scholarly journals Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology

2018 ◽  
Vol 10 (10) ◽  
pp. 1571 ◽  
Author(s):  
Bruna Gallo ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Satellite Images ◽  
Prediction Models ◽  
Bare Soil ◽  
Soil Mapping ◽  
Least Squares Regression ◽  
Composite Image ◽  
Soil Attributes ◽  
Multi Temporal

The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0–20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R2 = 0.75, root mean square error (RMSE) = 89.84 g kg−1, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping.

scholarly journals Estimation of land-use changes on Kitulo Plateau, Tanzania, using satellite imagery

Oryx ◽  
1994 ◽  
Vol 28 (3) ◽  
pp. 173-182
Author(s):  
Jon C. Lovett ◽  
Erik Prins
Keyword(s):  
Land Use ◽  
Satellite Imagery ◽  
Vascular Plants ◽  
Satellite Images ◽  
Nature Reserve ◽  
Land Use Changes ◽  
Bare Soil ◽  
Digital Analysis ◽  
Threatened Plants ◽  
Strong Case

The Kitulo Plateau of southern Tanzania is a lava plateau covering 273 sq km at an altitude of over 2500 m. The vegetation is predominately grassland with more than 350 taxa of vascular plants, of which nearly 5 per cent are of restricted distribution. Although the plateau is extensive, much of it is now cultivated. Digital analysis of satellite images showed that at least 24 per cent of the plateau was bare soil, modified grassland or cultivation between 1973 and 1989. The botanical importance of the plateau and increase in cultivation make a strong case for the establishment of a nature reserve to protect its rare and threatened plants.

scholarly journals Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe

2020 ◽  
Vol 12 (9) ◽  
pp. 1369 ◽  
Author(s):  
José Lucas Safanelli ◽  
Sabine Chabrillat ◽  
Eyal Ben-Dor ◽  
José A. M. Demattê
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Performance Testing ◽  
Bare Soil ◽  
Prediction Performance ◽  
Spatial And Temporal Variation ◽  
Gradient Boosting ◽  
Landsat Images ◽  
Soil Attributes ◽  
Sampling Campaign

Reflectance of light across the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral region is very useful for investigating mineralogical, physical and chemical properties of soils, which can reduce the need for traditional wet chemistry analyses. As many collections of multispectral satellite data are available for environmental studies, a large extent with medium resolution mapping could be benefited from the spectral measurements made from remote sensors. In this paper, we explored the use of bare soil composites generated from the large historical collections of Landsat images for mapping cropland topsoil attributes across the European extent. For this task, we used the Geospatial Soil Sensing System (GEOS3) for generating two bare soil composites of 30 m resolution (named synthetic soil images, SYSI), which were employed to represent the median topsoil reflectance of bare fields. The first (framed SYSI) was made with multitemporal images (2006–2012) framed to the survey time of the Land-Use/Land-Cover Area Frame Survey (LUCAS) soil dataset (2009), seeking to be more compatible to the soil condition upon the sampling campaign. The second (full SYSI) was generated from the full collection of Landsat images (1982–2018), which although displaced to the field survey, yields a higher proportion of bare areas for soil mapping. For evaluating the two SYSIs, we used the laboratory spectral data as a reference of topsoil reflectance to calculate the Spearman correlation coefficient. Furthermore, both SYSIs employed machine learning for calibrating prediction models of clay, sand, soil organic carbon (SOC), calcium carbonates (CaCO3), cation exchange capacity (CEC), and pH determined in water, using the gradient boosting regression algorithm. The original LUCAS laboratory spectra and a version of the data resampled to the Landsat multispectral bands were also used as reference of prediction performance using VIS-NIR-SWIR multispectral data. Our results suggest that generating a bare soil composite displaced to the survey time of soil observations did not improve the quality of topsoil reflectance, and consequently, the prediction performance of soil attributes. Despite the lower spectral resolution and the variability of soils in Europe, a SYSI calculated from the full collection of Landsat images can be employed for topsoil prediction of clay and CaCO3 contents with a moderate performance (testing R2, root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.44, 9.59, 1.77, and 0.36, 13.99, 1.54, respectively). Thus, this study shows that although there exist some constraints due to the spatial and temporal variation of soil exposures and among the Landsat sensors, it is possible to use bare soil composites for mapping key soil attributes of croplands across the European extent.

scholarly journals Land use/land cover patterns and challenges to sustainable management of the Mono transboundary biosphere reserve between Togo and Benin, West Africa

2020 ◽  
Vol 14 (5) ◽  
pp. 1734-1751
Author(s):  
Kossi Adjonou ◽  
Issa Adbou-Kérim Bindaoudou ◽  
Kossi Novinyo Segla ◽  
Rodrigue Idohou ◽  
Kolawole Valère Salako ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Planning ◽  
Forest Cover ◽  
Satellite Image ◽  
Spatial Dynamics ◽  
Biosphere Reserve ◽  
Ecological Monitoring ◽  
Bare Soil ◽  
Dense Forest

The Mono Transboundary Biosphere Reserve (RBTM) has significant resources but faces many threats that lead to habitat fragmentation and reduction of ecosystem services. This study, based on satellite image analysis and processing, was carried out to establish the baseline of land cover and land use status and to analyze their dynamics over the period 1986 to 2015. The baseline of land cover established six categories of land use including wetlands (45.11%), mosaic crops/fallow (25.99%), savannas (17.04%), plantation (5.50%), agglomeration/bare soil (4.38%) and dense forest (1.98%). The analysis of land use dynamics showed a regression for wetlands (-23%), savannas (-16.06%) and dense forest (-7.60%). On the contrary, occupations such as mosaic crops/fallow land, urban agglomerations/bare soil and plantation increase in area estimated at respectively 128.64%, 93.94% and 45.23%. These results are of interest to stakeholders who assess decisions affecting the use of natural resources and provide environmental information essential for applications ranging from land-use planning, forest cover monitoring and the production of environmental statistics.Keywords: Land use, baseline, spatial dynamics, environmental statistics, ecological monitoring.

Prediction of Energy Usage Intensity of the Commercial Blocks in the Urban Areas of Taiwan

2013 ◽  
Vol 316-317 ◽  
pp. 167-170
Author(s):  
Hsien Te Lin ◽  
Kang Li Wu ◽  
Lan Hsuan Lai
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Urban Areas ◽  
Prediction Models ◽  
Research Subjects ◽  
Energy Usage ◽  
Commercial Activity ◽  
Activity Intensity ◽  
Taichung City ◽  
Urban Energy

This study develops a set of prediction models to estimate energy usage for commercial blocks in urban areas. Commercial blocks in Taipei City, Taichung City, and Tainan City of Taiwan were selected as research subjects, and a total of 93 blocks were surveyed. With the survey of land use type and intensity, building usage, and actual building energy usage of the blocks, a set of commercial block energy usage prediction models were proposed and tested, and two commonly used land use variables (the floor area ratio and total floor areas) were included as independent variables in these models. The average number of floors and an indicator of commercial activity intensity were then employed to help define the level of energy usage intensity for the blocks surveyed in order to refine the models, and the R2 for both energy usage prediction models increases to 0.94 and 0.92, respectively. This study shows that land use variables can be utilized to rapidly estimate energy usage of commercial blocks in urban areas, and the finding should be useful for decision-makers to formulate policies on urban energy management and land use planning.

scholarly journals ASSESSMENT OF FOREST DISTURBANCES AND CARBON STOCK IN PANTABANGAN-CARRANGLAN WATERSHED, PHILIPPINES USING REMOTE SENSING

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 147-152
Author(s):  
J. J. V. Dida ◽  
C. L. Tiburan Jr. ◽  
I. Saizen
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Planning ◽  
Carbon Stock ◽  
Satellite Images ◽  
Landsat 8 ◽  
Forest Disturbances ◽  
Carbon Sequestration Potential ◽  
Sequestration Potential ◽  
Potential Carbon

Abstract. Forest disturbances contribute to the decrease in carbon sequestration potential and ecosystem services in a watershed. One of the important watersheds that is affected by land use changes and disturbances is the Pantabangan-Carranglan Watershed. The ability of the watershed to provide ecosystem services is affected by the existing land use and land cover (LULC) and its future trends. Therefore, this study aims to assess the changes in the LULC, forest disturbances, and potential carbon stock in the watershed using satellite images. The LULC types and indices used in detecting forest disturbances were classified and generated from the Landsat 8 satellite images covering two different years. The potential carbon storage in the watershed was estimated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Carbon model. The information generated can be used in the conduct of valuation studies and land use planning in the watershed.

scholarly journals Supervised Classification of Satellite Images to Analyze Multi-Temporal Land Use and Coverage : A Case Study for the Town of MARABA, State of PARA, Brazil

2015 ◽  
Author(s):  
Priscila Siqueira Aranha ◽  
Flavia Pessoa Monteiro ◽  
Paulo Andre Ignacio Pontes ◽  
Jorge Antonio Moraes de Souza ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
...  
Keyword(s):  
Land Use ◽  
Supervised Classification ◽  
Satellite Images ◽  
Multi Temporal ◽  
The Town

scholarly journals Impact of Land Use Land Cover Change on Coastal Tourism in Kundapura, Karnataka, Using Multi-temporal Remotely Sensed Data and GIS Techniques

2018 ◽  
Vol 13 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Shekar Naik ◽  
H Gangadhara Bhat ◽  
T N Sreedhara
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Agricultural Land ◽  
Coastal Development ◽  
Land Use Land Cover ◽  
Coastal Tourism ◽  
Remotely Sensed Data ◽  
Erdas Imagine ◽  
Multi Temporal

The present study is an attempt to examine the Land Use Land Cover changes in parts of Kundapura Taluk in Karnataka for the period 2006 and 2016 and its impact on coastal tourism. IRS satellite images of 2006 and 2016 have been used and processed using ERDAS Imagine and ArcGIS. The result indicated tremendous changes, particularly in mixed urban and agricultural land and proved that RS/GIS has advantages over conventional techniques. The result obtained, based on the multi-dated satellite data study, will assist in decision making and help to take appropriate measures to monitor and regulate coastal development in order to achieve sustainable and integrated coastal development.

scholarly journals Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump

2021 ◽  
Author(s):  
Vahid Khosravi ◽  
Faramarz Doulati Ardejani ◽  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Near Infrared ◽  
Prediction Models ◽  
Fitness Function ◽  
Reflectance Spectroscopy ◽  
Landsat 8 ◽  
Least Squares Regression ◽  
Cr Concentration ◽  
Sentinel 2A

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible--Near Infrared--Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky–Golay First-Derivative (SG-FD) and Savitzky–Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using correlation analysis, Partial Least Squares Regression (PLSR) and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg.kg-1 and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using Ordinary Kriging (OK) technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. The findings of this study indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered as a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas of Sarcheshmeh in Iran.

Sign in / Sign up

Export Citation Format

Share Document