Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data

2021 ◽  
Author(s):  
Zsófia Adrienn Kovács ◽  
János Mészáros ◽  
Mátyás Árvai ◽  
Annamária Laborczi ◽  
Gábor Szatmári ◽  
...  
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Hyperspectral Imagery ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Soil Carbon Content ◽  
Space Agency ◽  
Italian Space Agency ◽  
Agricultural Areas ◽  
Imagery Data

<p>The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.</p><p>The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.</p><p>The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.</p><p>Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km<sup>2</sup> in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.</p><p>We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.</p><p>For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.</p><p> </p><p><strong>Acknowledgment:</strong> Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022). Our project carried out using PRISMA Products, © of the Italian Space Agency (ASI), delivered under an ASI License to use.</p>

scholarly journals Flood Susceptibility Prediction Using Hybrid-Based Approaches of Support Vector Regression Model and Meta-Heuristic Algorithms

2021 ◽  
Author(s):  
Hossein Hamedi Sorajar ◽  
Ali Asghar Alesheikh ◽  
Mahdi Panahi ◽  
Saro Lee
Keyword(s):  
Land Use ◽  
Critical Role ◽  
Influential Factor ◽  
Slope Aspect ◽  
Support Vector ◽  
Human Beings ◽  
Operating Characteristics ◽  
Topographic Wetness Index ◽  
Support Vector Regression Model ◽  
Testing Dataset

Abstract Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a critical role in identifying prone areas and reducing the damage caused by landslides in each area. In the present study, deep learning algorithms including convolutional neural network (CNN) and long short-term memory (LSTM) were used to identify landslide prone areas in Ardabil province, Iran. Equql to 312 landslide locations were identified and randomly divided into train and test datasets at 70–30% ratios. Then, according to previous studies and environmental conditions in the study area, twelve factors affecting the occurrence of landslides were selected, namely altitude, slope angle, slope aspect, topographic wetness index (TWI), profile curvature, plan curvature, land-use, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. The ratio of the importance of each influential factor in landslide occurrence was obtained through information gain ranking filter (IGRF) method and it was found that land-use and profile curvature had the highest and lowest impacts, respectively. Afterwards, LSMs were generated using CNN and LSTM algorithms. In the next step, the performance of the models was evaluated based on the area under curve (AUC) value of receiver operating characteristics curve and the root mean square error (RMSE) method. The AUC values for CNN and LSTM models were 0.821 and 0.832, respectively. Furthermore, the RMSE values in the CNN model for each of the training and testing dataset were 0.121 and 0.132, respectively. The RMSE values in the LSTM model for each of the training and testing dataset were 0.185 and 0.188, respectively. Therefore, it can be concluded that CNN performance is slightly better than LSTM; but in general, both models have close performance and the accuracy of both models is acceptable.

scholarly journals A COMPARISON BETWEEN THREE CONDITIONING FACTORS DATASET FOR LANDSLIDE PREDICTION IN THE SAJADROOD CATCHMENT OF IRAN

2020 ◽  
Vol V-3-2020 ◽  
pp. 625-632
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
V. Saeidi ◽  
M. B. A. Gibril ◽  
...  
Keyword(s):  
Land Use ◽  
Total Curvature ◽  
The Other ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Mazandaran Province ◽  
Landslide Prediction ◽  
Conditioning Factors ◽  
Other Hand

Abstract. This study investigates the effectiveness of three datasets for the prediction of landslides in the Sajadrood catchment (Babol County, Mazandaran Province, Iran). The three datasets (D1, D2 and D3) are constructed based on fourteen conditioning factors (CFs) obtained from Digital Elevation Model (DEM) derivatives, topography maps, land use maps and geological maps. Precisely, D1 consists of all 14 CFs namely altitude, slope, aspect, topographic wetness index (TWI), terrain roughness index (TRI), distance to fault, distance to stream, distance to road, total curvature, profile curvatures, plan curvature, land use, steam power index (SPI) and geology. D2, on the other hand, is a subset of D1, consisting of eight CFs. This reduction was achieved by exploiting the Variance Inflation Factor, Gini Importance Indices and Chi-Square factor optimization methods. Dataset D3 includes only selected factors derived from the DEM. Three supervised classification algorithms were trained for landslide prediction namely the Support Vector Machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN). Experimental results indicate that D2 performed the best for landslide prediction with the SVM producing the best overall accuracy at 82.81%, followed by LR (81.71%) and ANN (80.18%). Extensive investigations on the results of factor optimization analysis indicate that the CFs distance to road, altitude, and geology were significant contributors to the prediction results. Land use map, slope, total-, plan-, and profile curvature and TRI, on the other hand, were deemed redundant. The analysis also revealed that sole reliance on Gini Indices could lead to inefficient optimization.

scholarly journals GIS-Based Flood Susceptibility Mapping Using Statistical Index and Weighting Factor Models

2021 ◽  
Vol 19 (6) ◽  
pp. 1-13
Author(s):  
Worawit Suppawimut ◽  
Keyword(s):  
Land Use ◽  
Weighting Factor ◽  
Susceptibility Mapping ◽  
Slope Aspect ◽  
Economic Losses ◽  
Soil Drainage ◽  
Topographic Wetness Index ◽  
Statistical Index ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are one of the most devastating natural hazards, causing deaths, economic losses, and destruction of property. Flood susceptibility maps are an essential tool for flood mitigation and preparedness planning. This study mapped flood susceptibility using statistical index (SI) and weighting factor (WF) models in San Pa Tong District, Chiang Mai Province, Thailand. The conditioning factors used to perform flood susceptibility mapping were elevation, slope, aspect, curvature, topographic wetness index, stream power index, rainfall, distance from rivers, stream density, soil drainage, land use, and road density. The flood data were randomly classified as training data for mapping (70% of data) and testing data for model validation (30% of data). The results revealed that the SI and WF models classified 49.49% and 51.74% of the study area, respectively, as very highly susceptible to flooding. In the WF model, the factors with the greatest influence were land use, soil drainage, and elevation. The validation of the models using the area under the curve revealed that the success rates of the SI and WF models were 91.80% and 93.06%, while the prediction rates were 92.05% and 93.52%, respectively. The results from this study can be useful for local authorities in San Pa Tong District for flood preparedness and mitigation.

scholarly journals Application of PRISMA satellite hyperspectral imagery to mineral alteration mapping at Cuprite, Nevada, USA

2020 ◽  
Vol 10 (2) ◽  
pp. 87
Author(s):  
Enton Bedini ◽  
Jiang Chen
Keyword(s):  
Mineral Exploration ◽  
Hyperspectral Imagery ◽  
Wavelength Region ◽  
Test Site ◽  
Hyperspectral Data ◽  
Imaging Systems ◽  
Arid Environments ◽  
Space Agency ◽  
Mineral Alteration ◽  
Italian Space Agency

The PRISMA hyperspectral imaging satellite of the Italian Space Agency was launched into orbit on March 22, 2019. The PRISMA is a pushbroom sensor that records 250 hyperspectral bands in the 0.4-2.5 μm wavelength region at a spatial resolution of 30 m. The swath of the hyperspectral imagery is 30 km. This study evaluates the application of the PRISMA hyperspectral imagery to mineral exploration. The study area is the Cuprite in Nevada, USA. Cuprite has served as test-site for a number of airborne and spaceborne remote sensing imaging systems. The Cuprite PRISMA hyperspectral data were analyzed with the Advanced Coherence Estimator algorithm. The analysis of the hyperspectral imagery accurately mapped the spatial distribution of alunite, kaolinite, hydrated silica, muscovite and buddingtonite. The study shows that the PRISMA hyperspectral imagery is a useful tool for mineral exploration projects in arid and semi-arid environments.  

scholarly journals Relating environmental variables with aquatic community structure in an agricultural/urban coldwater stream

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel T. L. Myers ◽  
Richard R. Rediske ◽  
James N. McNair ◽  
Aaron D. Parker ◽  
E. Wendy Ogilvie
Keyword(s):  
Land Use ◽  
Environmental Variables ◽  
Fish Community ◽  
Agricultural Land ◽  
Urban Land ◽  
Urban Land Use ◽  
Aquatic Communities ◽  
Natural Stream ◽  
Longitudinal Gradients ◽  
Agricultural Areas

Abstract Background Urban areas are often built along large rivers and surrounded by agricultural land. This may lead to small tributary streams that have agricultural headwaters and urbanized lower reaches. Our study objectives assessed are as follows: (1) landscape, geomorphic, and water quality variables that best explained variation in aquatic communities and their integrity in a stream system following this agricultural-to-urban land use gradient; (2) ways this land use gradient caused aquatic communities to differ from what would be expected for an idealized natural stream or other longitudinal gradients; and (3) whether the impacts of this land use gradient on aquatic communities would grow larger in a downstream direction through the agricultural and urban developments. Our study area was an impaired coldwater stream in Michigan, USA. Results Many factors structured the biological communities along the agricultural-to-urban land use gradient. Instream woody debris had the strongest relationship with EPT (Ephemeroptera, Plecoptera, and Trichoptera) abundance and richness and were most common in the lower, urbanized watershed. Fine streambed substrate had the strongest relationship with Diptera taxa and surface air breather macroinvertebrates and was dominant in agricultural headwaters. Fish community assemblage was influenced largely by stream flow and temperature regimes, while poor fish community integrity in lower urban reaches could be impacted by geomorphology and episodic urban pollution events. Scraping macroinvertebrates were most abundant in deforested, first-order agricultural headwaters, while EPT macroinvertebrate richness was the highest downstream of agricultural areas within the urban zone that had extensive forest buffers. Conclusion Environmental variables and aquatic communities would often not conform with what we would expect from an idealized natural stream. EPT richness improved downstream of agricultural areas. This shows promise for the recovery of aquatic systems using well-planned management in watersheds with this agricultural-to-urban land use pattern. Small patches of forest can be the key to conserving aquatic biodiversity in urbanized landscapes. These findings are valuable to an international audience of researchers and water resource managers who study stream systems following this common agricultural-to-urban land use gradient, the ecological communities of which may not conform with what is generally known about land use impacts to streams.

scholarly journals Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

2021 ◽  
Vol 13 (15) ◽  
pp. 8332
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Slope Aspect ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Slope Gradient ◽  
Phosphorus And Potassium ◽  
Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

scholarly journals Twenty-Three Years of Land-Use Changes Induced Considerable Threats to the Main Wetlands of Sardinia and Sicily (Italy) along the Mediterranean Bird Flyways

Diversity ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 240
Author(s):  
Alessandro Ferrarini ◽  
Marco Gustin ◽  
Claudio Celada
Keyword(s):  
Land Use ◽  
Habitat Degradation ◽  
Land Use Changes ◽  
Biodiversity Loss ◽  
Natura 2000 ◽  
Global Level ◽  
Natura 2000 Network ◽  
Stopover Sites ◽  
The Mediterranean ◽  
Agricultural Areas

Biodiversity loss has multiple causes, but habitat degradation through land-use change is the predominant driver. We investigated the effectiveness of the Natura 2000 network in preserving the main wetlands of the two largest islands of the Mediterranean region, whose conservation is critical for many avian species at European and global level, in a 23-year period (1990–2012). In Sardinia, the surroundings of 22 wetlands were affected by an increase in artificial areas (+64 ha/year) and decrease in agricultural (−54 ha/year) and natural (−17 ha/year) ones. In Sicily, the surroundings of 16 wetlands were impacted by an increase in agricultural areas (+50 ha/year) and decrease in natural and semi-natural ones (−62 ha/year). Results show that the Natura 2000 policies were effective in preserving wetlands (no shrinkages detected in both regions), but their surroundings experienced intense processes of degradation and artificialization in all the sub-periods considered (1990–2000, 2000–2006, 2006–2012), whose effects are now threatening waterbirds and wetland integrity. The enlargement of the existing Natura 2000 sites, the creation of new ones and the speedup of the application of the rules of the Habitats and Birds Directives seem necessary to counteract the rapid land-use changes around these important stopover sites.

scholarly journals Analyzing the Patterns, Trends and Dynamics of the Land-Use Changes in Azores Region: From 1990 to 2018

2021 ◽  
Vol 13 (10) ◽  
pp. 5433
Author(s):  
Rui Alexandre Castanho ◽  
José Manuel Naranjo Gómez ◽  
Gualter Couto ◽  
Pedro Pimentel ◽  
Áurea Sousa ◽  
...  
Keyword(s):  
Land Use ◽  
Development Strategy ◽  
Land Use Changes ◽  
Regional Government ◽  
Land Uses ◽  
Comprehensive Planning ◽  
Sustainability Planning ◽  
The Enlightenment ◽  
Planning Strategies ◽  
Agricultural Areas

The remarkable richness and singularity of the Azorean Region (located 38° North) and its landscapes require a sharp, well-defined, and comprehensive planning policy. Bearing in mind the significance of this issue in the enlightenment of sustainability, planning strategies should be based and supported by different studies and thematic domains to understand the problem thoroughly. Using GIS (Geographic Information Systems), the present article enables us to identify the dynamics and patterns of the evolution of the Land-Use Changes in the Azores Region from 1990 to 2018. In aggregate, the Azores islands showed growth in artificial surfaces and forest and seminatural land-uses by essentially decreasing agricultural areas—most resulting from the economic and social development strategy pursued by several Azorean governments. Moreover, this study permits us to reinforce that the Azores Archipelago’s land-uses has undergone multiple changes—marked by increasing and decreasing periods. In fact, some of these reducing dynamics are disturbing. They require closer monitorization by regional government actors to give protection, preservation, and conservation to these incomparable ultra-peripheral landscapes, environments, ecosystems, and the region as a whole.

scholarly journals A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4893 ◽  
Author(s):  
Hejar Shahabi ◽  
Ben Jarihani ◽  
Sepideh Tavakkoli Piralilou ◽  
David Chittleborough ◽  
Mohammadtaghi Avand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Vegetation Index ◽  
Scale Parameter ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Slope Length ◽  
Optimal Scale ◽  
Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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