by stand structure/ash dieback, on ash regeneration and the competition between ash seedlings and species growing in the understory. Materials and Methods: We selected 40 plots in a riparian forest located in Bavaria, Germany. Light-related variables (Leaf Area Index, gap fraction) were gathered with fish-eye photography, whereas other environmental factors were derived from vegetation surveys (Ellenberg indicator values). We assessed vegetation parameters such as species’ richness and coverage of the herb layer to account for competition with ash seedlings. Results: Our results indicate that ash regeneration is favoured under shady conditions. The majority of other abiotic factors were not statistically associated with the analysed ash metrics. In contrast, the coverage of grass was negatively related to LAI and positively to gap fraction. Higher herb and grass coverages were linked to a suppression of ash regeneration. A higher litter coverage was associated with a higher frequency of ash seedlings. Nonparametric partial correlation analyses demonstrated the influence of light and stressed that litter coverage is of particular importance. Conclusions: We conclude that gaps, inter alia induced by ash dieback, favour grass invasion. In turn, this invasion might suppress regeneration of ash. In this regard, rapid silvicultural management such as reforestation of gaps after dieback of mature trees is recommended. The influence of litter on interspecific competition during growth should be also considered. The pace of dieback might additionally influence the timing and quantity of litter accumulation; thus, further research should also focus on these interrelations.

<p>This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m^{<span lang="EN-US">–2</span>}) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystems.</p>

Texture strongly influences the soil’s fundamental functions in forest ecosystems. In response to the growing demand for information on soil properties for environmental modeling, more and more studies have been conducted over the past decade to assess the spatial variability of soil properties on a regional to global scale. These investigations rely on the acquisition and compilation of numerous soil field records and on the development of statistical methods and technology. Here, we used random forest machine learning algorithms to model and map particle size composition in ecoforest polygons for the entire area of managed forests in the province of Quebec, Canada. We compiled archived laboratory analyses of 29,570 mineral soil samples (17,901 sites) and a set of 33 covariates, including 22 variables related to climate, five related to soil characteristics, three to spatial position or spatial context, two to relief and topography, and one to vegetation. After five repeats of 5-fold cross-validation, results show that models that include two functionally independent values regarding particle size composition explain 60%, 34%, and 78% of the variance in sand, silt and clay fractions, respectively, with mean absolute errors ranging from 4.0% for the clay fraction to 9.5% for the sand fraction. The most important model variables are those observed in the field and those interpreted from aerial photography regarding soil characteristics, followed by those regarding elevation and climate. Our results compare favorably with those of previous soil texture mapping studies for the same territory, in which particle size composition was modeled mainly from rasterized climatic and topographic covariates. The map we provide should meet the needs of provincial forest managers, as it is compatible with the ecoforest map that constitutes the basis of information for forest management in Quebec, Canada.

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000&thinsp;km&lt;sup&gt;2&lt;/sup&gt; covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5&thinsp;m-pixel colour aerial photography to collect &gt;&thinsp;15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80&thinsp;% and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000&thinsp;km² covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5&thinsp;m-pixel colour aerial photography to collect >&thinsp;15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80&thinsp;% and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

<p><strong>Abstract.</strong> Landslide erosion is a serious land management problem in many parts of the world. In New Zealand, a combination of steep erodible hill country, frequent and intense rainstorms, and recent forest clearance for pastoral farming has led to extensive landslide erosion in many parts of the country. Effective mitigation measures against landslide erosion and its consequences require a detailed understanding of the history, location, extent, and severity of landsliding. Consequently, the preparation and availability of comprehensive and meaningful landslide inventories and spatial representation using advanced cartographic techniques can support mitigation of landslide erosion, risk communication, decision making and land management.</p><p>Remote sensing and new geospatial technologies have significantly increased objectivity and efficiency of geomorphological mapping, while at the same time recent developments in GIScience allow different theoretical perspectives on space, time, processes, and systems to be merged (Bishop et al., 2012; Napieralski et al., 2013). The complexity of natural phenomena such as landslides requires advanced and innovative image analysis methods for adequate and effective landslide inventory mapping (Guzzetti et al., 2012). Remote sensing data is well suited for detecting landslides and for deriving spatio-temporal landslide patterns. Manually or semi-automatically mapped landslides from optical satellite images or aerial photographs can be used as input for creating landslide pattern or hotspot maps, which constitute an easy-to-grasp visual representation of the worst landslide-affected areas following landslide triggering events such as heavy rainstorms.</p><p>For analysing and visualizing spatio-temporal landslide patterns we use historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA) (cf. Hölbling et al., 2016). The development of semi-automated image classification techniques such as OBIA offers the potential to considerably improve existing manual landslide mapping techniques, in particular the time needed for landslide detection can be significantly reduced while achieving acceptable mapping accuracies. The semi-automatically identified landslide polygons are rasterized at 1&amp;thinsp;m cell size and aggregated to 25&amp;thinsp;m pixel resolution by calculating the sum of 1&amp;thinsp;m landslide pixels per 25&amp;thinsp;m cell. The resulting map is a landslide cover or density map which displays the proportion of landslide area covering each pixel (625&amp;thinsp;m²) in percent. Figure 1 shows an example of the identified landslide hotspots based on the OBIA mapping using the aerial photograph from 2005. The comparison of the landslide hotspots based on the OBIA mapping with those based on manually mapped landslides shows very similar results for all periods.</p><p> Advanced visualization techniques and cartographic representations are finally used to illustrate the evolution of the landslide hotspots over space and time. Such a space-time and multidimensional representation of landslide patterns can reveal relevant new information about past and recent landslide activity and can be valuable for risk communication and targeted mitigation of landslide erosion.</p>

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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Today, unmanned aerial vehicles are increasingly used in various sectors of economy, including forestry. Therefore, there is a need for rationing and economic evaluation of these types of activity. Technical standardization of labor is an important stage in the implementation of innovative approaches, technologies, techniques, engineering samples and appliances in all spheres of production. This study serves the purpose of assessing the economic performance of unmanned aerial vehicles at aerial field work during forest management planning activities. The separate unit of the National University of Life and Environmental Sciences of Ukraine “Boiarka Forest Research Station” was identified as the base enterprise. The main analyzed means of technics within the scope of this research is the copter type unmanned aerial vehicle DJI Phantom 4. Based on the data of motion and time study of a quadcopter operator’s shifts, a rational balance of working time of a shift alongside with the normative indicators of labor productivity during aerial photographing activity were calculated. It is possible to adjust the production rate depending on the total travelled distance during a working shift. On the basis of the developed standards of labor productivity, the full prime cost of carrying out aerial photography on an area of 1 ha has been calculated. A comparison of the cost of carrying out aerial photography by means of an unmanned aerial vehicle with the cost of forest management planning and forest certification fieldwork has also been carried out. The results of the study suggest that provided the comparable qualitative and quantitative indicators of the collected data, the use of unmanned aerial vehicles for local clarification of the situation is less expensive compared to surveys by field teams.

As old forest inventory methods, based on questionnaires, become obsolete, a new procedure using remote sensing techniques has been developed. The objectives are fourfold: 1. Analysis of the forest situation in Flanders 2. Forest inventory methodology development 3. Creation of a forest management data bank 4. Production of base maps with high planimetric accuracy Basic material consists of colour infrared 1/30000 transparencies. Photo interpretation and processing is carried out using a Wild Aviopret APT-1 stereoscope and a Bausch &amp; Lomb Zoom Transfer Scope. The legend features species composition, development stage, ground cover percentage, management practice and ownership. Data from interpretation, field work and supplemental sources are presented as a series of thematic overlays on a 1/5000 colour infrared orthophotomap. Numerical processing is executed on a PDP 11/44 microcomputer. A Wild interactive graphic system (Synercom) is used for digitizing and area calculation. Automatic map production is performed on a Wild TA 2 drawing table. Availability of stand boundaries in digital form enables generation of thematic computer maps.

Nipa palms are exposed by the transformation of land use and land cover changes (LULCC) due to changes to aquaculture and orchards. Modern remote sensing for environmental monitoring of LULCC has been made easier by the use of high spatial resolution images, innovative image processing and Geographic Information Systems (GIS). The expense of high-resolution satellite imagery has resulted in investigators moving to open sources (e.g., Landsat), therefore, the interpretation of images at a medium resolution can be classified simply as LULCC classes and are constrained by the detection of small-scale disturbances. This research applied Landsat imagery with very high-resolution imagery from Unmanned Aerial Vehicles (UAVs). In order to be useful for real-world applications, the accuracy of remote sensing data must be validated using proven ground-based methods. UAVs equipped with multispectral sensors were flown over the Nipa palms at the Prasae River, Rayong Province, Thailand. The main advantage of UAV-based remote sensing is that it reduces costs and immediate availability of high-resolution data. The UAV imagery was expensed as “drone truthing data” to train image classification algorithms. These results show that UAV data can be used effectively to support and categorize similar land-cover/use classes (aquaculture vs. mangrove forest vs. nipa forest) with consistently high identification of over 87.6% on the generated thematic map, where the mangrove forest detection rate was as high as 86%. For that reason, UAVs are engaged successively in management and conservation tasks, which can be used for regional or local scale studies to compare the achieved accuracy to a general regional land cover map. This approach can be used for the variability of plants to rectify land-cover classification. Therefore, UAV images are a very useful tool to fill the gap between remote sensing information and expensive ground field campaigns.