scholarly journals Soil-Landscape Modeling and Remote Sensing to Provide Spatial Representation of Soil Attributes for an Ethiopian Watershed

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Nurhussen Mehammednur Seid ◽  
Birru Yitaferu ◽  
Kibebew Kibret ◽  
Feras Ziadat
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Soil Properties ◽  
Vegetation Index ◽  
Soil Depth ◽  
Statistical Prediction ◽  
Field Observations ◽  
Linear Regression Models ◽  
Alternative Source ◽  
Soil Attributes

Information about the spatial distribution of soil properties is necessary for natural resources modeling; however, the cost of soil surveys limits the development of high-resolution soil maps. The objective of this study was to provide an approach for predicting soil attributes. Topographic attributes and the normalized difference vegetation index (NDVI) were used to provide information about the spatial distribution of soil properties using clustering and statistical techniques for the 56 km2Gumara-Maksegnit watershed in Ethiopia. Multiple linear regression models implemented within classified subwatersheds explained 6–85% of the variations in soil depth, texture, organic matter, bulk density, pH, total nitrogen, available phosphorous, and stone content. The prediction model was favorably comparable with the interpolation using the inverse distance weighted algorithm. The use of satellite images improved the prediction. The soil depth prediction accuracy dropped gradually from 98% when 180 field observations were used to 65% using only 25 field observations. Soil attributes were predicted with acceptable accuracy even with a low density of observations (1-2 observations/2 km2). This is because the model utilizes topographic and satellite data to support the statistical prediction of soil properties between two observations. Hence, the use of DEM and remote sensing with minimum field data provides an alternative source of spatially continuous soil attributes.

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2102
Author(s):  
Elmar Ritz ◽  
Jarle W. Bjerke ◽  
Hans Tømmervik
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Traits ◽  
Soil Depth ◽  
Plant Stress ◽  
Understory Vegetation ◽  
Stress Vulnerability ◽  
Multi Method Approach ◽  
Common Plant

In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = −0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = −0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well.

scholarly journals Separation of soil and canopy reflectance signatures of Mid German agricultural soils

2011 ◽  
Vol 51 (No, 7) ◽  
pp. 296-303 ◽  
Author(s):  
T. Behrens ◽  
K. Gregor ◽  
W. Diepenbrock
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Field Experiments ◽  
Agricultural Soils ◽  
Soil Reflectance

Remote sensing can provide visual indications of crop growth during production season. In past, spectral optical estimations were well performed in the ability to be correlated with crop and soil properties but were not consistent within the whole production season. To better quantify vegetation properties gathered via remote sensing, models of soil reflectance under changing moisture conditions are needed. Signatures of reflected radiation were acquired for several Mid German agricultural soils in laboratory and field experiments. Results were evaluated at near-infrared spectral region at the wavelength of 850 nm. The selected soils represented different soil colors and brightness values reflecting a broad range of soil properties. At the wavelength of 850 nm soil reflectance ranged between 10% (black peat) and 74% (white quartz sand). The reflectance of topsoils varied from 21% to 32%. An interrelation was found between soil brightness rating values and spectral optical reflectance values in form of a linear regression. Increases of soil water content from 0% to 25% decreased signatures of soil reflectance at 850 nm of two different soil types about 40%. The interrelation of soil reflectance and soil moisture revealed a non-linear exponential function. Using knowledge of the individual signature of soil reflectance as well as the soil water content at the measurement, soil reflectance could be predicted. As a result, a clear separation is established between soil reflectance and reflectance of the vegetation cover if the vegetation index is known.

scholarly journals Using remote sensing data in population density estimation

2021 ◽  
Author(s):  
E.G. Shvetsov ◽  
N.M. Tchebakova ◽  
E.I. Parfenova
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Population Density ◽  
Vegetation Index ◽  
Census Data ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Population Census ◽  
Krasnoyarsk Krai ◽  
Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

scholarly journals Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment – Results from UAS based high resolution remote sensing

2021 ◽  
Author(s):  
Marc Wehrhan ◽  
Daniel Puppe ◽  
Danuta Kaczorek ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Spatial Patterns ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Biomass ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Promising Tool ◽  
Technical Features

Abstract. Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most of these studies were performed at relatively small plots with an intended low heterogeneity in soils and plant canopy composition, and results were extrapolated to larger spatial units up to global scale implicitly assuming similar environmental conditions. However, the emergence of new technical features and increasing knowledge on details in Si cycling leads to a more complex picture at landscape or catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation, but its biomass distribution and related Si stocks. Maximum Likelihood (ML) classification was applied to multispectral imagery captured by an Unmanned Aerial System (UAS) aiming the identification of land cover classes (LCC). Subsequently, the Normalized Difference Vegetation Index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si accumulating plants (Calamagrostis epigejos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epigejos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Si m−2), i.e., comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.

scholarly journals Evaluation of Geostatistical Techniques for Mapping Spatial Distribution of Soil PH, Salinity and Plant Cover Affected by Environmental Factors in Southern Iran

2010 ◽  
Vol 2 (4) ◽  
pp. 92-103 ◽  
Author(s):  
Mohammad ZARE-MEHRJARDI ◽  
Ruhollah TAGHIZADEH-MEHRJARDI ◽  
Ali AKBARZADEH
Keyword(s):  
Spatial Distribution ◽  
Environmental Factors ◽  
Soil Properties ◽  
Soil Ph ◽  
Vegetation Cover ◽  
Soil Depth ◽  
Plant Cover ◽  
Canopy Cover ◽  
Plot Sampling ◽  
Significant Difference

The study presented in this paper attempts to evaluate some interpolation techniques for mapping spatial distribution of soil pH, salinity and plant cover in Hormozgan province, Iran. The relationships among environmental factors and distribution of vegetation types were also investigated. Plot sampling was applied in the study area. Landform parameters of each plot were recorded and canopy cover percentages of each species were measured while stoniness and browsing damage were estimated. Results indicated that there was a significant difference in vegetation cover for high and low slope steepness. Also, vegetation cover was greater than other cases in the mountains with calcareous lithology. In general, there were no significant relationships among vegetation cover and soil properties such as pH, EC, and texture. Other soil properties, such as soil depth and gravel percentage were significantly affected by vegetation cover. Moreover, the geostatistical results showed that kriging and cokriging methods were better than inverse distance weighting (IDW) method for prediction of the spatial distribution of soil properties. Also, the results indicated that all the concerned soil and plant parameters were better determined by means of a cokriging method. Land elevation, which was highly correlated with studied parameters, was used as an auxiliary parameter.

scholarly journals Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series

2021 ◽  
Vol 13 (11) ◽  
pp. 2223
Author(s):  
Mahboobeh Tayebi ◽  
Jorge Tadeu Fim Rosas ◽  
Wanderson de Sousa Mendes ◽  
Raul Roberto Poppiel ◽  
Yaser Ostovari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Properties ◽  
Soil Depth ◽  
Controlling Factors ◽  
Land Use History ◽  
The Impact ◽  
Soc Stocks

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.

scholarly journals Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Yi Liu ◽  
Samuel Ortega-Farías ◽  
Fei Tian ◽  
Sufen Wang ◽  
Sien Li
Keyword(s):  
Spatial Distribution ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Northwest China ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Temporal And Spatial

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

scholarly journals Estimating the Spatial Distribution of Soil Organic Carbon and Soil Total Nitrogen Stocks by Remote Sensing Methods: A Case Study in a Forest-dominated Alpine Watershed, Western China

2021 ◽  
Author(s):  
chao wang ◽  
Chuanyan Zhao ◽  
Kaiming Li ◽  
Shouzhang Peng ◽  
Ying Wang
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Vegetation Index ◽  
Multiple Linear Regression Model ◽  
Western China ◽  
Linear Regression Models ◽  
Soil Total Nitrogen

Abstract Soil organic carbon and soil total nitrogen stocks are important indicators for evaluating soil health and stability. Accurately predicting the spatial distribution of soil organic carbon and total nitrogen stocks is an important basis for mitigating global warming, ensuring regional food security, and maintaining the sustainable development of ecologically fragile areas. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0–30 cm) into three soil layers of 0–10 cm, 10–20 cm, and 20–30 cm to carry out soil organic carbon and soil total nitrogen stocks estimation experiments in the Qilian Mountains in western China. A multiple linear regression model and a stepwise multiple linear regression model were used to estimate soil organic carbon and soil total nitrogen stocks. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results indicated that these two linear regression models showed good performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen stocks. In topsoil, remote sensing technology could be used to predict the soil properties in layers; however, as the soil depth increased, the performance of the linear regression models gradually decreased.

scholarly journals REMOTE SENSING AND GIS TECHNIQUES FOR URBAN GROWTH MONITORING OF BASARAH CITY

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
Salah A. H. Saleh
Keyword(s):  
Economic Growth ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Urban Growth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing And Gis ◽  
Growth Patterns ◽  
Growth Monitoring

Basarah city has experienced a rapid urban expansion over the last decades dueto accelerated economic growth. This paper reports an investigation into the application ofthe integration of remote sensing and geographic information systems (GIS) for detectingurban built up growth for the period 1973 - 2002, and evaluate its impact on theenvironmental situation of Basarah city by analyzing the spatial distribution of urbanexpansion according to land cover types and normalized difference vegetation index(NDVI). The integration of remote sensing and GIS was found to be effective inmonitoring and analyzing urban growth patterns and in evaluating urbanization impact onsurface conditions of Baghdad area.

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