scholarly journals The Laegeren Site: An Augmented Forest Laboratory

2020 ◽  
pp. 83-104
Author(s):  
Felix Morsdorf ◽  
Fabian D. Schneider ◽  
Carla Gullien ◽  
Daniel Kükenbrink ◽  
Reik Leiterer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Regional Scale ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Essential Biodiversity Variables ◽  
Level Information ◽  
Leaf Level ◽  
Airborne Imaging

AbstractGiven the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity.

scholarly journals BIODIVERSITY MAPPING VIA NATURA 2000 CONSERVATION STATUS AND EBV ASSESSMENT USING AIRBORNE LASER SCANNING IN ALKALI GRASSLANDS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1293-1299
Author(s):  
A. Zlinszky ◽  
B. Deák ◽  
A. Kania ◽  
A. Schroiff ◽  
N. Pfeifer
Keyword(s):  
Laser Scanning ◽  
Conservation Status ◽  
Regional Scale ◽  
Point Clouds ◽  
Natura 2000 ◽  
Global Scale ◽  
Sensor Data ◽  
Continental Scale ◽  
Data Products ◽  
Essential Biodiversity Variables

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.

scholarly journals BIODIVERSITY MAPPING VIA NATURA 2000 CONSERVATION STATUS AND EBV ASSESSMENT USING AIRBORNE LASER SCANNING IN ALKALI GRASSLANDS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1293-1299 ◽  
Author(s):  
A. Zlinszky ◽  
B. Deák ◽  
A. Kania ◽  
A. Schroiff ◽  
N. Pfeifer
Keyword(s):  
Laser Scanning ◽  
Conservation Status ◽  
Regional Scale ◽  
Point Clouds ◽  
Natura 2000 ◽  
Global Scale ◽  
Sensor Data ◽  
Continental Scale ◽  
Data Products ◽  
Essential Biodiversity Variables

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

Modelling of a construction pit

2021 ◽  
pp. 144-149
Author(s):  
G. G. Bickbulatova ◽  
E. N. Kupreeva
Keyword(s):  
Remote Sensing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Digital Model ◽  
Geodetic Measurement ◽  
Sensing Data

There are various programs for processing geodetic measurement and remote sensing data. This article discusses the use of Cyclone software for building a digital model of a construction pit surface based on a point cloud based on laser scanning and calculating the volume of earthworks.

Landscape Biodiversity Characterization in Ecoregion 29 Using MODIS

2013 ◽  
pp. 815-831
Author(s):  
Nitin Kumar Tripathi ◽  
Aung Phey Khant
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Global Warming ◽  
Forest Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Modis Data ◽  
Moderate Resolution ◽  
Landscape Biodiversity

Biodiversity conservation is a challenging task due to ever growing impact of global warming and climate change. The chapter discusses various aspects of biodiversity parameters that can be estimated using remote sensing data. Moderate resolution satellite (MODIS) data was used to demonstrate the biodiversity characterization of Ecoregion 29. Forest type map linked to density of the study area was also developed by MODIS data. The outcome states that remote sensing and geographic information systems can be used in combination to derive various parameters related to biodiversity surveillance at a regional scale.

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

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