Assessment of Aquatic Reed Stands from Airborne Photogrammetric 3K Data

Aquatic reed beds provide important ecological functions, yet their monitoring by remote sensing methods remains challenging. In this study, we propose an approach of assessing aquatic reed stand status indicators based on data from the airborne photogrammetric 3K-system of the German Aerospace Center (DLR). By a Structure from Motion (SfM) approach, we computed stand surface models of aquatic reeds for each of the 14 areas of interest (AOI) investigated at Lake Chiemsee in Bavaria, Germany. Based on reed heights, we subsequently calculated the reed area, surface structure homogeneity and shape of the frontline. For verification, we compared 3K aquatic reed heights against reed stem metrics obtained from ground-based infield data collected at each AOI. The root mean square error (RMSE) for 1358 reference points from the 3K digital surface model and the field-measured data ranged between 39 cm and 104 cm depending on the AOI. Considering strong object movements due to wind and waves, superimposed by water surface effects such as sun glint altering 3K data, the results of the aquatic reed surface reconstruction were promising. Combining the parameter height, area, density and frontline shape, we finally calculated an indicator for status determination: the aquatic reed status index (aRSI), which is based on metrics, and thus is repeatable and transferable in space and time. The findings of our study illustrate that, even under the adverse conditions given by the environment of the aquatic reed, aerial photogrammetry can deliver appropriate results for deriving objective and reconstructable parameters for aquatic reed status (Phragmites australis) assessment.

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Accuracy assessment of TanDEM-X IDEM using airborne LiDAR on the area of Poland

Geodesy and Cartography ◽

10.1515/geocart-2017-0007 ◽

2017 ◽

Vol 66 (1) ◽

pp. 137-148 ◽

Cited By ~ 3

Author(s):

Małgorzata Woroszkiewicz ◽

Ireneusz Ewiak ◽

Paulina Lulkowska

Keyword(s):

Digital Elevation Model ◽

Laser Scanning ◽

Accuracy Assessment ◽

Shuttle Radar Topography Mission ◽

Reference Points ◽

Airborne Lidar ◽

Surface Model ◽

Digital Elevation ◽

Elevation Model ◽

German Aerospace

Abstract The TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission launched in 2010 is another programme – after the Shuttle Radar Topography Mission (SRTM) in 2000 – that uses space-borne radar interferometry to build a global digital surface model. This article presents the accuracy assessment of the TanDEM-X intermediate Digital Elevation Model (IDEM) provided by the German Aerospace Center (DLR) under the project “Accuracy assessment of a Digital Elevation Model based on TanDEM-X data” for the southwestern territory of Poland. The study area included: open terrain, urban terrain and forested terrain. Based on a set of 17,498 reference points acquired by airborne laser scanning, the mean errors of average heights and standard deviations were calculated for areas with a terrain slope below 2 degrees, between 2 and 6 degrees and above 6 degrees. The absolute accuracy of the IDEM data for the analysed area, expressed as a root mean square error (Total RMSE), was 0.77 m.

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DETECTION OF COLLAPSED BUILDINGS DUE TO EARTHQUAKES USING A DIGITAL SURFACE MODEL CONSTRUCTED FROM AERIAL IMAGES

Journal of Earthquake and Tsunami ◽

10.1142/s1793431114500031 ◽

2014 ◽

Vol 08 (01) ◽

pp. 1450003 ◽

Cited By ~ 4

Author(s):

YOSHIHISA MARUYAMA ◽

AKIRA TASHIRO ◽

FUMIO YAMAZAKI

Keyword(s):

Aerial Images ◽

Surface Model ◽

Digital Surface Model ◽

Severe Damage ◽

Aerial Photogrammetry ◽

Surface Models ◽

Event Models ◽

Collapsed Buildings

The buildings that collapsed during the 2007 Niigata Chuetsu-oki earthquake are detected based on aerial photogrammetry using digital aerial images. The digital surface models (DSMs) in the area where severe damage to buildings was observed after the earthquake are constructed using digital aerial camera images. Pre- and post-event aerial images are employed to obtain the DSMs in this study. The differences in building heights between the pre- and post-event models are considered to detect collapsed buildings and the accuracy of the method is discussed in this paper. The results indicate that the collapsed buildings can be detected and undamaged buildings can also be correctly recognized by the proposed method.

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USING SURFACE FITTING AND BUFFER ANALYSIS TO ESTIMATE REGIONAL GEOIDAL UNDULATION

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702015000300035 ◽

2015 ◽

Vol 21 (3) ◽

pp. 624-636 ◽

Cited By ~ 3

Author(s):

FANG-SHII NING

Keyword(s):

Geoid Undulation ◽

Satellite System ◽

Surface Fitting ◽

Reference Points ◽

Surface Model ◽

Geoidal Undulation ◽

Surface Models ◽

Quadratic Surface ◽

Geodetic Height ◽

Global Navigation Satellite

Abstract:Geoidal undulation is the distance from the surface of an ellipsoid to the surface of a geoid measured along a line that is perpendicular to the ellipsoid. This paper describes how the geoidal undulation can be derived from the orthometric height, Global Navigation Satellite System geodetic height, and a surface model. Various surfaces fitting using the plane coordinates of the reference points and analysis with different buffers were used to determine the geoid undulation Taiwan. The results show that the quadratic surface model outperformed other surface models, yielding a buffer radius ranging from 15 to 25 km. According to the results, the accuracy of regional geoid undulation (city or state) can be improved through this process of surface fitting

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Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0053.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 897-915 ◽

Cited By ~ 20

Author(s):

Jennifer L. Jefferson ◽

Reed M. Maxwell ◽

Paul G. Constantine

Keyword(s):

Land Surface ◽

Stomatal Resistance ◽

Hydrologic Model ◽

Surface Model ◽

Land Surface Models ◽

Common Land Model ◽

Surface Models ◽

Input Parameters ◽

Rate Of Photosynthesis ◽

Quantities Of Interest

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

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Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

10.5194/egusphere-egu21-13546 ◽

2021 ◽

Author(s):

Jonathan Barichivich ◽

Philippe Peylin ◽

Valérie Daux ◽

Camille Risi ◽

Jina Jeong ◽

...

Keyword(s):

Tree Ring ◽

Land Surface ◽

Forest Ecosystems ◽

Ring Width ◽

Land Surface Model ◽

Surface Model ◽

Growth Variability ◽

Tree Ring Data ◽

Surface Models ◽

Global Land

Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (&#916;13C) and oxygen (&#948;18O) stable isotopes in six sites in boreal and temperate Europe.&#160; The model simulates &#916;13C (r = 0.31-0.80) and &#948;18O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with &#948;18O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, &#916;13C and &#948;18O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.

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Brief Communication: Mapping river ice using drones and structure from motion

The Cryosphere ◽

10.5194/tc-12-627-2018 ◽

2018 ◽

Vol 12 (2) ◽

pp. 627-633 ◽

Cited By ~ 9

Author(s):

Knut Alfredsen ◽

Christian Haas ◽

Jeffrey A. Tuhtan ◽

Peggy Zinke

Keyword(s):

Structure From Motion ◽

Low Cost ◽

Cold Climate ◽

River Ice ◽

River Morphology ◽

Ice Thickness ◽

Surface Model ◽

Engineering Data ◽

Site Access ◽

Remote Sensing Methods

Abstract. In cold climate regions, the formation and break-up of river ice is important for river morphology, winter water supply, and riparian and instream ecology as well as for hydraulic engineering. Data on river ice is therefore significant, both to understand river ice processes directly and to assess ice effects on other systems. Ice measurement is complicated due to difficult site access, the inherent complexity of ice formations, and the potential danger involved in carrying out on-ice measurements. Remote sensing methods are therefore highly useful, and data from satellite-based sensors and, increasingly, aerial and terrestrial imagery are currently applied. Access to low cost drone systems with quality cameras and structure from motion software opens up a new possibility for mapping complex ice formations. Through this method, a georeferenced surface model can be built and data on ice thickness, spatial distribution, and volume can be extracted without accessing the ice, and with considerably fewer measurement efforts compared to traditional surveying methods. A methodology applied to ice mapping is outlined here, and examples are shown of how to successfully derive quantitative data on ice processes.

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Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

Author(s):

Mustafa Berk Duygu ◽

Zuhal Akyürek

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

High Temporal Resolution ◽

Land Surface Models ◽

Statistical Measures ◽

Surface Models ◽

Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

The Cryosphere Discussions ◽

10.5194/tcd-7-2333-2013 ◽

2013 ◽

Vol 7 (3) ◽

pp. 2333-2372

Author(s):

E. Kantzas ◽

M. Lomas ◽

S. Quegan ◽

E. Zakharova

Keyword(s):

Land Surface ◽

Field Data ◽

Climate Models ◽

Optimization Techniques ◽

The Arctic ◽

Surface Model ◽

Land Surface Models ◽

Snow Density ◽

Data Set ◽

Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

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Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

10.5194/hess-2020-359 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Large Scale ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Land Surface Models ◽

Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

10.5194/egusphere-egu21-4083 ◽

2021 ◽

Author(s):

Mengyuan Mu ◽

Martin De Kauwe ◽

Anna Ukkola ◽

Andy Pitman ◽

Teresa Gimeno ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Strong Impact ◽

Water Fluxes ◽

Land Surface Models ◽

Groundwater Aquifer ◽

Surface Models

Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m-2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.

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