scholarly journals Digital Mapping of Soil Drainage Classes Using Multitemporal RADARSAT-1 and ASTER Images and Soil Survey Data

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Mohamed Abou Niang ◽  
Michel Nolin ◽  
Monique Bernier ◽  
Isabelle Perron
Keyword(s):  
Land Use ◽  
Remote Sensing Data ◽  
Soil Survey ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Land Use Type ◽  
Soil Drainage ◽  
Digital Mapping ◽  
Optimal Subset ◽  
Earth Observation Data

Discriminant analysis classification (DAC) and decision tree classifiers (DTC) were used for digital mapping of soil drainage in the Bras-d’Henri watershed (QC, Canada) using earth observation data (RADARSAT-1 and ASTER) and soil survey dataset. Firstly, a forward stepwise selection was applied to each land use type identified by ASTER image in order to derive an optimal subset of soil drainage class predictors. The classification models were then applied to these subsets for each land use and merged to obtain a digital soil drainage map for the whole watershed. The DTC method provided better classification accuracies (29 to 92%) than the DAC method (33 to 79%) according to the land use type. A similarity measure (S) was used to compare the best digital soil drainage map (DTC) to the conventional soil drainage map. Medium to high similarities (0.6≤S<0.9) were observed for 83% (187 km2) of the study area while 3% of the study area showed very good agreement (S≥0.9). Few soil polygons showed very weak similarities (S<0.3). This study demonstrates the efficiency of combining radar and optical remote sensing data with a representative soil dataset for producing digital maps of soil drainage.

scholarly journals A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 491
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing Data ◽  
Observation Data ◽  
Multiple Sources ◽  
Accuracy Improvement ◽  
Geometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Earth Observation Data ◽  
Sar Imagery ◽  
Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

scholarly journals Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

2019 ◽  
Vol 11 (15) ◽  
pp. 4160 ◽  
Author(s):  
Qin Liu ◽  
Tiange Shi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
River Basin ◽  
Hot Spot ◽  
Remote Sensing Data ◽  
Cold Spot ◽  
Strengthening Effect ◽  
Land Use Type ◽  
Sensing Data ◽  
Ecological Vulnerability

Ecological vulnerability assessment increases the knowledge of ecological status and contributes to formulating local plans of sustainable development. A methodology based on remote sensing data and spatial principal component analysis was introduced to discuss ecological vulnerability in the Toutun River Basin (TRB). Exploratory spatial data analysis and a geo-detector were employed to evaluate the spatial and temporal distribution characteristics of ecological vulnerability and detect the driving factors. Four results were presented: (1) During 2003 and 2017, the average values of humidity, greenness, and heat in TRB increased by 49.71%, 11.63%, and 6.51% respectively, and the average values of dryness decreased by 165.24%. However, the extreme differences in greenness, dryness, and heat tended to be obvious. (2) The study area was mainly dominated by a high and extreme vulnerability grade, and the ecological vulnerability grades showed the distribution pattern that the northern desert area was more vulnerable than the central artificial oasis, and the central artificial oasis was more vulnerable than the southern mountainous area. (3) Ecological vulnerability in TRB showed significant spatial autocorrelation characteristics, and the trend was enhanced. The spatial distribution of hot/cold spots presented the characteristics of “hot spot—cold spot—secondary hot spot—cold spot” from north to south. (4) The explanatory power of each factor of ecological vulnerability was temperature (0.5955) > land use (0.5701) > precipitation (0.5289) > elevation (0.4879) > slope (0.3660) > administrative division (0.1541). The interactions of any two factors showed a non-linear strengthening effect, among which, land use type ∩ elevation (0.7899), land use type ∩ precipitation (0.7867), and land use type ∩ temperature (0.7791) were the significant interaction for ecological vulnerability. Overall, remote sensing data contribute to realizing a quick and objective evaluation of ecological vulnerability and provide valuable information for decision making concerning ecology management and region development.

scholarly journals Identification of Dominant Factors Affecting Soil Erosion and Water Yield within Ecological Red Line Areas

2020 ◽  
Vol 12 (3) ◽  
pp. 399 ◽  
Author(s):  
Jiangbo Gao ◽  
Yuan Jiang ◽  
Huan Wang ◽  
Liyuan Zuo
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Soil Erosion ◽  
Spatial Heterogeneity ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Water Yield ◽  
Land Use Type ◽  
Functional Areas ◽  
Red Line

Soil conservation and water retention are important metrics for designating key ecological functional areas and ecological red line (ERL) areas. However, research on the quantitative identification of dominant environmental factors in different ecological red line areas remains relatively inadequate, which is unfavorable for the zone-based management of ecological functional areas. This paper presents a case study of Beijing’s ERL areas. In order to objectively reflect the ecological characteristics of ERL areas in Beijing, which is mainly dominated by mountainous areas, the application of remote sensing data at a high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations as well as soil erosion and water yield were calculated using the revised universal soil loss equation (RUSLE) and integrated valuation of ecosystem services and tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type, and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing’s ERL areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different ERL areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing’s ERL areas, explaining greater than 30% of its spatial heterogeneity. Land use type could explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (>55% in various ERL areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in ERL areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ERL areas. Thus, in efforts to enhance ERL protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ERL areas.

Using Earth Observation Data for Soil Drainage Classification and Mapping

2010 ◽  
Author(s):  
Mohamed Abou Niang ◽  
M C Nolin ◽  
S Perrault ◽  
I Perron ◽  
M Bernier
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Soil Drainage ◽  
Earth Observation Data

PITFALLS IN INTERPRETATION OF SOIL DRAINAGE FROM SOIL SURVEY INFORMATION

1986 ◽  
Vol 66 (1) ◽  
pp. 37-44 ◽  
Author(s):  
J. A. McKEAGUE ◽  
G. C. TOPP
Keyword(s):  
Land Use ◽  
Hydraulic Conductivity ◽  
Tile Drainage ◽  
Soil Survey ◽  
Major Influence ◽  
Soil Morphology ◽  
Soil Series ◽  
Soil Drainage ◽  
Near Surface ◽  
Drainage Texture

Soil drainage groups assigned on the basis of soil survey information were evaluated against measured saturated hydraulic conductivity (Ksat) data for nine soils in Ontario. The drainage groups used in the drainage guide for Ontario, are based mainly on assumed relationships between soil texture and the capacity of the soil to transmit water.Measured Ksat values were incompatible with the drainage groups assigned to at least four of the nine soils. For the soils tested, there was very little relationship between texture and Ksat. Structure, including porosity, had a major influence on Ksat, and near-surface structure is influenced greatly by land use. Thus, general interpretations of the drainage characteristics of soil series have serious limitations. The usefulness of soil survey information for interpretation of soil drainage could be increased by improved description of soil morphology and by reliable estimates of Ksat during mapping. Such estimates can be based on morphology if they are regularly recalibrated by measurement. Key words: Hydraulic conductivity, tile drainage, texture, soil morphology

Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

2017 ◽  
Vol 33 (11) ◽  
pp. 1202-1222 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prosper Basommi Laari ◽  
Sk. Mustak ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Earth Observation Data ◽  
Tons River Basin

scholarly journals Europe’s Green Arteries—A Continental Dataset of Riparian Zones

2016 ◽  
Author(s):  
Christof J. Weissteiner ◽  
Martin Ickerott ◽  
Hannes Ott ◽  
Markus Probeck ◽  
Gernot Ramminger ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Vegetation Indices ◽  
Biodiversity Loss ◽  
Ecosystem Functions ◽  
Riparian Zones ◽  
Observation Data ◽  
Riparian Areas ◽  
Earth Observation Data

Riparian zones represent ecotones between terrestrial and aquatic ecosystems and are of utmost importance to biodiversity and ecosystem functions. Modelling/mapping of these valuable and fragile areas is needed for an improved ecosystem management, based on an accounting of changes and on monitoring of their functioning in time. In Europe, the main legislative driver behind this goal is the European Commission&rsquo;s Biodiversity Strategy to 2020, on one hand aiming at reducing biodiversity loss, on the other hand enhancing ecosystem services by 2020, and restoring them as far as feasible. A model, based on Earth Observation data, including Digital Elevation Models, hydrological, soil, land cover/land use data, and vegetation indices is employed in a multi-modular and stratified approach, based on fuzzy logic and object based image analysis, to delineate potential, observed and actual riparian zones. The approach is designed in an open modular way, allowing future modifications and repeatability. The results represent a first step of a future monitoring and assessment campaign for European riparian zones and their implications on biodiversity and on ecosystem functions and services. Considering the complexity and the enormous extent of the area, covering 39 European countries, including Turkey, the level of detail is unprecedented. Depending on the accounting modus, 0.95%&ndash;1.19% of the study area can be attributed as actual riparian area (considering Strahler&rsquo;s stream orders 3-8, based on the Copernicus EU-Hydro dataset), corresponding to 55,558&ndash;69.128 km2. Similarly depending on the accounting approach, the potential riparian zones are accounted for about 3-5 times larger. Land cover/land use in detected riparian areas was mainly of semi-natural characteristics, while the potential riparian areas are predominately covered by agriculture, followed by semi-natural and urban areas.

Development of earth observation data cubes for monitoring land degradation processes in South Africa

2021 ◽  
Author(s):  
Insa Otte ◽  
Nosiseko Mashiyi ◽  
Pawel Kluter ◽  
Steven Hill ◽  
Andreas Hirner ◽  
...  
Keyword(s):  
South Africa ◽  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
South African ◽  
Land Degradation ◽  
Earth Observation ◽  
Observation Data ◽  
Data Cubes ◽  
Earth Observation Data

&lt;p&gt;Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African &amp;#8220;Science Partnerships for the Adaptation to Complex Earth System Processes&amp;#8221;. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.&lt;/p&gt;

scholarly journals Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2474 ◽  
Author(s):  
Inken Müller ◽  
Hannes Taubenböck ◽  
Monika Kuffer ◽  
Michael Wurm
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Population Densities ◽  
Poverty And Inequality ◽  
Test Of Homogeneity ◽  
Chi Squared ◽  
Earth Observation Data ◽  
Do So

Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of slums. Contrary to the perception indirectly stated in the literature, we find that slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of slums on hills steeper than 10° is even less than 5% of all slums. However, we also find a higher likelihood of slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas.

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