Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study

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Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass; information that is difficult to obtain using point samples.

Cite this paper

Hansen, C. , Williams, G. and Adjei, Z. (2015) Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study. Natural Resources, 6, 123-129. doi: 10.4236/nr.2015.62011.

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http://dx.doi.org/10.3354/meps272059                                                                               eww150225lx

Inexpensive Method to Assess Mangroves Forest through the Use of Open Source Software and Data Available Freely in Public Domain

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53723#.VNB0jizQrzE

ABSTRACT

Mapping and assessment of mangrove environment are crucial since the mangrove has an important role in the process of human-environment interaction. In Indonesia alone, 25% of South East Asia’s mangroves available are under threat. Recognizing the availability and the ability of new sensor of Landsat data, this study investigates the use of Landsat ETM + 7 and Landsat 8, acquired in 2002 and 2013 respectively, for assessing the extent of mangroves along the South Sulawesi’s coastline. For each year, a supervised classification of the mangrove was performed using open source GRASS GIS software. The resulting maps were then compared to quantify the change. Field work activities were conducted and confirmed with the changes that occurred in the study area.  Considering the accuracy assessment, our study shows that the RGB composite color-supervised classification is better than band ratio-supervised classification methods. By linking the open source software with the Landsat data and Google Earth satellite imagery that is available in public domain, mangroves forest conversion and changes can be observed remotely. Ground truth surveys confirmed that, decreases of mangroves forest is due to the expansion of fishpond activity. This technique could potentially allow rapid, inexpensive remote monitoring of cascading, indirect effects of human activities to mangroves forest.

Cite this paper

Ramdani, F. , Rahman, S. and Setiani, P. (2015) Inexpensive Method to Assess Mangroves Forest through the Use of Open Source Software and Data Available Freely in Public Domain. Journal of Geographic Information System, 7, 43-57. doi: 10.4236/jgis.2015.71004.

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Snow Cover Detection Based on Visible Red and Blue Channel from MODIS Imagery Data

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53566#.VMnUDizQrzE

ABSTRACT

In the present work, a new snow cover detection method based on visible red and blue bands from MODIS imagery data is proposed for Akita prefecture under the sunny cloud-free conditions. Before the snow cover detection, the MODIS imagery of the study area is pre-processed by geographic correction, clipping, atmospheric correction and topographic correction. Snow cover detection is carried out by applying the reflectance similarities of snow and other substances in the visible red band 1 and blue band 3. Then, the threshold values are confirmed to distinguish snow pixels from other substances by analyzing the composited true color images and 2-dimensional scatter plots. The MOD10_L2 products and in-situ snow depth data from 31 observation stations across the whole study area are chosen to compare and validate the effectivity of proposed method for snow cover detection. We calculate the overall accuracy, over-estimation error and under-estimation error of snow cover detection during the snowy season from May 2012 to April 2014, and the results are compared by classifying all of the observation stations into forest areas, basin areas and plain areas. It proves that the snow cover can be detected effectively in Akita prefecture by the proposed method. And the average overall accuracy of proposed method is higher than MOD10_L2 product, improved by 26.27%. The proposed method is expected to improve the environment management and agricultural development for local residents.

Cite this paper

Pan, P. , Chen, G. , Saruta, K. and Terata, Y. (2015) Snow Cover Detection Based on Visible Red and Blue Channel from MODIS Imagery Data. International Journal of Geosciences, 6, 51-66. doi: 10.4236/ijg.2015.61004.

References

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http://dx.doi.org/10.1002/047172372X                                                                       eww150129lx

A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi-Temporal Landsat Dataset

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53553#.VMiLzyzQrzE

ABSTRACT

Wetlands are those landscapes which are saturated with water or covered by water either perennially or for a major part of the year. Due to transforming nature of the wetlands from aquatic to terrestrial, the related physical features are not easy to be monitored. With the recent advancement in Remote sensing technique, the feature extraction of wetland with the help of different satellite derived band algebras including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI) is being used by experts. The common diagnostic features of wetlands are surface water, swamps and aquatic vegetation. The present study is based on a comparison between these four indices. Baraila wetland of Vaishali district Bihar is selected as a site for the study because the surface water of this “BAT” shaped wetland has decreased rapidly in last half decade which is alarming for the related ecology and biodiversity.

Cite this paper

Ashraf, M. and Nawaz, R. (2015) A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi-Temporal Landsat Dataset. Journal of Geographic Information System, 7, 1-19. doi: 10.4236/jgis.2015.71001.

References

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Active-Layer Soil Moisture Content Regional Variations in Alaska and Russia by Ground-Based and Satellite-Based Methods, 2002 through 2014

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53322#.VL3BWizQrzE

ABSTRACT

Soil moisture is a vital physical parameter of the active-layer in permafrost environments, and associated biological and geophysical processes operative at the microscopic to hemispheric spatial scales and at hourly to multi-decadal time scales. While in-situ measurements can give the highest quality of information on a site-specific basis, the vast permafrost terrains of North America and Eurasia require space-based techniques for assessments of cause and effect and long-term changes and impacts from the changes of permafrost and the active-layer. Satellite-based 6.925 and 10.65 GHz sensor algorithmic retrievals of soil moisture by Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard NASA-Aqua and follow-on AMSR2 onboard JAXA-Global Change Observation Mission—Water-1 are ongoing since July 2002. Accurate land-surface temperature and vegetation parameters are critical to the success of passive microwave algorithmic retrieval schemes. Strategically located soil moisture measurements are needed for spatial and temporal co-location evaluation and validation of the space-based algorithmic estimates. We compare on a daily basis ground-based (subsurface-probe) 50- and 70-MHz radio-frequency soil moisture measurements with NASA- and JAXA-algorithmic retrieval passive microwave retrievals. We find improvements in performance of the JAXA-algorithm (AMSR-E reprocessed and AMSR2 ongoing) relative to the earlier NASA-algorithm version. In the boreal forest regions, accurate land-surface temperatures and vegetation parameters are still needed for algorithmic retrieval success. Over the period of AMSR-E retrievals, we find evidence of at the high northern latitudes of growing terrestrial radio-frequency interference in the 10.65 GHz channel soil moisture content. This is an important error source for satellite-based active and passive microwave remote sensing soil moisture retrievals in Arctic regions that must be addressed.

ABSTRACT

Soil moisture is a vital physical parameter of the active-layer in permafrost environments, and associated biological and geophysical processes operative at the microscopic to hemispheric spatial scales and at hourly to multi-decadal time scales. While in-situ measurements can give the highest quality of information on a site-specific basis, the vast permafrost terrains of North America and Eurasia require space-based techniques for assessments of cause and effect and long-term changes and impacts from the changes of permafrost and the active-layer. Satellite-based 6.925 and 10.65 GHz sensor algorithmic retrievals of soil moisture by Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard NASA-Aqua and follow-on AMSR2 onboard JAXA-Global Change Observation Mission—Water-1 are ongoing since July 2002. Accurate land-surface temperature and vegetation parameters are critical to the success of passive microwave algorithmic retrieval schemes. Strategically located soil moisture measurements are needed for spatial and temporal co-location evaluation and validation of the space-based algorithmic estimates. We compare on a daily basis ground-based (subsurface-probe) 50- and 70-MHz radio-frequency soil moisture measurements with NASA- and JAXA-algorithmic retrieval passive microwave retrievals. We find improvements in performance of the JAXA-algorithm (AMSR-E reprocessed and AMSR2 ongoing) relative to the earlier NASA-algorithm version. In the boreal forest regions, accurate land-surface temperatures and vegetation parameters are still needed for algorithmic retrieval success. Over the period of AMSR-E retrievals, we find evidence of at the high northern latitudes of growing terrestrial radio-frequency interference in the 10.65 GHz channel soil moisture content. This is an important error source for satellite-based active and passive microwave remote sensing soil moisture retrievals in Arctic regions that must be addressed.

Cite this paper

Muskett, R. , Romanovsky, V. , Cable, W. and Kholodov, A. (2015) Active-Layer Soil Moisture Content Regional Variations in Alaska and Russia by Ground-Based and Satellite-Based Methods, 2002 through 2014. International Journal of Geosciences, 6, 12-41. doi: 10.4236/ijg.2015.61002.

References

[1] Hillel, D. (1980) Fundamentals of Soil Physics. Academic Press, New York.
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[4] Raich, J.W. and Schlesinger, W.H. (1992) The Global Carbon Dioxide Flux in Soil Respiration and Its Relationship to Vegetation and Climate. Tellus B, 44, 81-99.
http://dx.doi.org/10.1034/j.1600-0889.1992.t01-1-00001.x
[5] Schar, C., Luthi, D., Beyerle, U. and Heise, E. (1999) The Soil-Precipitation Feedback: A Process Study with a Regional Climate Model. Journal of Climate, 12, 722-741.
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[6] Jackson, R.D. (1973) Diurnal Soil-Water Content Changes during Drying, Field Solar Water Regime. Soil Science Society of America, 5, 37-56.
[7] Blanchard, B.J. and Chang, A.T.C. (1983) Estimation of Soil Moisture from SEASAT SAR Data. Journal of the American Water Resources Association, 19, 803-810.
http://dx.doi.org/10.1111/j.1752-1688.1983.tb02803.x                                                         eww150120lx

Location and Characterization of Breeding Sites of Solitary Desert Locust Using Satellite Images Landsat 7 ETM+ and Terra MODIS

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53299#.VLydg8nQrzE

ABSTRACT

In the southern Algerian Sahara, populations of the desert locust have been monitored during the past 43 years. On a limited study area, the use of remote sensing data from Landsat 7 ETM+ and Terra MODIS, coupled with the locust population database, allowed the identification and mapping of solitary desert locust breeding areas during remission periods. These sites are mainly located in wadis and in areas of accumulation/spreading of rainwater. The use of this methodology to all the Algerian Sahara is surely possible in order to improve the preventive management of this pest.

Cite this paper

Mohammed, L. , Diongue, A. , Yang, J. , Bahia, D. and Michel, L. (2015) Location and Characterization of Breeding Sites of Solitary Desert Locust Using Satellite Images Landsat 7 ETM+ and Terra MODIS. Advances in Entomology, 3, 6-15. doi: 10.4236/ae.2015.31002.

References

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Potential Hazard Map for Snow Disaster Prevention Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques: A Case Study in Northern Xinjiang, China

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52718#.VKH_3cCAM4

ABSTRACT

Snow disaster is one of the top ten natural disasters worldwide. Almost every year, there will be snow disasters in north Xinjiang, northwestern China. Since the accumulated heavy snow in winter season will seriously threaten people’s lives, the main object of this study is to produce a potential hazard map for snow avalanche prevention. Taking three snow seasons from November to March of year 2008 to 2010, potential hazard areas were estimated, based on snow volume products and terrain features. Remote sensing (RS) techniques and geographical information system (GIS) based weighted linear combination (WLC) approach were applied, taking into consideration multiple criteria. Snow avalanche risks were analyzed using physical exposure and vulnerability indexes. The analysis indicates that: the areas at high-risk of avalanches are located in the north and south part of the counties of Altay, Bortala and Ili prefectures; the areas at medium-risk of avalanches are found in the certain part of Altay prefecture and Urumqi, Changji, Tacheng prefectures; the avalanche risk is generally low throughout the large area to the certain part of the study area and the region on the border of the eastern north Xinjiang. Overall, the risks of snow avalanche in Altay and Ili prefectures are higher than that other regions; those areas should be allocated correspondingly more salvage materials.

Cite this paper

Abake, G. , Al-Hanbali, A. , Alsaaideh, B. and Tateishi, R. (2014) Potential Hazard Map for Snow Disaster Prevention Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques: A Case Study in Northern Xinjiang, China. Advances in Remote Sensing, 3, 260-271. doi: 10.4236/ars.2014.34018.

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http://dx.doi.org/10.1007/s11442-014-1097-z                                                                            eww141230lx