scholarly journals CHARACTERIZING SPATIAL AND TEMPORAL TRENDS OF SOIL AND SURFACE PROPERTIES CHANGES IN AN AREA WITH URBAN, BARE SOIL AND WETLAND COVERS: A 30-YEAR CASE STUDY IN GOMISHAN, IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 51-56
Author(s):  
S. K. Alavipanah ◽  
M. Konyushkova ◽  
S. Hamzeh ◽  
A. A. Kakroodi ◽  
A. Heidari ◽  
...  
Keyword(s):  
Surface Properties ◽  
Vegetation Cover ◽  
Land Surface ◽  
Single Channel ◽  
Temporal Trends ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Temporal Variations ◽  
Landsat Images ◽  
Spatial And Temporal Trends

Abstract. Climate is one most important factors that can reconstructs the formation of soils. Accordingly, the objective of this study is characterizing spatial and temporal trends of soil and surface properties changes in Gomishan region during the period of 2017–1987. For this purpose, 432 monthly product of LST (MOD11C3) and vegetation cover (MOD13C2) of MODIS sensor and 3 Landsat images were used. Single-channel algorithm and various spectral indexes were used to modeling of Land surface temperature (LST) and surface properties including brightness, greenness, wetness and salinity. Then, based on the soil line analyse, pixels with the full cover of soil were extracted. Finally, trend of LST and surface properties variations were investigated for these pixels and whole studied area. The average of LST and vegetation cover changes in January, February, March and April are higher than other months. The variance of LST and surface properties for Gomishan wetland was higher than other regions of the studied area. The values of Soil salinity index in 2000 year was higher than 1987 and 2017 years. The LST of pixels with full cover of soil in the north of study area was higher than the south. Also, wetness of these pixels in the northern regions is lower than the southern regions of the study area. The results of study indicate, spatial and temporal variations of the surface properties of the Gomishan area derived from remote-sensing data were high.

scholarly journals Temporal and spatial variations in freeze warnings in the conterminous United States: 2005–2018

2019 ◽  
Vol 139 (3-4) ◽  
pp. 1379-1384
Author(s):  
Brandon Lawhorn ◽  
Robert C. Balling
Keyword(s):  
United States ◽  
Temporal Trends ◽  
The United States ◽  
Temporal Variations ◽  
Atmospheric Composition ◽  
Southeastern Us ◽  
The Us ◽  
Temporal And Spatial ◽  
Frost Free Period ◽  
Spatial And Temporal Trends

AbstractIt is well-documented that the United States (US), along with other mid-latitude land locations, has experienced warming in recent decades in response to changes in atmospheric composition. Among other changes, Easterling (2002) reported that the frost-free period is now longer across much of the US with the first frost in fall occurring later and the last freeze in spring occurring earlier. In this investigation, we explore spatial and temporal variations in all freeze warnings issued by the US National Weather Service. Freeze warning counts are highest in the southeastern US peaking overall in the spring and fall months. Freeze warnings tend to occur more toward summer moving northward and westward into more northerly states. Consistent with the warming in recent decades, we find statistically significant northward movements in freeze warning centroids in some months (December, February) across the study period (2005–2018). Detection of spatial and temporal trends in freeze warnings may be of interest to any number of scientists with applied climatological interests.

scholarly journals Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

2020 ◽  
Vol 12 (9) ◽  
pp. 1466 ◽  
Author(s):  
Hitesh Supe ◽  
Ram Avtar ◽  
Deepak Singh ◽  
Ankita Gupta ◽  
Ali P. Yunus ◽  
...  
Keyword(s):  
Land Surface ◽  
Power Plants ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Google Earth ◽  
Landsat 8 ◽  
Arid Environments ◽  
Solar Panels ◽  
Google Earth Engine ◽  
Sand Deposition

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

scholarly journals Modeling regional evaporation through ANFIS incorporated solely with remote sensing data

2013 ◽  
Vol 10 (5) ◽  
pp. 6153-6192
Author(s):  
F.-J. Chang ◽  
W. Sun
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Data Allocation ◽  
Remotely Sensed Data ◽  
Estimation Model ◽  
Landsat Images ◽  
Inference System ◽  
Sensing Data

Abstract. The study aims to model regional evaporation that possesses the ability to present the spatial distribution of evaporation across the whole Taiwan by the adaptive network-based fuzzy inference system (ANFIS) based solely on remote sensing data. The remote sensing data used in this study consist of Landsat image products including Enhanced Vegetation Index (EVI) and land surface temperature (LST). The model construction is designed through two types of data allocation (temporal and spatial) driven with the same ten-year data of EVI and LST derived from Landsat images. Evidences indicate the estimation model based solely on remotely sensed data can effectively detect the spatial variation of evaporation and appropriately capture the evaporation trend with acceptable errors of about 1 mm day−1. The results also demonstrate the composite of EVI and LST input to the proposed estimation model improves the accuracy of estimated evaporation values as compared with the model using LST as the only input, which reveals EVI indeed benefits the estimation process. The results suggest Model-T (temporal input allocation) is suitable for making island-wide evaporation estimation while Model-S (spatial input allocation) is suitable for making evaporation estimation at ungauged sites. An island-wide evaporation map for the whole study area (Taiwan Island) is then derived. It concludes the proposed ANFIS model incorporated solely with remote sensing data can reasonably well generate evaporation estimation and is reliable as well as easily applicable for operational estimation of evaporation over large areas where the network of ground-based meteorological gauging stations is not dense enough or readily available.

Studies on soils condition on the area of the closed Babina mine in 2009-2019 using multispectral satellite images

2020 ◽  
Author(s):  
Anna Buczyńska
Keyword(s):  
Temporal Trends ◽  
Remote Sensing Data ◽  
Soil Condition ◽  
Open Pit ◽  
Multispectral Images ◽  
Negative Effects ◽  
Industrial History ◽  
Multispectral Satellite Images ◽  
Spatial And Temporal Trends ◽  
Sentinel 2

<p>Mining activity on the area of ​​the former open-pit and underground brown coal mine called Friendship of Nations - Shaft Babina, which is at this moment part of the UNESCO Geopark - the Muskau Arch, was finished in 1973 and reclamation works were started with a special dedication to the forestation. As a part of the reclamation works, a number of technical and biological operations were performed, the subjects were: adjustment of water conditions, relevant land forming, development of ​​the former mine area by plantings and improvement of soil condition. The last of mentioned factor is extremely significant element whose condition determines the proper growth of vegetation. Considering the mining-industrial history and current development of this area, it seems necessary to constantly monitor the components of the natural environment, in particular soils. Adequate and timely used remedies can limit the negative effects and degradation of flora. The purpose of this research was an analysis of the soils condition in 2009-2019 on the area of Babina mine on the basis of geological indices determined using multispectral images of Sentinel-2 and Landsat 5/8 satellite missions. The subjects of analysis were the following soil properties: humidity, overall condition, salinity, texture and chemical composition. It should be emphasized that the research was the first on this area in which remote sensing data was used. Obtained results allowed determining of the current condition of soils, describing their changes in the last 10 years and indicating spatial and temporal trends of changes in the future. In addition, the results of the analysis made it possible to identify areas that may still be under the influence of former mining activities that adversely affect the condition of soils.</p>

scholarly journals A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5659
Author(s):  
Yue Jiang ◽  
WenPeng Lin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Thermal Infrared Remote Sensing ◽  
Retrieval Algorithms ◽  
Split Window Algorithm

In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.

scholarly journals Modelling and Delineation of Hydrocarbon Micro - Seepage Prone Zones on Soil and Sediment in Ugwueme, South Eastern Nigeria with Soil Adjustment Vegetation Index (SAVI)

2020 ◽  
pp. 13-33
Author(s):  
Mfoniso Asuquo Enoh ◽  
Uzoma Chinenye Okeke ◽  
Needam Yiinu Barinua
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat Images ◽  
Landsat 7 ◽  
Soil And Sediment

Remote Sensing is an excellent tool in monitoring, mapping and interpreting areas, associated with hydrocarbon micro-seepage. An important technique in remote sensing known as the Soil Adjusted Vegetation Index (SAVI), adopted in many studies is often used to minimize the effect of brightness reflectance in the Normalized Difference Vegetation Index (NDVI), related with soil in areas of spare vegetation cover, and mostly in areas of arid and semi–arid regions. The study aim at analyzing the effect of hydrocarbon micro – seepage on soil and sediments in Ugwueme, Southern Eastern Nigeria, with SAVI image classification method. To achieve this aim, three cloud free Landsat images, of Landsat 7 TM 1996 and ETM+ 2006 and Landsat 8 OLI 2016 were utilized to produce different SAVI image classification maps for the study.  The SAVI image classification analysis for the study showed three classes viz Low class cover, Moderate class cover and high class cover.  The category of high SAVI density classification was observed to increase progressive from 31.95% in 1996 to 34.92% in 2006 and then to 36.77% in 2016. Moderately SAVI density classification reduced from 40.53% in 1996 to 38.77% in 2006 and then to 36.96% in 2016 while Low SAVI density classification decrease progressive from 27.51% in 1996 to 26.31% in 2006 and then increased to 28.26% in 2016. The SAVI model is categorized into three classes viz increase, decrease and unchanged. The un – changed category increased from 12.32km2 (15.06%) in 1996 to 17.17 km2 (20.96%) in 2006 and then decelerate to 13.50 km2 (16.51%) in 2016.  The decrease category changed from 39.89km2 (48.78%) in 1996 to 40.45 km2 (49.45%) in 2006 and to 51.52 km2 (63.0%) in 2016 while the increase category changed from 29.57km2 (36.16%) in 1996 to 24.18 km2 (29.58%) in 2006 and to 16.75 km2 (20.49%) in 2016. Image differencing, cross tabulation and overlay operations were some of the techniques performed in the study, to ascertain the effect of hydrocarbon micro - seepage.  The Markov chain analysis was adopted to model and predict the effect of the hydrocarbon micro - seepage for the study for 2030.  The study expound that the SAVI is an effective technique in remote sensing to identify, map and model the effect of hydrocarbon micro - seepage on soil and sediment particularly in areas characterized with low vegetation cover and bare soil cover.

scholarly journals Exploring the Relationship between LST, LULC and NDVI in Saïss Plain using Geospatial Techniques

2021 ◽  
Vol 314 ◽  
pp. 04001
Author(s):  
Manal El Garouani ◽  
Mhamed Amyay ◽  
Abderrahim Lahrach ◽  
Hassane Jarar Oulidi
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Sensing Data

Land use/land cover (LULC) change has been confirmed that have a significant impact on climate through various pathways that modulate land surface temperature (LST) and precipitation. However, there are no studies illustrated this link in the Saïss plain using remote sensing data. Thus, the aim of this study is to monitor the LST relationship between LULC and vegetation index change in the Saïss plain using GIS and Remote Sensing Data. We used 18 Landsat images to study the annual and interannual variation of LST with LULC (1988, 1999, 2009 and 2019). To highlight the effect of biomass on LST distribution, the Normalized Difference Vegetation Index (NDVI) was calculated, which is a very good indicator of biomass. The mapping results showed an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. Based on statistical analyzes, the LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of LULC type. The variation of land surface temperature with NDVI shows a negative correlation. This explains the increase in the surface temperature in rangelands and arable land while it decreases in irrigated crops and arboriculture.

Conservation, Ecology, and Management of Catfish: The Second International Symposium

2011 ◽  
Keyword(s):  
Ictalurus Punctatus ◽  
Mississippi River ◽  
Market Competition ◽  
Temporal Trends ◽  
Temporal Variations ◽  
Commercial Fishing ◽  
Substantial Portion ◽  
Black Bullhead ◽  
Commercial Harvest ◽  
Spatial And Temporal Trends

<em>Abstract</em>.—Ictalurids compose a substantial portion of the commercial harvest in the upper Mississippi River (UMR). The purpose of this investigation was to examine spatial and temporal trends in commercial harvest of ictalurids in the UMR. The study focused on four species: channel catfish <em>Ictalurus punctatus</em>, flathead catfish<em> Pylodictis olivaris</em>, blue catfish <em>I. furcatus</em>, and black bullhead <em>Ameiurus melas</em>. We described trends in yield and market value and evaluated the influence of numerous factors on commercial catfish harvest in Pools 3–26 of the UMR between 1953 and 2001. Spatial and temporal variations in commercial harvest of catfish appeared to be driven by different factors through time. Early factors included habitat loss and overexploitation, and later factors included loss of the market share and increased market competition with aquaculture. Ictalurids have maintained a consistent proportion of the total commercial harvest in the UMR, and decreases in catfish harvest may indicate larger declines in commercial fishing.

Sign in / Sign up

Export Citation Format

Share Document