Investor Origin and Deforestation: Evidence from Global Mining Sites

Mining is of major economic, environmental and societal consequence, yet knowledge and understanding of its global footprint is still limited. Here, we produce a global mining land use dataset via remote sensing analysis of high-resolution, publicly available satellite imagery. The dataset comprises 74,548 polygons, covering ~66,000 km2 of features like waste rock dumps, pits, water ponds, tailings dams, heap leach pads and processing/milling infrastructure. Our polygons finely contour the edges of mine features and do not include the space between them. This distinguishes our dataset from others that employ broader definitions of mining lands. Hence, despite our database being the largest to date by number of polygons, comparisons show relatively lower global land use. Our database is made freely available to support future studies of global mining impacts. A series of spatial analyses are also presented that highlight global mine distribution patterns and broader environmental risks.

HighlightsVegetation indices (NDVI, GNDVI, and SAVI) extracted from high-resolution satellite imagery were significantly associated with vegetation indices extracted from UAV imagery.High-resolution satellite data can be used to predict maize yield at breeding plot scale.Breeding plot sizes and the variability between maize genotypes may be associated with prediction accuracies.Abstract. The recent availability of high spatial and temporal resolution satellite imagery has widened its applications in agriculture. Plant breeding and genetics programs are currently adopting unmanned aerial vehicle (UAV) based imagery data as a complement to ground data collection. With breeding trials across multiple geographic locations, UAV imaging is not always convenient. Hence, we anticipate that, similar to UAV imaging, phenotyping of individual test plots from high-resolution satellite imagery may also provide value to plant genetics and breeding programs. In this study, high spatial resolution satellite imagery (~38 to 48 cm pixel-1) was compared to imagery acquired using a UAV for its ability to phenotype maize grown in two-row and six-row breeding plots. Statistics (mean, median, sum) of color (red, green, blue), near-infrared, and vegetation indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and soil adjusted vegetation index (SAVI) were extracted from imagery from both sources (UAV and satellite) for comparison at three time points. In general, a strong correlation between satellite and UAV imagery extracted NDVI, GNDVI, and SAVI features (especially with mean and median statistics, p < 0.001) was observed at different time points. The correlation of both UAV and satellite image features with yield potential was maximum (p < 0.001) at the third time point (milk/dough growth stages). For example, Pearson’s correlation coefficients between mean NDVI, GNDVI, and SAVI features with yield potential were 0.52, 0.54, and 0.51 for data derived from UAV imagery, and 0.34, 0.41, and 0.40 for data derived from satellite imagery, respectively. Machine learning algorithms, including least absolute shrinkage and selection operator (Lasso) regression, were evaluated for yield prediction using vegetation index features that were significantly correlated with observed yield. The relationship between satellite imagery with crop performance can be a function of plot size in addition to crop variability. Nevertheless, with the ongoing improvement of satellite technologies, there is a possibility for the integration of satellite data into breeding programs, thus improving phenotyping efficiencies. Keywords: Image processing, Machine learning, Plant breeding, Statistical analysis, Unmanned aerial vehicles.

In this research was developed the methodology useful for the future Caspian Sea coastal management what is nowadays inevitable for Azerbaijan to support decision makers in their everyday activities related with the sustainable development of coastal zones. As the material for the research were selected the reliable data sources as high-resolution satellite imagery which can be compared with the aerial photography which was always suitable for the purposes of the coastal management. The high-resolution satellite imagery satisfied the needs of coastal environmental management giving almost the same results as aerial photography. The satellite images were obtained with the coverage of pilot territories of Gizil-Agach strict nature reserve and sanctuary located along Caspian Sea coastal line. The main objective of this research was to present the up-to-date developed method of how to predict the Caspian Sea level fluctuation impacts on the coastal areas by monitoring visually results of geographical information system processing analysis based on the accurate spatial data acquired from high-resolution stereo satellite imagery. Besides nowadays acquisition of satellite imagery is easily accessible periodically what gives possibility for the permanent tracking of coastal line change dynamics. Using the advantages of high-resolution satellite imagery it is possible to extract majority of all relief topographical details along the coastal areas and over whole pilot territory using stereo plotting techniques what is very important for the further GIS modeling of Caspian Sea possible level fluctuation impacts to coastal areas. In comparison with the already implemented works in this field, the main advantage of this research is the creation of the high- accurate digital terrain model what gives us possibility for the interpolation through the defined interval and based on this principle modeling of the potential water flood and drop areas on the different levels of Caspian Sea. The results of this research presented that the processing of high-resolution stereo satellite imagery and preparation of precise stereo model, digital terrain models and digital orthophoto maps and further GIS processing of these accurate material sources are absolutely suitable methodology for the environmental modeling of potential Caspian Sea water level fluctuations. It means that it is possible to transform this photogrammetric methodology and GIS techniques into the integrated technological form that can be used by the decision makers and all other parties involved in the activities related with the Caspian Sea coastal management.

David Mercier Abstract High royalty rates, along with low oil price, can make an operation uneconomic creating a lose-lose situation for the royalty owner and producer. For the operator, a high royalty rate has the same effect as higher operating costs. Both the royalty owner and operator have a tremendous incentive to be innovative when negotiating profit distribution splits. Unfortunately neither the royalty owner nor the operator can consistently predict changes in oil price, all we can do is seek the flexibility to survive oil price fluctuations. However, a royalty schedule that tracks oil price, when properly engineered, decreases the likelihood of negative cash flow during a low oil price swing. Introduction Traditionally, many royalty owners have tried to get the highest fixed royalty rate possible thinking a higher rate naturally translates into a higher royalty payment. This approach has resulted in royalties that need to be renegotiated when the oil price goes lower to prevent premature abandonment. Historical crude oil price forecast have been extremely unreliable - Ref 1. Negotiating the highest possible royalty rate the producer will accept should never be the royalty owner's strategy. In any profit sharing agreement the (royalty owner / operator) economic interdependence is large; i.e., a win-lose relationship ultimately ends up lose-lose. Royalty owners' focus should be on maximizing royalty revenue. High royalty rates do not necessarily equate to maximized royalty revenue. Net Profits sharing contracts are progressive systems employed in many places around the world (Ref 2), however, typically, net profit contracts require the royalty (mineral) owner to be constantly evaluating the details of the operation. This continuous auditing, of the operation, is costly for both the mineral owner and operator. One of the worst experiences a working interest owner can have is to see the operator's president driving an expensive car while wondering if the car was paid for out of his net profit's share; that is, because of an accounting error. It has been my experience, when net profits decrease or go negative the (working interest owner / field operator) relationship tends to deteriorate. Not many operators want to have the mineral owner constantly going over their accounting books. Also, net profit contracts, as compared with royalty contracts, expose the mineral owner to increased financial liability - the risk of negative cash flow. Increased financial risk is not bad when it comes with a proportionate amount of increased benefit. Since overseeing a royalty rate schedule requires less administrative overhead than a net profits split, switching from a net profits split to a royalty schedule can potentially increase field value. This paper will present different techniques used to obtain maximum field value while reducing risk. A price sensitive sliding scale lessens risk by reducing operator cash flow variation while lowering the likelihood of field abandonment. Experienced oil investors know that they cannot control oil price swings; however, with this royalty rate that slides with oil price they can control the risk that, in turn, drives the returns. It is common in the United States for owners of oil properties to charge those who recover and sell these resources a royalty expressed as a fraction of the property's gross production. Royalty agreements most commonly observed specify a constant royalty. Two criteria that need to be satisfied when determining a royalty are:equity (Is the royalty rate fair?)Efficiency (Does the royalty rate interfere unduly with the economics of the oil field?). With a fixed royalty rate, the royalty owners total revenue equals the product of royalty rate, oil price, and the total production. Higher royalty rates can lower production, often causing properties to be abandon prematurely. To the producer, increased royalty rates are similar to increased operating costs. P. 159^

Mapping and characterizing Arctic beaded streams through high resolution satellite imagery

This damage assessment relied heavily on the use of remote sensing technology. Never before has the availability of high-resolution satellite and aerial imagery been so open and accessible. Data from different missions (World Bank-ImageCat-RIT Remote Sensing Mission (15cm optical and 2 pt/m2 LiDAR), Google (15cm optical), NOAA (25cm optical), Pictometry, as well as satellite imagery from GeoEye and Digitalglobe) has allowed damage from the Haiti earthquake to be viewed through multiple sensors and at different times. These multi-dimensional perspectives have been invaluable in understanding the magnitude and scope of damage caused by this earthquake. BACKGROUND This paper provides a brief account of how technology, inspiration and collaboration were used to quickly assess the amount of damage caused by the January 12, 2010 Haiti earthquake. In less than a minute, this event leveled approximately 20 percent of the buildings in greater Port-au-Prince; killed close to a quarter of a million people; injured as many; and left over a million individuals homeless. While not considered a great earthquake (from seismological standards), this event will rank as one of the deadliest earthquakes of the 21 st century. This event will also be known as one of the first events where technology (especially high-resolution imagery) was embraced at such a large scale in a real operational sense. Almost from the very onset of the disaster, high-resolution satellite imagery was available to provide the first glimpse of the devastation caused by this earthquake. Days later, very-high resolution aerial imagery was available to provide even more detail on the damage caused in this event. Together, these valuable datasets allowed a small army of remote sensing experts to provide one of the most accurate assessments of building damage in the last decade. Furthermore, this information was shared with Haitian government officials in relatively short time – within two months of the earthquake – in the

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

The present study, for the first time, examined land-use land cover (LULC), changes using GIS, between 2000 and 2018 for the IIT Bombay campus, India. Objective was to evaluate hydro-ecological balance inside campus by determining spatio-temporal disparity between hydrological parameters (rainfall-runoff processes), ecological components (forest, vegetation, lake, barren land), and anthropogenic stressors (urbanization and encroachments). High-resolution satellite imageries were generated for the campus using Google Earth Pro, by manual supervised classification method. Rainfall patterns were studied using secondary data sources, and surface runoff was estimated using SCS-CN method. Additionally, reconnaissance surveys, ground-truthing, and qualitative investigations were conducted to validate LULC changes and hydro-ecological stability. LULC of 2018 showed forest, having an area cover of 52%, as the most dominating land use followed by built-up (43%). Results indicated that the area under built-up increased by 40% and playground by 7%. Despite rapid construction activities, forest cover and Powai lake remained unaffected. This anomaly was attributed to the drastically declining barren land area (up to ~ 98%) encompassing additional construction activities. Sustainability of the campus was demonstrated with appropriate measures undertaken to mitigate negative consequences of unwarranted floods owing to the rise of 6% in the forest cover and a decline of 21% in water hyacinth cover over Powai lake. Due to this, surface runoff (~ 61% of the rainfall) was observed approximately consistent and being managed appropriately despite major alterations in the LULC. Study concluded that systematic campus design with effective implementation of green initiatives can maintain a hydro-ecological balance without distressing the environmental services.

Chinese companies are far from taking control over African or global mining. In 2018, they control less than 7% of the value of total African mine production. Chinese investments in African mining of non-fuel minerals between 1995 and 2018 have contributed to production growth but it has also increased Chinese control over African mineral and metal production. There is evidence pointing to a continued Chinese expansion in African minerals and metals but at a slower pace than in the past decade. Through a detailed analysis of every mine, fully or partially controlled by Chinese interest in Africa and all other parts of the world the paper also measures total Chinese control over global mine production to be around 3% of the total value.

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.

Agricultural field boundary information is important and often required for the geosciences and the agricultural sector. In this paper, a novel method is developed to extract sub-boundaries within the permanent boundaries of agricultural land parcels from high-resolution optical satellite imagery using an improved cluster-based snake model. The method takes the advantage of the results of an automatic fuzzy c-means (FCM) clustering and edge detection to compute external forces for an improved gradient vector flow (GVF) snake model. The GVF snake algorithm is improved by using an automatic seeding model based on clustering results and image moment functions. To seed the improved GVF algorithm, an ellipse is automatically delineated for each cluster within agricultural parcel by utilizing image moment functions (in particular silhouette moments). The GVF snake model is then implemented for each seed, one seed at a time. Active contours tend to have curve shapes rather than straight lines due to their structure that consists of several connected nodes within each contour. Therefore, the final accurate results are obtained after performing a three-stage line simplification operation. The experiments of the method were conducted on 20 test fields in a study area located near to the town of Karacabey, Turkey, using the 4-m resolution IKONOS multispectral (xs) image, the 2.44-m resolution QuickBird xs image, and the 0.61-m resolution QuickBird pan-sharpened (PS) image. Experimental results demonstrate that using both the clustering and edge detection results as external forces for the improved GVF snake model increases the accuracy of the results. In addition, the developed method showed a fairly good performance in extracting sub-boundaries for the fields comprising crops with an inherent high inner heterogeneity, such as rice and corn. The method can potentially be applied in the extraction of within-field sub-boundaries from high-resolution satellite imagery in agricultural areas.

This study investigates market capitalization elasticity with respect to changes in mineral price by analyzing the relationship between the Korea Composite Stock Price Index market capitalization and the international prices of six major strategic minerals. Weekly data from the 1st week of 2009 to the 52nd week of 2022 were analyzed using the autoregressive distributed lag model. The model comprises market capitalization, mineral prices, exchange rate, money supply, and leading economic index variables with time lags. The results revealed that the changes in the prices of all six strategic minerals except bituminous coal had a positive effect on the market capitalization, while bituminous coal had no effect. The findings of this study support recent developments in the importance of strategic minerals in ensuring resource and economic security.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr−1). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices.

High-resolution satellite imagery is considered an excellent candidate for extracting information about the human activities on Earth. The information about residential development and suburban area mapping is of interest that can be obtained from these images. Shadow of structures such as man-made buildings is one of the main cues for structure detection in panchromatic high-resolution satellite imagery. However, to correctly exploit the information of the shadow in an image, the shadow needs to be detected and isolated first. In this paper, we propose a new algorithm for shadow detection and isolation of buildings in high-resolution panchromatic satellite imagery. The proposed algorithm is based on tailoring the traditional model of the geometric active contours such that the new model of the contours is systematically biased toward segmenting the shadow and the dark regions in the image. The systematic biasing in the proposed contour model is accomplished by novel encoding of the radiometric characteristics of the shadows regions. After detecting and segmenting the shadow and the dark regions in the image, further processing steps are introduced. The proposed postprocessing is based on selection of optimal threshold and a boundary complexity metric to distinguish the true shadows from the clutter. Experimental results are presented to validate the performance of the proposed algorithm on real high-resolution panchromatic satellite images.