Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world.

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Special Issue on Remote Sensing of Snow and Its Applications

Geosciences ◽

10.3390/geosciences9060277 ◽

2019 ◽

Vol 9 (6) ◽

pp. 277 ◽

Cited By ~ 1

Author(s):

Ali Nadir Arslan ◽

Zuhal Akyürek

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Land Surface ◽

Climate Models ◽

Weather Forecasting ◽

Special Issue ◽

Carbon Cycles ◽

Wide Range ◽

Remote Sensing Techniques

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

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Classification of a burnt area based on spectral images

MATEC Web of Conferences ◽

10.1051/matecconf/201824700017 ◽

2018 ◽

Vol 247 ◽

pp. 00017

Author(s):

Anna Szajewska

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Burnt Area ◽

Forest Protection ◽

The Earth ◽

Remote Sensing Techniques ◽

Spectral Ranges ◽

Mathematical Operations

The use of remote sensing techniques allows obtaining information about processes that occur on the surface of the Earth. In the aspects of fire protection and forest protection, it is important to know a burnt area which was created as a result of a fire of the soil cover or a total fire. The knowledge of this area is necessary to assess losses. Remote sensing techniques allow obtaining images in various spectral ranges. Remote sensing satellites offer multi-band data. Mathematical operations that operate on values coming from different spectral ranges allow determining various remote sensing indicators. The manuscript presents the possibility of using the NDVI (Normalized Difference Vegetation Index) to classify the burnt area. The NDVI is relatively easy to obtain because it operates in the spectral ranges from 630 up to 915 nm, and is obtainable with one detector only. Thus, it can be obtained without any major problems using unmanned aerial vehicles, regardless of time and cloudiness, as is the case when acquiring satellite images. The manuscript describes experimental research and presents the results.

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ASSESSMENT OF COAL MARINE LOGISTICS IN AUSTRALIA USING THE EARTH REMOTE SENSING TECHNIQUES

Ugol ◽

10.18796/0041-5790-2016-10-85-87 ◽

2016 ◽

pp. 85-87

Author(s):

I.V. Zenkov ◽

◽

B.N. Nefedov ◽

V.I. Barkova ◽

Yu.P. Yuronen ◽

...

Keyword(s):

Remote Sensing ◽

Earth Remote Sensing ◽

The Earth ◽

Remote Sensing Techniques

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Geomorphological Mapping of Razzaza–Habbaria Area using Remote Sensing Techniques

Baghdad Science Journal ◽

10.21123/bsj.13.1.155-166 ◽

2018 ◽

Vol 13 (1) ◽

pp. 155-166

Author(s):

Baghdad Science Journal

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Image Data ◽

Unsupervised Classification ◽

Multispectral Data ◽

Geomorphological Mapping ◽

The Earth ◽

Sensing Application ◽

Remote Sensing Techniques ◽

Spatial Entities

Landforms on the earth surface are so expensive to map or monitor. Remote Sensing observations from space platforms provide a synoptic view of terrain on images. Satellite multispectral data have an advantage in that the image data in various bands can be subjected to digital enhancement techniques for highlighting contrasts in objects for improving image interpretability. Geomorphological mapping involves the partitioning of the terrain into conceptual spatial entities based upon criteria. This paper illustrates how geomorphometry and mapping approaches can be used to produce geomorphological information related to the land surface, landforms and geomorphic systems. Remote Sensing application at Razzaza–Habbaria area southwest of Razzaza Lake shows the different geomorphologic units and the land use maps that were delineated from Landsat ETM+ Image. Digital Image unsupervised classification was adopted to delineate the different classes by applying ERDAS 8.4 software. According to this classification five classes were selected and delineated in different colors.

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Quantifying the Impact of Light Pollution on Sea Turtle Nesting Using Ground-Based Imagery

Remote Sensing ◽

10.3390/rs12111785 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1785

Author(s):

James Vandersteen ◽

Salit Kark ◽

Karina Sorrell ◽

Noam Levin

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Cloud Cover ◽

Sea Turtle ◽

Spatial And Temporal Variation ◽

Light Pollution ◽

Night Time ◽

Wide Range ◽

Remote Sensing Techniques ◽

The Impact

Remote sensing of anthropogenic light has substantial potential to quantify light pollution levels and understand its impact on a wide range of taxa. Currently, the use of space-borne night-time sensors for measuring the actual light pollution that animals experience is limited. This is because most night-time satellite imagery and space-borne sensors measure the light that is emitted or reflected upwards, rather than horizontally, which is often the light that is primarily perceived by animals. Therefore, there is an important need for developing and testing ground-based remote sensing techniques and methods. In this study, we aimed to address this gap by examining the potential of ground photography to quantify the actual light pollution perceived by animals, using sea turtles as a case study. We conducted detailed ground measurements of night-time brightness around the coast of Heron Island, a coral cay in the southern Great Barrier Reef of Australia, and an important sea turtle rookery, using a calibrated DSLR Canon camera with an 8 mm fish-eye lens. The resulting hemispheric photographs were processed using the newly developed Sky Quality Camera (SQC) software to extract brightness metrics. Furthermore, we quantified the factors determining the spatial and temporal variation in night-time brightness as a function of environmental factors (e.g., moon light, cloud cover, and land cover) and anthropogenic features (e.g., artificial light sources and built-up areas). We found that over 80% of the variation in night-time brightness was explained by the percentage of the moon illuminated, moon altitude, as well as cloud cover. Anthropogenic and geographic factors (e.g., artificial lighting and the percentage of visible sky) were especially important in explaining the remaining variation in measured brightness under moonless conditions. Night-time brightness variables, land cover, and rock presence together explained over 60% of the variation in sea turtle nest locations along the coastline of Heron Island, with more nests found in areas of lower light pollution. The methods we developed enabled us to overcome the limitations of commonly used ground/space borne remote sensing techniques, which are not well suited for measuring the light pollution to which animals are exposed. The findings of this study demonstrate the applicability of ground-based remote sensing techniques in accurately and efficiently measuring night-time brightness to enhance our understanding of ecological light pollution.

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Editorial for Special Issue “Remote Sensing for Monitoring Wildlife and Habitat in a Changing World”

Remote Sensing ◽

10.3390/rs13142762 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2762

Author(s):

Andrés Viña

Keyword(s):

Remote Sensing ◽

Human Impacts ◽

Special Issue ◽

The Earth ◽

Changing World

Escalating human impacts on the Earth are creating unprecedented challenges, including the drastic degradation and loss of biodiversity worldwide [...]

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