Early Forest Fire Detection Using Radio-Acoustic Sounding System

Automated early fire detection systems have recently received a significant amount of attention due to their importance in protecting the global environment. Some emergent technologies such as ground-based, satellite-based remote sensing and distributed sensor networks systems have been used to detect forest fires in the early stages. In this study, a radio-acoustic sounding system with fine space and time resolution capabilities for continuous monitoring and early detection of forest fires is proposed. Simulations show that remote thermal mapping of a particular forest region by the proposed system could be a potential solution to the problem of early detection of forest fires

Remote mining: from clustering to DTM

LIDAR data acquisition is becoming an indispensable task for terrain characterization in large surfaces. In Mediterranean woods this job results hard due to the great variety of heights and forms, as well as sparse vegetation that they present. A new data mining-based approach is proposed with the aim of classifying LIDAR data clouds as a first step in DTM generation. The developed methodology consists in a multi-step iterative process that splits the data into different classes (ground and low/med/high vegetation) by means of a clustering algorithm. This method has been tested on three different areas of the southern Spain with successful results, verging on 80% hitsMinisterio de Ciencia y Tecnología TIN2007-6808

Quantification of a continuous-cover forest in Sweden using remote sensing techniques

Mapping and quantifying forest information about e.g. land cover, tree height and biomass has traditionally been a both time-consuming and labour-intensive part of forestry and forest research as field measurements typically are collected manually using handheld equipment. Remote sensing has proved to be a valuable complement to field based measurements as it enables for fast and relatively cheap collection of data from areas that would be hard to access from the ground. The aim of this thesis was to map and quantify the Romperöd forest outside Glimåkra in southern Sweden where selective thinning forestry has been practised since the 1960’s. The study was carried out using high resolution multispectral aerial images and small-footprint discrete-return LiDAR data included in the Swedish national elevation model in conjunction with field measurements. The results revealed a mixed forest where Norway spruce was the most dominating tree species, accounting for 40.2 % of the total coverage of the study area, followed by Scots pine (13.8 %), broadleaved trees (8.7 %), succession (6.7 %) and bare-ground (4.1 %). The elevation of the terrain varies between 76.2 and 107.3 meters above sea level, with a ridge extending from south to north. The canopy height of the forest varies greatly throughout the study area and ranged between 1.0 and 34.6 m with an average height of 15.1 m and a standard deviation of 8 m. Above-ground biomass (AGB) was estimated by fitting a multiple regression model to LiDAR-derived vegetation metrics (independent variables) and AGB estimates based on field measurements (dependent variable). The model managed to explain 70 % of the variability in the field measured AGB estimates and was applied to the entire study area yielding an average AGB of 122 900 kg/ha and a standard deviation of 50 497 kg/ha. The inclusion of remote sensing data improved the AGB estimates compared to those based solely on field measurements. The results were compared to the AGB data included in the SLU Forest Map which showed low correlation with AGB estimates based on field measurements (adjusted R2: 0.14), proving it unsuitable for the part of the Romperöd forest characterized by selective thinning.Att karlägga och kvantifiera skogsinformation angående exempelvis marktäcke, terräng, trädhöjder och volym är en traditionellt både tidskrävande och dyr del av skogsbruk och forskning eftersom mätningar vanligtvis samlas in i fält med handhållna instrument. Fjärranalys har visat sig vara ett värdefullt komplement till fältbaserade mätningar eftersom det möjliggör för snabb och relativt billig insamling av data från områden som skulle vara svåra att besöka i fält. Syftet med denna uppsats var att kartlägga och kvantifiera Romperödskogen utanför Glimåkra i nordöstra Skåne där blädningsskogsbruk har praktiserats sedan 1960-talet. Studien genomfördes med hjälp av fjärranalysdata i kombination med mätdata som samlats in i fält och blottlade en blandskog där gran utgör det dominerande trädslaget (40,2 % av studieområdet), följt av tall (13,8 %), lövträd (8,7 %), föryngringar (6,7 %) och bar mark (4,1 %). Terrängen varierar från 76,2 till 107,3 meter över havet med en ås som sträcker sig från söder till norr. Höjden på krontaket är heterogent i hela studieområdet och varierar mellan 1,0 och 34,6 m med en medelhöjd på 15,1 m och en standardavikelse på 8 m. Biomassa ovan jord uppskattades för hela studieområdet och visade ett genomsnitt på 122 900 kg/ha med en standardavikelse på 50 497 kg. Resultaten jämfördes med biomassa ovan jord enligt SLUs Skogskarta som visade låg överensstämmelse med skattningar baserade på fältmätningar vilket visar att SLU:s Skogskarta ej är applicerbar för den del av Romperödskogen som kännetecknas av blädning. Den föreslagna metodiken kan användas för att planera skogsbruk eller för att studera framtida förändringar eller störningar i skogen. Resultaten kan även vara till hjälp vid framtida forskning angående Romperödskogen och dess kolutbyte med atmosfären då biomassa ovan jord direkt kan konverteras till kolförråd, vilket är ett viktigt steg för att kunna studera effekten blädningsskogsbruk har på kolcykeln i skogen

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Interactions among land cover, disturbance, and productivity across Arctic-Boreal ecosystems of Northwestern North America from remote sensing

Arctic and Boreal ecosystems are experiencing accelerated carbon cycling that coincides with trends in the normalized difference vegetation index (NDVI), a widely used remotely sensed proxy for vegetation productivity. Meanwhile, a variety of processes are extensively altering Arctic-Boreal land cover, complicating the relationship between NDVI and productivity. Because high-quality information on land cover is lacking, understanding of relationships among Arctic-Boreal greenness trends, productivity, and land cover change is lacking. Multidecadal time series of moderate resolution (30 m) reflectance data from Landsat and high resolution (<4 m) imagery were used to map annual cover and quantify changes in land cover over the study domain of NASA’s Arctic-Boreal Vulnerability Experiment. Results identify two primary modes of ecosystem transformation that are consistent with increased high latitude productivity: (1) in the Boreal biome, simultaneous decreases in Evergreen Forest area and increases in Deciduous Forest area caused by fire and harvest; and (2) climate change-induced expansion of Arctic Shrub and Herbaceous vegetation. Land cover change imposes first-order control on the sign and magnitude of NDVI trends. Over a quarter of NDVI trends were associated with land cover change. Relative to locations with stable land cover, areas of land cover change were twice as likely to exhibit statistically significant trends in Landsat-derived NDVI. The highest magnitude trends were concentrated in areas of forest disturbance and regrowth and shrub expansion, while undisturbed land showed subtler, but widespread, greening trends. Based on Orbiting Carbon Observatory-2 data, sun-induced fluorescence, a proxy for productivity, reflected relationships among land cover, disturbance age, and productivity that were not fully captured in NDVI data. In contrast with NDVI, time series of aboveground biomass provide physically-based measures of productivity in forests. Using Landsat-based land cover and reflectance and ICESat lidar data, aboveground biomass was mapped annually across the study domain. Most forests showed increasing biomass, with wildfires imposing substantial interannual variability and harvest imposing steady biomass losses. This dissertation provides new information on how disturbances are driving land cover and productivity change across Arctic-Boreal northwestern North America and reveals insights regarding the interpretation of remote sensing observations in these biomes

Earth Resources: A continuing bibliography with indexes, issue 17

This bibliography lists 775 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1978. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

A Sense of Scale: Mapping Exotic Annual Grasses with Satellite Imagery Across a Landscape and Quantifying Their Biomass at a Plot Level with Structure-from-Motion in a Semi-Arid Ecosystem

The native vegetation communities in the sagebrush steppe, a semi-arid ecosystem type, are under threat from exotic annual grasses. Exotic annual grasses increase fire severity and frequency, decrease biodiversity, and reduce soil carbon storage amongst other ecosystem services. The invasion of exotic annual grasses is causing detrimental impacts to land use by eliminating forage for livestock and creating a huge economic cost from fire control and post-fire restoration. To combat invasion, land managers need to know what exotic annual grasses are present, where they are invading, and estimates of their biomass. Mapping exotic annual grasses is challenging because many areas in the sagebrush steppe are difficult to access; yet field measurements are the main method to identify and quantify their existence. In this study, we address this challenge by exploring the use of both landscape-scale and plot-scale observations with remote sensing. First, we use satellite imagery to map where exotic annual grasses are invading and identify the native species which are being encroached upon. Second, we investigate the use of fine-scale imagery for non-destructive measurements of biomass of exotic annual grasses. Understanding the location of exotic annual grasses is important for restoration efforts, e.g. large swath (~100m) herbicide spraying. Restoration efforts are expensive and often ineffective in areas already dominated by exotic annual grasses. Early detection of exotic annual grasses in sagebrush and native grasses communities will increase the chances of effective ecosystem restoration. We used Sentinel-2 satellite imagery in Google Earth Engine, a cloud computing platform, to train a random forest (RF) machine learning algorithm to map vegetation in ~150,000 acres in the sagebrush steppe in southeast Idaho. The result is a classification map of vegetation (overall accuracy of 72%) and a map of percent cover of annual grass (R2 = 0.58). The combination of these two maps will allow land managers to target areas of restoration and make informed decisions about where to allow grazing. In addition to knowing what exotic annual grasses exist and their percent cover, detailed information about their biomass is important for understanding fuel loads and forage quality. Structure from Motion (SfM) is a photogrammetry technique that uses digital images to develop 3-dimensional point clouds that can be transformed into volumetric measurements of biomass. The SfM technique has the potential to quantify biomass estimates across multiple plots while minimizing field work. We developed allometric equations relating SfM-derived volume (m3) to biomass (g/m2) for a study area in southeast Oregon. The resulting equation showed a positive relationship (R2 = 0.51) between the log transformed SfM-derived volume and log transformed biomass when litter was removed. This relationship shows promise in being upscaled to larger surveys using aerial platforms. This method can reduce the need for destructively harvesting biomass, and thus allow field work to cover a greater spatial extent. Ultimately, increasing spatial coverage for biomass will improve accuracy in quantifying fuel loads and carbon storage, providing insights to how these exotic plants are altering ecosystem services

PyrSat - Prevention and response to wild fires with an intelligent Earth observation CubeSat

Forest fires are a pervasive and serious problem. Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. It applies Machine Learning algorithms to autonomously process images and obtain final data products on-board the satellite for direct transmission to users on the ground. Used in combination with other services such as EFFIS or AFIS, the system could considerably reduce the extent and consequences of forest fires

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings

A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented