Calculating macroalgal height and biomass using bathymetric LiDAR and a comparison with surface area derived from satellite data in Nova Scotia, Canada

2020 ◽  
Vol 63 (1) ◽  
pp. 43-59 ◽  
Author(s):  
Tim Webster ◽  
Candace MacDonald ◽  
Kevin McGuigan ◽  
Nathan Crowell ◽  
Jean-Sebastien Lauzon-Guay ◽  
...  
Keyword(s):  
Nova Scotia ◽  
Surface Area ◽  
Survey Data ◽  
Satellite Data ◽  
High Tide ◽  
Field Measurements ◽  
Ground Truth ◽  
Cost Effective ◽  
Satellite System ◽  
Ground Truth Data

AbstractThe ability to map and monitor the macroalgal coastal resource is important to both the industry and the regulator. This study evaluates topo-bathymetric lidar (light detection and ranging) as a tool for estimating the surface area, height and biomass of Ascophyllum nodosum, an anchored and vertically suspended (floating) macroalga, and compares the surface area derived from lidar and WorldView-2 satellite imagery. Pixel-based Maximum Likelihood classification of low tide satellite data produced 2-dimensional maps of intertidal macroalgae with overall accuracy greater than 80%. Low tide and high tide topo-bathymetric lidar surveys were completed in southwestern Nova Scotia, Canada. Comparison of lidar-derived seabed elevations with ground-truth data collected using a survey grade global navigation satellite system (GNSS) indicated the low tide survey data have a positive bias of 15 cm, likely resulting from the seaweed being draped over the surface. The high tide survey data did not exhibit this bias, although the suspended canopy floating on the water surface reduced the seabed lidar point density. Validation of lidar-derived seaweed heights indicated a mean difference of 30 cm with a root mean square error of 62 cm. The modelled surface area of seaweed was 28% greater in the lidar model than the satellite model. The average lidar-derived biomass estimate was within one standard deviation of the mean biomass measured in the field. The lidar method tends to overestimate the biomass compared to field measurements that were spatially biased to the mid-intertidal level. This study demonstrates an innovative and cost-effective approach that uses a single high tide bathymetric lidar survey to map the height and biomass of dense macroalgae.

scholarly journals EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 573-578 ◽  
Author(s):  
◽  
S. S. Ray
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Ground Truth ◽  
Kappa Coefficient ◽  
Ground Truth Data ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Crop Classification ◽  
Sentinel 2

<p><strong>Abstract.</strong> Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10&amp;thinsp;m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.</p>

scholarly journals POST-FIRE FORESTRY RECOVERY MONITORING USING HIGH-RESOLUTION MULTISPECTRAL IMAGERY FROM UNMANNED AERIAL VEHICLES

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 301-305 ◽  
Author(s):  
L. Pádua ◽  
T. Adão ◽  
N. Guimarães ◽  
A. Sousa ◽  
E. Peres ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Tree Height ◽  
Ground Truth ◽  
Cost Effective ◽  
Forest Disturbances ◽  
Ground Truth Data ◽  
High Resolution Data ◽  
Aerial Vehicles ◽  
Fire Scenarios

<p><strong>Abstract.</strong> In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios.</p>

scholarly journals Resolution and Accuracy of an Airborne Scanning Laser System for Beach Surveys

2013 ◽  
Vol 30 (10) ◽  
pp. 2452-2464 ◽  
Author(s):  
J. H. Middleton ◽  
C. G. Cooke ◽  
E. T. Kearney ◽  
P. J. Mumford ◽  
M. A. Mole ◽  
...  
Keyword(s):  
Large Scale ◽  
Laser System ◽  
Ground Truth ◽  
Satellite System ◽  
Airborne Lidar ◽  
Scanning Laser ◽  
Lidar Data ◽  
Ground Truth Data ◽  
Short Period ◽  
Airborne Lidar Data

Abstract Airborne scanning laser technology provides an effective method to systematically survey surface topography and changes in that topography with time. In this paper, the authors describe the capability of a rapid-response lidar system in which airborne observations are utilized to describe results from a set of surveys of Narrabeen–Collaroy Beach, Sydney, New South Wales, Australia, over a short period of time during which significant erosion and deposition of the subaerial beach occurred. The airborne lidar data were obtained using a Riegl Q240i lidar coupled with a NovAtel SPAN-CPT integrated Global Navigation Satellite System (GNSS) and inertial unit and flown at various altitudes. A set of the airborne lidar data is compared with ground-truth data acquired from the beach using a GNSS/real-time kinematic (RTK) system mounted on an all-terrain vehicle. The comparison shows consistency between systems, with the airborne lidar data being less than 0.02 m different from the ground-truth data when four surveys are undertaken, provided a method of removing outliers—developed here and designated as “weaving”—is used. The combination of airborne lidar data with ground-truth data provides an excellent method of obtaining high-quality topographic data. Using the results from this analysis, it is shown that airborne lidar data alone produce results that can be used for ongoing large-scale surveys of beaches with reliable accuracy, and that the enhanced accuracy resulting from multiple airborne surveys can be assessed quantitatively.

scholarly journals From Village to Globe: A Dynamic Real-Time Map of African Fields Through PlantVillage

2021 ◽  
Vol 5 ◽  
Author(s):  
Annalyse Kehs ◽  
Peter McCloskey ◽  
John Chelal ◽  
Derek Morr ◽  
Stellah Amakove ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Cost Effective ◽  
Quality Data ◽  
Learning Tools ◽  
High Quality ◽  
Ground Truth Data ◽  
Cost Effective Method ◽  
High Throughput Method ◽  
Major Bottleneck

A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.

scholarly journals Impacts of forest spatial structure on variation of the multipath phenomenon of navigation satellite signals

2019 ◽  
Vol 61 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Michał Brach ◽  
Krzysztof Stereńczak ◽  
Leszek Bolibok ◽  
Łukasz Kwaśny ◽  
Grzegorz Krok ◽  
...  
Keyword(s):  
Forest Structure ◽  
Ground Truth ◽  
Satellite System ◽  
Forest Stand ◽  
Navigation Satellite ◽  
Radio Waves ◽  
Ground Truth Data ◽  
Significant Difference ◽  
Gnss Measurements ◽  
Global Navigation Satellite

AbstractThe GNSS (Global Navigation Satellite System) receivers are commonly used in forest management in order to determine objects coordinates, area or length assessment and many other tasks which need accurate positioning. Unfortunately, the forest structure strongly limits access to satellite signals, which makes the positioning accuracy much weak comparing to the open areas. The main reason for this issue is the multipath phenomenon of satellite signal. It causes radio waves reflections from surrounding obstacles so the signal do not reach directly to the GNSS receiver’s antenna. Around 50% of error in GNSS positioning in the forest is because of multipath effect. In this research study, an attempt was made to quantify the forest stand features that may influence the multipath variability. The ground truth data was collected in six Forest Districts located in different part of Poland. The total amount of data was processed for over 2,700 study inventory plots with performed GNSS measurements. On every plot over 25 forest metrics were calculated and over 25 minutes of raw GNSS observations (1500 epochs) were captured. The main goal of this study was to find the way of multipath quantification and search the relationship between multipath variability and forest structure. It was reported that forest stand merchantable volume is the most important factor which influence the multipath phenomenon. Even though the similar geodetic class GNSS receivers were used it was observed significant difference of multipath values in similar conditions.

scholarly journals Potentiality and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Calò ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Satellite Data ◽  
Low Cost ◽  
Ground Truth ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Ground Truth Data ◽  
Flood Peak ◽  
Sar Data ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Characterization of Alpine peatlands based on remote sensing of vegetation and water content

2021 ◽  
Author(s):  
Sonia Silvestri ◽  
Alessandra Borgia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Regional Scale ◽  
Ground Truth ◽  
Organic Carbon Content ◽  
Ground Truth Data ◽  
The World ◽  
The Eu

&lt;p&gt;Storing up to 70 kg of carbon per cubic meter, peatlands are among the most carbon-dense environments in the world. If in pristine conditions, peatlands support a number of ecosystem services as for example water retention and mitigation of droughts and floods, water purification, water availability to wildlife. Their preservation is one of the main goals of the EU policy and of other initiatives around the world.&lt;/p&gt;&lt;p&gt;Despite their importance, Alpine peatlands have been rarely studied and their presence is not even included in the EU maps, as for example the JRC Relative Cover of Peat Soils map, and only some sites are included in the Corine Land Cover map. The precise localization of peatland sites and the assessment of their extent is the first fundamental step for the implementation of adequate conservation policies. To this end, satellite remote sensing is the ideal instrument to provide adequate spatial resolution to detect and characterize Alpine peatlands at the regional scale. In this study, we use Sentinal-2 satellite data combined with 2m spatial resolution digital elevation model (from LiDAR data) to detect and quantify the extent of peatlands in the Trentino - Alto Adige region, an area of about 12,000 sq km located in the heart of the Italian Alpine region. Ground truth data include 71 peatlands that cover a total surface of more than 2,000 sq m. Field campaigns and lab analyses on some selected sites show that, on average, the sampled peatlands have depth of about 1m, Bulk Density of 0.128 g cm&lt;sup&gt;-3&lt;/sup&gt; and LOI of 63%, hence indicating that the organic carbon content by soil volume is high, being on average 0.04 g cm&lt;sup&gt;-3&lt;/sup&gt;. Satellite data analysis allowed us to detect a large number of peatland sites with high accuracy, thus confirming the importance of Alpine peatlands as carbon stock sites for the region. Moreover, thanks to the correlation between two indices (NDVI and NDWI) we could characterize the water content of these sites, hence analyzing its seasonal variation and inferring possible future scenarios linked to climate change effects.&lt;/p&gt;

Measuring Ocean Bottom Pressure at the North Pole

2014 ◽  
Vol 48 (5) ◽  
pp. 52-68 ◽  
Author(s):  
Cecilia Peralta-Ferriz ◽  
James H. Morison ◽  
Scott E. Stalin ◽  
Christian Meinig
Keyword(s):  
Arctic Ocean ◽  
Ground Truth ◽  
Satellite System ◽  
North Pole ◽  
Ocean Bottom ◽  
Bottom Pressure ◽  
Ground Truth Data ◽  
Ocean Bottom Pressure ◽  
The North ◽  
Aircraft Landing

AbstractHigh-precision deep Arctic Bottom Pressure Recorders (ABPRs) were developed to measure ocean bottom pressure variations in the perennial ice-covered Arctic Ocean. The ABPRs use the tsunami detection DART acoustic modem technology and have been programmed to store and transmit the data acoustically without the need to recover the instrument. ABPRs have been deployed near the North Pole, where the ice cover is a year-round challenge for access with a ship. Instead, the ABPRs have been built as light-weight mechanical systems that we can install using aircraft landing on the ice. ABPRs have provided the first records of uninterrupted pressure data over continuous years ever made in the central Arctic. The ABPR data have allowed us to identify and understand modes of Arctic Ocean bottom pressure variability that were unknown before the ABPR records and have offered new means of investigating and understanding the rapidly changing Arctic system. The ABPR records have also shown outstanding agreement with the satellite-sensed ocean bottom pressure anomalies from GRACE, providing ground truth data for validation of the satellite system. Due to the successful science findings as well as the ABPRs' capability to fulfill the upcoming potential gaps of pressure measurements between GRACE and a GRACE follow-on mission, we highlight the urgent need to develop and maintain an Arctic observing network using ABPRs.

scholarly journals ANALYSIS OF WHEAT CROP FORECASTS, IN INDIA, GENERATED USING REMOTE SENSING DATA, UNDER FASAL PROJECT

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 223-228
Author(s):  
S. Kumar ◽  
S. Saxena ◽  
S. K. Dubey ◽  
K. Chaudhary ◽  
S. Sehgal ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Spatial Resolution ◽  
Satellite Data ◽  
National Level ◽  
Remote Sensing Data ◽  
State Level ◽  
Ground Truth ◽  
District Level ◽  
Wheat Crop ◽  
Ground Truth Data

<p><strong>Abstract.</strong> Wheat (<i>Triticum aestivum</i> L.) is a major cereal crop of the world, which plays an important role in global food and nutritional security. In India, wheat grown areas are more as compared to other food crops, except for rice. The total area under wheat cultivation is 30.60 million hectares with production of 98.38 million tonnes and the productivity is 3.22 tonnes /hectare (DES, 2017). The main objective of this paper is to highlight the development of satellite-based methodology, compare the relative deviations (%) at national level, RMSE (%) and correlation coefficient at state level and correlation coefficient at district level between DES and FASAL estimates from 2013 to 2017. It was observed that the area and production estimates improved with improvement in the satellite resolution and ground truth data. During the last 10 years of estimation the spatial resolution of the satellite data has gradually improved from 23.5 meter of (Reourcesat-2, LISS-III) and finally 10&amp;thinsp;m of Sentinel-2, MSI, which is being currently used for acreage estimation purpose. Hooda R.S et al (2006) studied that the improvement in the spatial resolution, spectral and temporal resolution of the satellite data has also improved the crop discrimination. Both accuracy as well as precision of the estimates has improved over the years from 2013 to 2017, as reflected by relative deviation, RMSE (%) and Coefficient of correlation values at national, state and district level respectively.</p>

scholarly journals Economic losses caused by tree species proportions and site type errors in forest management planning

Silva Fennica ◽  
2019 ◽  
Vol 53 (2) ◽  
Author(s):  
Arto Haara ◽  
Annika Kangas ◽  
Sakari Tuominen
Keyword(s):  
Satellite Data ◽  
Tree Species ◽  
Forest Inventory ◽  
Ground Truth ◽  
Study Data ◽  
Aerial Image ◽  
Economic Losses ◽  
Ground Truth Data ◽  
Site Type ◽  
Species Proportions

The aim of this study was to estimate economic losses, which are caused by forest inventory errors of tree species proportions and site types. Our study data consisted of ground truth data and four sets of erroneous tree species proportions. They reflect the accuracy of tree species proportions in four remote sensing data sets, namely 1) airborne laser scanning (ALS) with 2D aerial image, 2) 2D aerial image, 3) 3D and 2D aerial image data together and 4) satellite data. Furthermore, our study data consisted of one simulated site type data set. We used the erroneous tree species proportions to optimise the timing of forest harvests and compared that to the true optimum obtained with ground truth data. According to the results, the mean losses of Net Present Value (NPV) because of erroneous tree species proportions at an interest rate of 3% varied from 124.4 € ha to 167.7 € ha. The smallest losses were observed using tree species proportions predicted using ALS data and largest using satellite data. In those stands, respectively, in which tree species proportion errors actually caused economic losses, they were 468 € ha on average with tree species proportions based on ALS data. In turn, site type errors caused only small losses. Based on this study, accurate tree species identification seems to be very important with respect to operational forest inventory.–1–1–1

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