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3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

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ISBN: 9783039217823 9783039217830 Year: Pages: 188 DOI: 10.3390/books978-3-03921-783-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Ecology
Added to DOAB on : 2019-12-09 11:49:16
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Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed.

Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

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ISBN: 9783039212392 9783039212408 Year: Pages: 334 DOI: 10.3390/books978-3-03921-240-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:15
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Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

Keywords

conifer forest --- leaf area index --- smartphone-based method --- canopy gap fraction --- terrestrial laser scanning --- forest inventory --- density-based clustering --- forest aboveground biomass --- root biomass --- tree heights --- GLAS --- artificial neural network --- allometric scaling and resource limitation --- structure from motion (SfM) --- 3D point cloud --- remote sensing --- local maxima --- fixed tree window size --- managed temperate coniferous forests --- point cloud --- spectral information --- structure from motion (SfM) --- unmanned aerial vehicle (UAV) --- chlorophyll fluorescence (ChlF) --- drought --- Mediterranean --- photochemical reflectance index (PRI) --- photosynthesis --- R690/R630 --- recovery --- BAAPA --- remote sensing --- household survey --- forest --- farm types --- automated classification --- sampling design --- adaptive threshold --- over and understory cover --- LAI --- leaf area index --- EPIC --- simulation --- satellite --- MODIS --- biomass --- evaluation --- southern U.S. forests --- VIIRS --- leaf area index (LAI) --- Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) --- MODIS --- consistency --- uncertainty --- evaluation --- downscaling --- Pléiades imagery --- unmanned aerial vehicle --- stem volume estimation --- remote sensing --- clumping index --- leaf area index --- trunk --- terrestrial LiDAR --- HemiView --- forest above ground biomass (AGB) --- polarization coherence tomography (PCT) --- P-band PolInSAR --- tomographic profiles --- canopy closure --- global positioning system --- hemispherical sky-oriented photo --- signal attenuation --- geographic information system --- digital aerial photograph --- aboveground biomass --- leaf area index --- photogrammetric point cloud --- recursive feature elimination --- machine-learning --- forest degradation --- multisource remote sensing --- modelling aboveground biomass --- random forest --- Brazilian Amazon --- validation --- phenology --- NDVI --- LAI --- spectral analyses --- European beech --- altitude --- forests biomass --- remote sensing --- REDD+ --- random forest --- Tanzania --- RapidEye

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MDPI - Multidisciplinary Digital Publishing Institute (2)


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english (2)


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2019 (2)