scholarly journals Radar vision in the mapping of forest biodiversity from space

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Soyeon Bae ◽  
Shaun R. Levick ◽  
Lea Heidrich ◽  
Paul Magdon ◽  
Benjamin F. Leutner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Large Scale ◽  
External Validation ◽  
Predictive Ability ◽  
Radar Data ◽  
Forest Regions ◽  
Local Species ◽  
Spatio Temporal ◽  
Derived Data

Abstract Recent progress in remote sensing provides much-needed, large-scale spatio-temporal information on habitat structures important for biodiversity conservation. Here we examine the potential of a newly launched satellite-borne radar system (Sentinel-1) to map the biodiversity of twelve taxa across five temperate forest regions in central Europe. We show that the sensitivity of radar to habitat structure is similar to that of airborne laser scanning (ALS), the current gold standard in the measurement of forest structure. Our models of different facets of biodiversity reveal that radar performs as well as ALS; median R² over twelve taxa by ALS and radar are 0.51 and 0.57 respectively for the first non-metric multidimensional scaling axes representing assemblage composition. We further demonstrate the promising predictive ability of radar-derived data with external validation based on the species composition of birds and saproxylic beetles. Establishing new area-wide biodiversity monitoring by remote sensing will require the coupling of radar data to stratified and standardized collected local species data.

Confronting and combining high-resolution simulations (ICON-LEM) with remote sensing observations

2020 ◽  
Author(s):  
Vera Schemann ◽  
Mario Mech
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
General Structure ◽  
Radar Data ◽  
Synoptic Variability ◽  
Lateral Boundary Conditions ◽  
Large Eddy ◽  
Land Surfaces ◽  
Selection Of

<p>The current generation of large-eddy models (e.g. the ICON-LEM) allows us to go beyond idealized simulations and to capture synoptic variability by including heterogeneous land surfaces as well as lateral boundary conditions. This would offer the possibility to compare simulations and observations of clouds on a detailed day-to-day basis. But while LEMs are able to reach resolutions that start to be comparable to state-of-the-art observations (e.g. Radar data), they are still facing the issue of different parameter spaces: either the model output has to be transfered to observable quantities, or the other way around. We will present examples from recent field campaigns (e.g. ACLOUD, EUREC4A), where we combined ICON-LEM simulations with remote sensing observations by applying the Passive and Active Microwave TRAnsfer simulator (PAMTRA). By the selection of examples, we will show the potential of this combination of high-resolution modeling, remote sensing observations and forward simulations at different places under different conditions (Arctic, European and Caribbean). While the general structure of clouds (e.g. timing, type, height) is often already captured quite well, the comparison to the remote sensing observations allows us to also get insights into the composition of clouds and to constrain microphysical parameterizations as well as the influence of the large-scale forcing on a more detailed level.</p>

scholarly journals The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

2013 ◽  
Vol 6 (6) ◽  
pp. 10699-10730
Author(s):  
A. Devasthale ◽  
L. Norin
Keyword(s):  
Temporal Variability ◽  
High Intensity ◽  
Large Scale ◽  
Weather Radar ◽  
Radar Data ◽  
The Arctic ◽  
Northern Sweden ◽  
Late Night ◽  
Spatio Temporal ◽  
Precipitation Events

Abstract. Using measurements from the national network of 12 weather radar stations for the last decade (2000–2010), we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects namely, the diurnal cycle of precipitation and its seasonality, the dominant time scale (diurnal vs. seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate to high intensity events (precipitation > 0.34 mm (3 h)−1) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high intensity events (precipitation > 1.7mm (3 h)−1) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

scholarly journals Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia

2017 ◽  
Author(s):  
Mulugeta Genanu ◽  
Tena Alamirew ◽  
Gabriel Senay ◽  
Mekonnen Gebremichael
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Large Scale ◽  
Surface Energy Balance ◽  
Wet Season ◽  
Total Crop ◽  
Spatio Temporal ◽  
Water Surplus ◽  
Soil Moisture Variability

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ETa) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ETa over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ETa values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ETa is limited to water surplus areas only and in wet season, ETa was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ETa over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ETa estimated for the whole estate were 107 Mm3, 140 Mm3, and 178 Mm3 using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Developing statistically driven eco-engineering designs from LiDAR and structure from motion surveys for marine artificial structures.

2020 ◽  
Author(s):  
Peter Lawrence ◽  
Ally Evans ◽  
Paul Brooks ◽  
Tim D'Urban Jackson ◽  
Stuart Jenkins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Habitat Loss ◽  
Structure From Motion ◽  
Laser Scanning ◽  
Large Scale ◽  
Spatial Scales ◽  
Coastal Development ◽  
Ecological Design ◽  
Artificial Structures ◽  
Engineering Research

<p>Coastal ecosystems are threatened by habitat loss and anthropogenic “smoothing” as hard engineering approaches to sea defence, such as sea-walls, rock armouring, and offshore reefs, become common place. These artificial structures use homogenous materials (e.g. concrete or quarried rock) and as a result, lack the surface heterogeneity of natural rocky shoreline known to play a key role in niche creation and higher species diversity. Despite significant investment and research into soft engineering and ecologically sensitive approaches to coastal development, there are still knowledge gaps, particularly in relation to how patterns that are observed in nature can be utilised to improve artificial shores.</p><p>Given the technical improvements and significant reductions in cost within the portable remote sensing field (structure from motion and laser scanning), we are now able to plug gaps in our understanding of how habitat heterogeneity can influence overall site diversity. These improvements represent an excellent opportunity to improve our understanding of the spatial scales and complexity of habitats that species occur within and ultimately improve the ecological design of engineered structures in areas experiencing “smoothing” and habitat loss.</p><p>In this talk, I will highlight how advances in remote sensing techniques can be applied to context-specific ecological problems, such as low diversity and loss of rare species within marine infrastructure. I will describe our approach to combining large-scale ecological, 3D geophysical and engineering research to design statistically-derived ecologically-inspired solutions to smooth artificial surfaces. We created experimental concrete enhancement units and deployed them at a number of coastal locations. I will present preliminary ecological results, provide a workflow of unit development and statistical approaches, and finally discuss how these advances may improve future ecological intervention and design options.</p>

scholarly journals A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images

2020 ◽  
Vol 12 (3) ◽  
pp. 450 ◽  
Author(s):  
Quan Xiong ◽  
Yuan Wang ◽  
Diyou Liu ◽  
Sijing Ye ◽  
Zhenbo Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Solar Altitude ◽  
Time Consumption ◽  
Detection Approach ◽  
Spatio Temporal

Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.

scholarly journals Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia

2016 ◽  
Author(s):  
Mulugeta Genanu ◽  
Tena Alamirew ◽  
Gabriel Senay ◽  
Mekonnen Gebremichael
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Large Scale ◽  
Surface Energy Balance ◽  
Meteorological Data ◽  
Wet Season ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Water Surplus

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map pixel-by-pixel basis, and compare the actual evapotranspiration (ETa) of the Wonji Shoa Sugarcane Estate using Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms on Landsat7 ETM+ images acquired on four days in 2002. The algorithms were based on image processing which uses spatially distributed spectral satellite data and ground meteorological data to derive the surface energy balance components. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0–6.85, 0.0–9.36, 0.0–3.61, 0.0–6.83 mm/day; using SSEB 0.0–6.78, 0.0–7.81, 0.0–3.65, 0.0–6.46 mm/day, and SSEBop were 0.05–8.25, 0.0–8.82, 0.2–4.0, 0.0–7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ETa over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop and water storage areas had higher ETa values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season ETa is limited to water surplus areas only and in wet season, ETa was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ETa over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ETa estimated for the whole estate were 107 Mm3, 140 Mm3, and 178 Mm3 using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

scholarly journals Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest

2021 ◽  
Vol 13 (11) ◽  
pp. 2063
Author(s):  
Luka Jurjević ◽  
Mateo Gašparović ◽  
Xinlian Liang ◽  
Ivan Balenović
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Large Scale ◽  
Deciduous Forest ◽  
Information Source ◽  
Data Sources ◽  
Pedunculate Oak ◽  
Absolute Deviation ◽  
Close Range ◽  
Remote Sensing Techniques

Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.

The Comparative Study on the Automatic Extraction Methods of Artificial Channel Based on ETM Image

2012 ◽  
Vol 500 ◽  
pp. 506-510
Author(s):  
Cheng Lu ◽  
Qiang Li ◽  
Li Jun Yu
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Large Scale ◽  
Water Shortage ◽  
Extraction Methods ◽  
Scientific Basis ◽  
Wetland Protection ◽  
Gis Technology ◽  
Band Method ◽  
Spatio Temporal

It’s urgent for China to solve the water shortage. Quickly and accurately extracting water resources from satellite remote sensing has become an important means of the investigation and monitoring of water resources and wetland protection. The fact that the spatio-temporal span of channel is large made the investigation difficult especially by the conventional way. Remote Sensing plays an increasing important role in the water resources protection with advantages of large scale, integration, dynamics and fastness. The RS images recorded the truth of the surface landscape in history and can reflect the distributing and the status quo of the channel in different courses of history. The article analyses the spectral and spatial feature of channel in ETM images in order to extraction the channel automatically with the different RS methods combined with GIS technology. A comparison among these methods is made. In addition, the article assesses the results of single-band method and multi-band method qualitatively and quantitatively. This study provide a scientific basis for the protection of water resource.

GIS of the chora of Cyrene: fortifications and Christian buildings in Late Antiquity

2016 ◽  
Vol 47 ◽  
pp. 31-66 ◽  
Author(s):  
A. Abdalrahim Sheriff Saad ◽  
S. Farag Abdel Hati ◽  
Sonia Antonelli ◽  
Oliva Menozzi ◽  
Veronica Petraccia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Late Antiquity ◽  
Laser Scanning ◽  
Large Scale ◽  
Landscape Archaeology ◽  
Local Economy ◽  
Gis Mapping ◽  
Differential Gps ◽  
Non Invasive ◽  
Traditional Research

AbstractThe project of mapping the chora of Cyrene, for the team of Chieti University, started between 1999 and 2001 as a layer of a ‘macro-GIS’ of the area to the east of Cyrene, that is, the transect between Cyrene and El-Gubba/Qubbah. Because of the large scale of the area and the monumentality of the sites, the team is composed of several research units based around a large number of scholars and technicians. The project employs a suite of traditional methodologies for the study of landscape archaeology (surveys, GIS mapping, differential GPS, excavations), in combination with technologies integrating the knowledge of the territory (remote sensing on HD satellite photos, geomorphological reconstruction, laser scanning, archaeometric analysis, non-invasive geophysical prospection and infrared diagnostic analysis). The large quantity of data coming from this wide approach has been organised into a flexible and multilayer GIS. A joint team of Libyan and Italian archaeologists and technicians is testing a common protocol for monitoring the monuments and sites in the territory, using surveys and remote sensing analysis, which has intensified during these problematic periods, and regularly analysing satellite sets over the past four years.The project aims to map and document as much as possible in this territory, to identify the location of the region's so-called ‘minor sites’, which are numerous and almost unknown. They were, from the Late Classical to the Islamic periods, vital sites for the management of the local economy. This paper presents the main issues relating to settlements and sites in Late Antiquity, concentrating mainly on fortifications along the limes and basilicas within the area of the transect. Moreover, in the presentation of the data, the GIS approach has been integrated here with data coming both from the remote sensing and from more traditional research approaches, such as planimetrical and typological analysis of the buildings, study of the sources and detailed mapping of the building techniques.

scholarly journals A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7406
Author(s):  
Nitu Ojha ◽  
Olivier Merlin ◽  
Abdelhakim Amazirh ◽  
Nadia Ouaadi ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Radar Data ◽  
Passive Microwave ◽  
Field Scale ◽  
Data Set ◽  
Thermal Data ◽  
Spatio Temporal

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

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