scholarly journals Lineament Analysis and Inference of Geological Structures in Bansara-Boki Area, Southeastern Nigeria

2020 ◽  
pp. 28-44
Author(s):  
M. A. Agbebia ◽  
N. Egesi
Keyword(s):  
Landsat Imagery ◽  
Ground Truth ◽  
Southeastern Nigeria ◽  
Topographic Analysis ◽  
Ground Truth Data ◽  
Shuttle Radar Topographic Mission ◽  
Landsat 7 ◽  
Ground Truthing ◽  
Lineament Analysis ◽  
Lineament Extraction

The purpose of this study is to extract lineaments from satellite images in order to contribute to the understanding of the structural geology of parts of Boki and its environs. Shuttle Radar Topographic Mission (SRTM) and Landsat 7 ETM images of path 187 and row 056 were used for the analysis which is processed for automated extraction, validated through ground-truthing of planar and linear geological features displaying altitude of about 233 for Bansara sheet 304. Lineament extraction processing was done using PCI Geomatica version 2016 for Landsat imagery and ArcGIS 10.5 used to generate Digital Elevation Model (DEM) and Slope Map for SRTM imagery. Statistically a total of 3191 count of highly dense lineament were generated ranging between 0.86 to 4.33 km in length with the mean of 1.22 and a standard deviation of 0.83 intersecting at low percentage of 3-6%. The DEM display a range of 1335 to -1335 m sloping in the range of 0-2.81 and 61.224-89.725 m for topographic analysis. The lineament extracted were trending majorly in NW/SE and other minor ones in NE/SW directions some which were agreement with the altitude of the ground-truth data. The variation is possibly as a result of influence from regional process such as deformation, metamorphism, magmatism and method of data acquisition and analysis. Lineament analysis are profound index parameters for engineering of dams, economic mineral and water resources exploration, exploitation, planning and development. It is also useful in geohazard studies and its mitigation as the areas are prone to rockfalls, rockslides, landslides, mudslides and flooding due to high rainfall and human activities at the foot of the highlands.

General Discussion

1983 ◽  
Vol 309 (1508) ◽  
pp. 283-284
Keyword(s):  
Satellite Imagery ◽  
Landsat Imagery ◽  
Ground Truth ◽  
Remotely Sensed ◽  
Individual Plant ◽  
Ground Truth Data ◽  
Remotely Sensed Imagery ◽  
Effective Evaluation ◽  
Geographical Environment ◽  
Multispectral Satellite Imagery

Monica M. Cole (Bedford College, London, U. K.). In contributing to a discussion of the use of multispectral satellite imagery in the exploration for petroleum and minerals covered by Mr Peters I wish to emphasize four points, some of which are relevant also to statements made by Dr Curran in his presentation. The first point is that remotely sensed imagery is a tool and its interpretation a technique to be used as appropriate and integrated with other techniques in mineral exploration. Mr Peters has reviewed the potential of multispectral satellite imagery and emphasized its value in initial reconnaissance studies notably for the identification of geological structures and lithologies. I would emphasize also its value at more advanced stages of exploration when reinterpretation of imagery at large scales and with reference to ground truth data can yield valuable information. My second point, which follows naturally from the first, is that effective interpretation of remotely sensed imagery requires an appreciation of the geographical environment as well as the geological environment. It is reflectances from the components of the geographical environment that produce the colours and tones seen on the colour composites generated from Landsat imagery. Except in arid areas largely devoid of plant cover, in natural terrain reflectances from vegetation dominate over those from soils and bedrock. Their contribution increases with increasing density of cover. The reflectances from different types of vegetation and from individual plant species, however, vary greatly, depending on the geometry of the canopy, the colour of foliage, the size, shape, angle, etc., of leaves, and the turgidity, water content and nutrient status of leaf cells. It is the differences in vegetation cover producing differing reflectances that permit the discrimination of lithologies and identification of structures on colour composites generated from Landsat imagery. In some areas, however, any or all of relict laterite, superficial cover, former and ephemeral drainage systems, and other physiographic features that are the legacies of geomorphological processes, complicate relations. These need to be understood for effective evaluation of imagery for geological purposes. In this context there is no substitute for field investigations, which are essential for the acquisition of ground truth data needed for effective evaluation of imagery.

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.

scholarly journals Comparison and Ground Truthing of Different Remote and Proximal Sensing Platforms to Characterize Variability in a Hedgerow-Trained Vineyard

2021 ◽  
Vol 13 (11) ◽  
pp. 2056
Author(s):  
Cecilia Squeri ◽  
Stefano Poni ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese ◽  
Matteo Gatti
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Yield ◽  
Ground Truth ◽  
Large Degree ◽  
Proximal Sensing ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Sentinel 2

Appropriate characterization of intra-parcel variability is a key element for the effective application of precision farming techniques. Nowadays there are many platforms available to end users differing for pixel spatial resolution and the type of acquisition (remote or proximal). A challenging aspect pertaining to remote sensing image acquisition in the vineyard ecosystem is that, in a large majority of cases, vegetation is discontinuous and single rows alternate with strips of either bare or grassed soil. In this paper, four different satellite platforms (Sentinel-2, Spot-6, Pleiades, and WorldView-3) having different spatial resolution and MECS-VINE® proximity sensor were compared in terms of accuracy at describing spatial variability. Vineyard mapping was coupled with detailed ground truthing of growth, yield, and grape composition variables. The analysis was conducted based on vigor indices (Normalized Difference Vegetation Index or Canopy Index) and using the Moran Index (MI) to assess the degree of spatial auto-correlation for the different variables. The results obtained showed a large degree of intra-plot variability in the main agronomic parameters (pruning weight CV: 33.86%, yield: 32.09%). The univariate Moran index showed a log-linear function relating MI coefficients to the resolution levels. Comparison between vigor indices and agronomic data showed that the highest bivariate MI was reached by Pleiades followed by MECS-VINE® which also did not exhibit the negative effect of the border pixel owing to the proximal scanning acquisition. Despite WorldView-3′s high resolution (1.24 m pixel) allowing very detailed data imaging, the comparison with ground-truth data was not encouraging, probably due to the presence of pure ground pixels, while Sentinel-2 was affected by the oversized pixel at 10 m.

scholarly journals Integrating Hierarchical Statistical Models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models

2021 ◽  
Vol 13 (6) ◽  
pp. 1161
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

In 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.

scholarly journals Continuous Urban Tree Cover Mapping from Landsat Imagery in Bengaluru, India

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 220
Author(s):  
Nils Nölke
Keyword(s):  
Landsat Imagery ◽  
Ground Truth ◽  
Urban Environments ◽  
Urban Trees ◽  
Tree Cover ◽  
Neural Network Approach ◽  
Ground Truth Data ◽  
High Resolution Imagery ◽  
Wide Range ◽  
Very High Resolution Imagery

Percent tree cover maps derived from Landsat imagery provide a useful data source for monitoring changes in tree cover over time. Urban trees are a special group of trees outside forests (TOFs) and occur often as solitary trees, in roadside alleys and in small groups, exhibiting a wide range of crown shapes. Framed by house walls and with impervious surfaces as background and in the immediate neighborhood, they are difficult to assess from Landsat imagery with a 30 m pixel size. In fact, global maps based on Landsat partly failed to detect a considerable portion of urban trees. This study presents a neural network approach applied to the urban trees in the metropolitan area of Bengaluru, India, resulting in a new map of estimated tree cover (MAE = 13.04%); this approach has the potential to also detect smaller trees within cities. Our model was trained with ground truth data from WorldView-3 very high resolution imagery, which allows to assess tree cover per pixel from 0% to 100%. The results of this study may be used to improve the accuracy of Landsat-based time series of tree cover in urban environments.

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 Geospatial Techniques for Paddy Crop Acreage and Yield Estimation

2020 ◽  
pp. 71-79
Author(s):  
Krishna Desai ◽  
N. L. Rajesh ◽  
U. K. Shanwad ◽  
N. Ananda ◽  
B. G. Koppalkar ◽  
...  
Keyword(s):  
Biomass Yield ◽  
Satellite Image ◽  
Ground Truth ◽  
Landsat 8 ◽  
Yield Estimation ◽  
Crop Acreage ◽  
Relative Deviation ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Paddy Crop

Paddy crop acreage and yield estimation using geospatial technology were carried out in North Eastern Dry Zone (Zone-2) covering Shorapur taluk, Yadgir district, Karnataka state, India, during rabi late sown or summer 2016-17 season. The study area is located between 16° 20ꞌ to 17° 45ꞌ north latitude and 76° 04ꞌ to 77° 42ꞌ east longitude, at an elevation of 428 meters above mean sea level. The RESOURCESAT-1 LISS III satellite image of 31st January 2017, 24th February 2017, 20th March 2017 and LANDSAT-8 of 15th April 2017 were used for paddy crop acreage estimation at taluk level. Paddy signatures were identified using ground truth GPS data and then, these temporal imageries were subjected to NDVI classification and estimated the paddy biomass and further validated with the ground-truthing in corresponding to Green Seeker NDVI value. The estimated paddy crop acreage through imagery NDVI were 2145.75 ha, 17602.21 ha, 19838 ha and 23004.01 ha area during Jan-2017, Feb-2017, March-2017 and April-2017 respectively. When these results were compared with acreage estimates as reported by the State Department of Agriculture, shown a relative deviation of 11.41, 35.78, 23.01& 3.89 per cent for Jan-2017, Feb-2017, March-2017 and April-2017 respectively. Therefore, LandSat-8 NDVI paddy acreage has showed significantly on par with the ground truth data at the crop harvest stage. Relative deviation of 10.75 for yield comparison among imagery NDVI biomass yield with the DOA yield estimation infer that NDVI biomass yield estimation would give better result at 90 days after sowing. Positive correlation of NDVI values with estimated acreage and yield, indicates that application of remote sensing techniques for forecasting paddy biomass yield is more accurate, economical and could be beneficial to the policy makers for quick decisions.

scholarly journals Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 309 ◽  
Author(s):  
Isaac Kyere ◽  
Thomas Astor ◽  
Rüdiger Graß ◽  
Michael Wachendorf
Keyword(s):  
Agricultural Land ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Type Model ◽  
Periodic Field ◽  
Ground Truth Data ◽  
Agricultural Planning ◽  
Crop Type ◽  
Multi Temporal

The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

scholarly journals Spectralradiometry and caribou range classification

Rangifer ◽  
1986 ◽  
Vol 6 (2) ◽  
pp. 191 ◽  
Author(s):  
Thomas C. Meredith
Keyword(s):  
Ground Truth ◽  
Classification Criteria ◽  
Landsat Data ◽  
Ground Truth Data ◽  
Cover Type ◽  
Spectral Curves ◽  
Components Analysis ◽  
Ground Truthing

The utility of Landsat in caribou range studies has been limited by problems of heterogeneity in cover type at the scale of pixies and by logistic barriers to ground truthing. Spectralradiometry provides an economical way of collecting ground truth data that are precisely comparable with Landsat data and which could provide a basis for hierarchic key classification rather than classification based on prinicipal components analysis. Spectral curves are presented for six common cover types and it is shown how the information could be used to develop classification criteria. Airborne data which could have provided a direct comparison with Landsat data proved to be too highly variable because of equipment constraints but there do not appear to be any significant barriers to developing the technique.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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