scholarly journals Remote‐sensing Based Assessment of Long‐term Riparian Vegetation Health in Proximity to Agricultural Lands with Herbicide Use History

2019 ◽  
Vol 15 (4) ◽  
pp. 528-543 ◽  
Author(s):  
Foad Yousef ◽  
Mekonnen Gebremichael ◽  
Lula Ghebremichael ◽  
Jeffrey Perine
Keyword(s):  
Remote Sensing ◽  
Riparian Vegetation ◽  
Agricultural Lands ◽  
Vegetation Health ◽  
Herbicide Use

The problem of ballistic design of the space system Earth remote sensing in stable near-circular sun-synchronous orbits with long-term spacecraft lifetime

2018 ◽  
pp. 3-9
Author(s):  
V. M. Artyushenko ◽  
D. Y. Vinogradov
Keyword(s):  
Remote Sensing ◽  
Target Function ◽  
Normal Operation ◽  
Space Systems ◽  
Light Conditions ◽  
Space System ◽  
Earth Remote Sensing ◽  
Synchronous Orbit ◽  
Favorable Conditions

The article deals with the issues related to the problem of ballistic design of the space system of remote sensing of the Earth on stable near-circular solar-synchronous orbits with long-term existence of spacecraft. We propose a rational method of maintaining a solar-synchronous orbit in given light conditions with prolonged active lifetime of space systems. In solving this problem, the total time of normal operation of the system for a given period of operation, during which the most favorable conditions for the use of spacecraft are provided on the main parts of orbits, is taken as a target function.

Budapest airport air quality long-term studies by remote sensing with DOAS and FTIR with focus upon runway emissions

2007 ◽  
Author(s):  
Klaus Schäfer ◽  
Gregor Schürmann ◽  
Carsten Jahn ◽  
Candy Matuse ◽  
Herbert Hoffmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Long Term Studies

scholarly journals Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

2021 ◽  
Vol 13 (11) ◽  
pp. 2131
Author(s):  
Jamon Van Den Hoek ◽  
Alexander C. Smith ◽  
Kaspar Hurni ◽  
Sumeet Saksena ◽  
Jefferson Fox
Keyword(s):  
Remote Sensing ◽  
Geographic Distribution ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Forest Growth ◽  
Forest Cover Change ◽  
Scale Evaluation ◽  
Beneficial Effects ◽  
Affect Detection

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016) TIC. TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

scholarly journals Extensive Evaluation of a Continental-Scale High-Resolution Hydrological Model Using Remote Sensing and Ground-Based Observations

2021 ◽  
Vol 13 (7) ◽  
pp. 1247
Author(s):  
Bowen Zhu ◽  
Xianhong Xie ◽  
Chuiyu Lu ◽  
Tianjie Lei ◽  
Yibing Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hydrological Modeling ◽  
Infiltration Capacity ◽  
Modeling Framework ◽  
Large Domain ◽  
Continental Scale ◽  
Extensive Evaluation ◽  
Mean Differences ◽  
Fine Resolution

Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections.

scholarly journals A Century of French Railways: The Value of Remote Sensing and VGI in the Fusion of Historical Data

2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Robert Jeansoulin
Keyword(s):  
Remote Sensing ◽  
Meta Analysis ◽  
Computer Assisted ◽  
Railway Network ◽  
Reconstruction Process ◽  
Railway Stations ◽  
Eu Member States ◽  
Public Data ◽  
Public Datasets

Providing long-term data about the evolution of railway networks in Europe may help us understand how European Union (EU) member states behave in the long-term, and how they can comply with present EU recommendations. This paper proposes a methodology for collecting data about railway stations, at the maximal extent of the French railway network, a century ago.The expected outcome is a geocoded dataset of French railway stations (gares), which: (a) links gares to each other, (b) links gares with French communes, the basic administrative level for statistical information. Present stations are well documented in public data, but thousands of past stations are sparsely recorded, not geocoded, and often ignored, except in volunteer geographic information (VGI), either collaboratively through Wikipedia or individually. VGI is very valuable in keeping track of that heritage, and remote sensing, including aerial photography is often the last chance to obtain precise locations. The approach is a series of steps: (1) meta-analysis of the public datasets, (2) three-steps fusion: measure-decision-combination, between public datasets, (3) computer-assisted geocoding for ‘gares’ where fusion fails, (4) integration of additional gares gathered from VGI, (5) automated quality control, indicating where quality is questionable. These five families of methods, form a comprehensive computer-assisted reconstruction process (CARP), which constitutes the core of this paper. The outcome is a reliable dataset—in geojson format under open license—encompassing (by January 2021) more than 10,700 items linked to about 7500 of the 35,500 communes of France: that is 60% more than recorded before. This work demonstrates: (a) it is possible to reconstruct transport data from the past, at a national scale; (b) the value of remote sensing and of VGI is considerable in completing public sources from an historical perspective; (c) data quality can be monitored all along the process and (d) the geocoded outcome is ready for a large variety of further studies with statistical data (demography, density, space coverage, CO2 simulation, environmental policies, etc.).

scholarly journals The Mountain Pine Beetle Epidemic in the Black Hills, South Dakota: The Consequences of Long Term Fire Policy, Climate Change and the Use of Remote Sensing to Enhance Mitigation

2018 ◽  
Vol 10 (1) ◽  
pp. 69 ◽  
Author(s):  
Kyle Mullen ◽  
Fei Yuan ◽  
Martin Mitchell
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ponderosa Pine ◽  
Mountain Pine Beetle ◽  
Black Hills ◽  
Fire Policy ◽  
Mountain Pine ◽  
Pine Beetle ◽  
Ponderosa Pine Forest

The recent and intense outbreak (first decade of 2000s) of the mountain pine beetle in the Black Hills of South Dakota and Wyoming, which impacted over 33% of the 1.2 million acre (486,000 ha) Black Hills National Forest, illustrates what can occur when forest management practices intersect with natural climatic oscillations and climate change to create the “perfect storm” in a region where the physical environment sets the stage for a plethora of economic activities ranging from extractive industries to tourism. This study evaluates the potential of WorldView-2 satellite imagery for green-attacked tree detection in the ponderosa pine forest of the Black Hills, USA. It also discusses the consequences of long term fire policy and climate change, and the use of remote sensing technology to enhance mitigation. It was found that the near-infrared one (band 7) of WorldView-2 imagery had the highest influence on the green-attack classification. The Random Forest classification produced the best results when transferred to the independent dataset, whereas the Logistic Regression models consistently yielded the highest accuracies when cross-validated with the training data. Lessons learned include: (1) utilizing recent advances in remote sensing technologies, most notably the use of WorldView-2 data, to assist in more effectively implementing mitigation measures during an epidemic, and (2) implementing pre-emptive thinning strategies; both of which can be applied elsewhere in the American West to more effectively blunt or preclude the consequences of a mountain pine beetle outbreak on an existing ponderosa pine forest. 

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