General Discussion

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.

2019 ◽  
pp. 1098-1128
Author(s):  
Gennady Gienko ◽  
Michael Govorov

Researchers worldwide use remotely sensed imagery in their projects, in both the social and natural sciences. However, users often encounter difficulties working with satellite images and aerial photographs, as image interpretation requires specific experience and skills. The best way to acquire these skills is to go into the field, identify your location in an overhead image, observe the landscape, and find corresponding features in the overhead image. In many cases, personal observations could be substituted by using terrestrial photographs taken from the ground with conventional cameras. This chapter discusses the value of terrestrial photographs as a substitute for field observations, elaborates on issues of data collection, and presents results of experimental estimation of the effectiveness of the use of terrestrial ground truth photographs for interpretation of remotely sensed imagery. The chapter introduces the concept of GeoTruth – a web-based collaborative framework for collection, storing and distribution of ground truth terrestrial photographs and corresponding metadata.


Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 220
Author(s):  
Nils Nölke

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.


2022 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Zhihao Wei ◽  
Kebin Jia ◽  
Xiaowei Jia ◽  
Pengyu Liu ◽  
Ying Ma ◽  
...  

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.


Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 309 ◽  
Author(s):  
Isaac Kyere ◽  
Thomas Astor ◽  
Rüdiger Graß ◽  
Michael Wachendorf

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.


The paper reviews the application of multispectral satellite imagery to mineral and petroleum exploration, from the stage when satellite imagery first became available, with the launch of ERTS-1 (Landsat) just over 10 years ago, to the present day. The operation of Landsat is briefly described, and it is noted that the continuing success of this system for geological application has been in part due to the development of a world-wide network of receiving stations and the application of sophisticated data-processing techniques. Current research into the measurement of infrared spectra for the discrimination of rocks and minerals is discussed. Interpretation techniques are important but their success depends largely upon the experience of the interpreters as geologists. Examples of the use of Landsat imagery in exploration are given, and interpretations techniques are reviewed. Combining Landsat interpretation with that of regional geophysical surveys can bring important advantage. Finally the new generation of imaging multispectral satellites is described and the implications with regard to petroleum and mineral exploration are discussed.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esther Rolf ◽  
Jonathan Proctor ◽  
Tamma Carleton ◽  
Ian Bolliger ◽  
Vaishaal Shankar ◽  
...  

AbstractCombining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance.


Author(s):  
Gennady Gienko ◽  
Michael Govorov

Researchers worldwide use remotely sensed imagery in their projects, in both the social and natural sciences. However, users often encounter difficulties working with satellite images and aerial photographs, as image interpretation requires specific experience and skills. The best way to acquire these skills is to go into the field, identify your location in an overhead image, observe the landscape, and find corresponding features in the overhead image. In many cases, personal observations could be substituted by using terrestrial photographs taken from the ground with conventional cameras. This chapter discusses the value of terrestrial photographs as a substitute for field observations, elaborates on issues of data collection, and presents results of experimental estimation of the effectiveness of the use of terrestrial ground truth photographs for interpretation of remotely sensed imagery. The chapter introduces the concept of GeoTruth – a web-based collaborative framework for collection, storing and distribution of ground truth terrestrial photographs and corresponding metadata.


Author(s):  
M. A. Agbebia ◽  
N. Egesi

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.


2018 ◽  
Vol 10 (3) ◽  
pp. 398 ◽  
Author(s):  
Ana Militino ◽  
M. Ugarte ◽  
Unai Pérez-Goya

Sign in / Sign up

Export Citation Format

Share Document