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2022 ◽  
Vol 270 ◽  
pp. 112881
Author(s):  
Brianna D. Corsa ◽  
Mylène Jacquemart ◽  
Michael J. Willis ◽  
Kristy F. Tiampo
Keyword(s):  

2022 ◽  
Vol 14 (2) ◽  
pp. 968
Author(s):  
Tuo Han ◽  
Qi Feng ◽  
Tengfei Yu ◽  
Xiaofang Zhang ◽  
Xiaomei Yang ◽  
...  

Stomatal conductance (gs), the process that governs plant carbon uptake and water loss, is fundamental to most Land Surface Models (LSMs). With global change accelerating, more attention should be paid to investigating stomatal behavior, especially in extremely arid areas. In this study, gas exchange measurements and environmental/biological variables observations during growing seasons in 2016 and 2017 were combined to investigate diurnal and seasonal characteristics of gs and the applicability of the optimal stomatal conductance model in a desert oasis vineyard. The results showed that the responses of gs to environmental factors (photosynthesis active radiation, PAR; vapor pressure deficit, VPD; and temperature, T) formed hysteresis loops in the daytime. The stomatal conductance slope, g1, a parameter in the unified stomatal optimal model, varied in different growing seasons and correlated with the soil-to-leaf hydraulic conductance (KL). These results indicated the potential bias when using a constant g1 value to simulate gs and highlighted that the water-use strategy of oasis plants might not be consistent throughout the entire growing season. Our findings further help to achieve a better understanding of stomata behavior in responding to climate change and encourage future efforts toward a more accurate parameterization of gs to improve the modeling of LSMs.


Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 126
Author(s):  
Minzhuo Ou ◽  
Shupeng Zhang

Soil moisture is a key state variable in land surface processes. Since field measurements of soil moisture are generally sparse and remote sensing is limited in terms of observation depth, land surface model simulations are usually used to continuously obtain soil moisture data in time and space. Therefore, it is crucial to evaluate the performance of models that simulate soil moisture under various land surface conditions. In this work, we evaluated and compared two land surface models, the Common Land Model version 2014 (CoLM2014) and the Community Land Model Version 5 (CLM5), using in situ soil moisture observations from the Soil Climate Analysis Network (SCAN). The meteorological and soil attribute data used to drive the models were obtained from SCAN station observations, as were the soil moisture data used to validate the simulation results. The validation results revealed that the correlation coefficients between the simulations by CLM5 (0.38) and observations are generally higher than those by CoLM2014 (0.11), especially in shallow soil (0–0.1016 m). The simulation results by CoLM2014 have smaller bias than those by CLM5 . Both models could simulate diurnal and seasonal variations of soil moisture at seven sites, but we found a large bias, which may be due to the two models’ representation of infiltration and lateral flow processes. The bias of the simulated infiltration rate can affect the soil moisture simulation, and the lack of a lateral flow scheme can affect the models’ division of saturated and unsaturated areas within the soil column. The parameterization schemes in land surface models still need to be improved, especially for soil simulations at small scales.


2022 ◽  
Vol 14 (2) ◽  
pp. 337
Author(s):  
Simon Baier ◽  
Nicolás Corti Meneses ◽  
Juergen Geist ◽  
Thomas Schneider

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.


2022 ◽  
Vol 14 (1) ◽  
pp. 219
Author(s):  
Dorothée James ◽  
Antoine Collin ◽  
Antoine Mury ◽  
Rongjun Qin

The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR.


2021 ◽  
Vol 30 (4) ◽  
Author(s):  
Dominik Brétt

The article is focused on the methodology of processing interferometric images and associated challenges with the processing. The article also contains useful links with explanations that can be used for processing data from the Sentinel-1 satellite. To emphasize the data limits of Sentinel-1, several areas of interest were chosen for comparison – in the home environment of the Czech Republic, the Bílina quarry area, and the Žatec area were selected. For subsequent comparison, arid areas with a rich history located in Sudan were selected. The colleagues of the author from the Faculty of The Environment of Jan Evangelista Purkyně University participate in expeditions there. Each of these locations is limited by different parameters – the areas in the Czech Republic are mainly limited by location because of occurring vegetation. Sudan's regions, on the other hand, are arid but are limited by insufficient coverage by capturing the Sentinel-1 satellite. To create digital height models from Sentinel-1 satellite data, it is necessary to search for data with sufficient coherence of images, and parameters of the amount of vegetation with a period between individual images play an important role. The areas were compared with each other and with the commonly available SRTM elevation model, both from a visual point of view – where digital height models and shaded surface models were created, as well as statistically using RMSE.


2021 ◽  
Vol 14 (1) ◽  
pp. 142
Author(s):  
Jiang Ye ◽  
Yuxuan Qiang ◽  
Rui Zhang ◽  
Xinguo Liu ◽  
Yixin Deng ◽  
...  

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.


2021 ◽  
pp. 4557-4570
Author(s):  
Abdalrahman R. Qubaa ◽  
Alaa N. Hamdon ◽  
Taha A. Al Jawwadi

    Today, Unmanned Aerial Vehicles (UAVs) or Drones are a valuable source of data on inspection, surveillance, mapping and 3D modelling matters. Drones can be considered as the new alternative of classic manned aerial photography due to their low cost and high spatial resolution. In this study, drones were used to study archaeological sites. The archaeological Nineveh site, which is a very famous site located in heart of the city of Mosul, in northern Iraq, was chosen. This site was the largest capital of the Assyrian Empire 3000 years ago. The site contains an external wall that includes many gates, most of which were destroyed when Daesh occupied the city in 2014. The local population of the city of Mosul has also largely overtaken the central part of this archaeological site, while the northern and southern parts are still uninhabited. The awareness of the existence of unchanged surface and ground forms in the northern or southern parts led us to prepare an urgent study to interpret the outer surface of those parts and to analyze any discovery in the surface morphology. So, drone data and GIS technologies were used in this study to find any discovery that could aid in understanding the original surface of this ancient site. Visual and digital interpretations of satellite images, drone images, and Digital Surface Models (DSMs) were used to analyze and study the data. As a final result, certain morphological features were identified in the southern parts of the ancient site which could be a new archaeological and compositional discovery with reference to the earlier activities of the ancient society during the Assyrian empires, represented by the ditches and building lands used by the ancient inhabitants. Small modern tunnels penetrated the ancient wall were also discovered in addition, to a drainage canal and a motorway newly constructed during the occupation of the city that had penetrated the archaeological land.


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