scholarly journals Evaluating drone truthing as an alternative to ground truthing : an example with wetland plant identification

2021 ◽  
Author(s):  
Glenn Suir ◽  
Christina Saltus ◽  
Charles Sasser ◽  
J. Harris ◽  
Molly Reif ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ground Truth ◽  
Wetland Vegetation ◽  
Unmanned Aircraft ◽  
Wetland Plant ◽  
Plant Identification ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Aircraft System ◽  
Accuracy Assessments

Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.

scholarly journals Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

2019 ◽  
pp. 60-65
Author(s):  
K Choudhary ◽  
M S Boori ◽  
A Kupriyanov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Groundwater Potential ◽  
Groundwater Potential Zone ◽  
Potential Zone ◽  
Ground Truth Data ◽  
Groundwater Availability ◽  
Sensing Data

The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.

scholarly journals Geomorphological evolution of landslides near an active normal fault in northern Taiwan, as revealed by lidar and unmanned aircraft system data

2018 ◽  
Vol 18 (3) ◽  
pp. 709-727 ◽  
Author(s):  
Kuo-Jen Chang ◽  
Yu-Chang Chan ◽  
Rou-Fei Chen ◽  
Yu-Chung Hsieh
Keyword(s):  
Remote Sensing ◽  
Normal Fault ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Remote Sensing Techniques ◽  
Aircraft System ◽  
Geomorphological Evolution ◽  
Northern Taiwan ◽  
Landslide Morphology

Abstract. Several remote sensing techniques, namely traditional aerial photographs, an unmanned aircraft system (UAS), and airborne lidar, were used in this study to decipher the morphological features of obscure landslides in volcanic regions and how the observed features may be used for understanding landslide occurrence and potential hazard. A morphological reconstruction method was proposed to assess landslide morphology based on the dome-shaped topography of the volcanic edifice and the nature of its morphological evolution. Two large-scale landslides in the Tatun volcano group in northern Taiwan were targeted to more accurately characterize the landslide morphology through airborne lidar and UAS-derived digital terrain models and images. With the proposed reconstruction method, the depleted volume of the two landslides was estimated to be at least 820 ± 20  ×  106 m3. Normal faulting in the region likely played a role in triggering the two landslides, because there are extensive geological and historical records of an active normal fault in this region. The subsequent geomorphological evolution of the two landslides is thus inferred to account for the observed morphological and tectonic features that are indicative of resulting in large and life-threatening landslides, as characterized using the recent remote sensing techniques.

scholarly journals Vegetation Cover Change Detection and Assessment in Arid Environment Using Multi-temporal Remote Sensing images and Ecosystem Management Approach

2016 ◽  
Author(s):  
Anwar Abdelrahman Aly ◽  
Abdulrasoul Mosa Al-Omran ◽  
Abdulazeam Shahwan Sallam ◽  
Mohammad Ibrahim Al-Wabel ◽  
Mohammad Shayaa Al-Shayaa
Keyword(s):  
Remote Sensing ◽  
Ecosystem Management ◽  
Vegetation Cover ◽  
Secondary Data ◽  
Ground Truth ◽  
Arid Environment ◽  
Management Approach ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Multi Temporal

Abstract. Vegetation cover (VC) changes detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the centre of Saudi Arabia. Characteristics and dynamics of VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images; Landsat4 TM 1987, Landsat7 ETM+ 2000, and Landsat8 2013. The VC pattern and changes were linked to both natural and social processes to investigate the drivers responsible for the change. The analyses of the three satellite images concluded that the surface area of the VC increased by 107.4 % between 1987 and 2000, it was decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment; while the south-western part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

Soil salinity assessment using temporal series of Sentinel2 satellite images in irrigated paddy fields of Western Africa

2020 ◽  
Author(s):  
Moussa Issaka ◽  
Walter Christian ◽  
Michot Didier ◽  
Pichelin Pascal ◽  
Nicolas Hervé ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Ground Truth ◽  
Growing Season ◽  
Bare Soil ◽  
Atmospheric Effects ◽  
Ground Truth Data ◽  
Multi Temporal ◽  
Salinity Index ◽  
Niger River

<p>Salinization and alkalinization are worldwide among the soil degradation threats in irrigated schemes affecting soil productivity. Niger River basin irrigated schemes in the Sahel arid zone are no exception (ONAHA, 2011). The use of remote sensing for identifying and evaluating the level of these phenomena is an interesting tool. The launching of the Sentinel2 satellite constellation (2015) brings new perspectives with high spectral and temporal resolutions images. The aim of this study was to develop a methodology for detection of salt-affected soils in this climatic condition.</p><p>To achieve our goal, we used two types of data: remote sensing and ground truth data.</p><p>Two complementary approaches were used: one by observing salinity on bare soil by the use of salinity index (SI) and the other by observing the indirect effects of salinity on the vegetation during eight (8) rice growth phases  using vegetation index NDVI.</p><p>Remote sensing data were acquired from multi temporal sentinel2 images over 4 years (from 11/12/2015 to 30/11/2019). One hundred and fifty seven (157) images were downloaded (one image each 5 days) and corrected from atmospheric effects and some bands resampled to 5 m using python software. The salinity and vegetation indices were calculated. NDVI index was calculated and NDVI integral between NDVI curve and the threshold of 0.21 NDVI calculated for the eight growing cycles.</p><p>Ground truth data were collected in 2019 during the dry growing season (January – may 2019) from 24 calibration plots and 40 validation plots. One hundred and twenty (120) soil samples collected and analyzed for pH and electrical conductivity and finally forty six (46) biomass samples were collected, air dried and weighed for biomass yield and 46 grains samples collected for grain yield.</p><p>NDVI integral proved to be good indicator for yield variations and could distinguish crops behavior according to the growing period. It also makes it possible to distinguish plots which were not cultivated or with weak growth due to strong constraints of which the main one is salinity. It showed also that the effect of salinity on growth differs according to the growing season and the possibility of managing irrigation. Bare soil analysis distinguishes fields with different salinity indexes despite the low number of dates for which bare soil can be observed.</p><p>Ascending Hierarchical Classification (AHC) enabled to identify four classes of NDVI dynamics over time and bare soil salinity index. High saline soils according to direct soil measurements were related to the class characterized by high frequency of no-cultivation during the dry season and low NDVI integral during the wet season. Multi-temporal Sentinel2 images analysis enabled therefore to detect rice crop fields affected by salinity through its influence on crop behavior. This approach will be tested over the whole paddy schemes of the Niger River valley.</p>

EFFICACITE DES IMAGES RADAR ET INFRAROUGE THERMIQUE, ET DE LA PHOTO COULEUR INFRAROUGE POUR L’INVENTAIRE DES CULTURES

1980 ◽  
Vol 60 (4) ◽  
pp. 1077-1085
Author(s):  
ROGER PAQUIN ◽  
GILLES LADOUCEUR
Keyword(s):  
Remote Sensing ◽  
Ground Truth ◽  
Thermal Infrared ◽  
Infrared Imagery ◽  
Ground Truth Data ◽  
Infrared Photography ◽  
Remote Sensing Technique ◽  
Field Crops ◽  
Radar Imagery ◽  
Thermal Infrared Imagery

Crops from 888 fields in a 300-km2 area between Rougemont and St-Hyacinthe were surveyed to compare the efficiency of radar (3–80 cm) and thermal infrared (8–14 μm) imagery with color infrared photography for crop identification. The color infrared photography and the thermal infrared imagery were taken by the Canadian Centre for Remote Sensing on 11 Aug. 1978, and the radar imagery by Intera on 19 Aug. The analysis of the thermal infrared imagery showed some correlations with the ground truth data, but the image could not be used in crop identification. Accordingly, observations from radar imagery could not serve in crop identification. However, similarities were observed between the radar and the thermal infrared imageries. The results showed once more that the color infrared photography as a remote sensing technique is the most useful to survey field crops.

scholarly journals Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation: taxonomy, abundance and population ecological models

2018 ◽  
Author(s):  
Christian Damgaard
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Learning Algorithms ◽  
Plant Competition ◽  
Image Data ◽  
Ground Truth ◽  
Ecological Models ◽  
Machine Learning Algorithms ◽  
Ground Truth Data ◽  
Ground Truthing

AbstractIn order to fit population ecological models, e.g. plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

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