scholarly journals Use of Multi-Seasonal Satellite Images to Predict SOC from Cultivated Lands in a Montane Ecosystem

2021 ◽  
Vol 13 (23) ◽  
pp. 4772
Author(s):  
Sushil Lamichhane ◽  
Kabindra Adhikari ◽  
Lalit Kumar

Although algorithms are well developed to predict soil organic carbon (SOC), selecting appropriate covariates to improve prediction accuracy is an ongoing challenge. Terrain attributes and remote sensing data are the most common covariates for SOC prediction. This study tested the predictive performance of nine different combinations of topographic variables and multi-season remotely sensed data to improve the prediction of SOC in the cultivated lands of a middle mountain catchment of Nepal. The random forest method was used to predict SOC contents, and the quantile regression forest for quantifying the prediction uncertainty. Prediction of SOC contents was improved when remote sensing data of multiple seasons were used together with the terrain variables. Remote sensing data of multiple seasons capture the dynamic conditions of surface soils more effectively than using an image of a single season. It is concluded that the use of remote sensing images of multiple seasons instead of a snapshot of a single period may be more effective for improving the prediction of SOC in a digital soil mapping framework. However, an image with the right timing of cropping season can provide comparable results if a parsimonious model is preferred.

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Biswajeet Pradhan

AbstractThis paper summarizes the findings of groundwater potential zonation mapping at the Bharangi River basin, Thane district, Maharastra, India, using Satty’s Analytical Hierarchal Process model with the aid of GIS tools and remote sensing data. To meet the objectives, remotely sensed data were used in extracting lineaments, faults and drainage pattern which influence the groundwater sources to the aquifer. The digitally processed satellite images were subsequently combined in a GIS with ancillary data such as topographical (slope, drainage), geological (litho types and lineaments), hydrogeomorphology and constructed into a spatial database using GIS and image processing tools. In this study, six thematic layers were used for groundwater potential analysis. Each thematic layer’s weight was determined, and groundwater potential indices were calculated using groundwater conditions. The present study has demonstrated the capabilities of remote sensing and GIS techniques in the demarcation of different groundwater potential zones for hard rock basaltic basin.


Author(s):  
Ram L. Ray ◽  
Maurizio Lazzari ◽  
Tolulope Olutimehin

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.


2020 ◽  
Vol 12 (24) ◽  
pp. 4139
Author(s):  
Ruirui Wang ◽  
Wei Shi ◽  
Pinliang Dong

The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.


2013 ◽  
Vol 10 (5) ◽  
pp. 6153-6192
Author(s):  
F.-J. Chang ◽  
W. Sun

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.


2021 ◽  
Vol 929 (1) ◽  
pp. 012002
Author(s):  
L R Bikeeva ◽  
Z Kh Safarov ◽  
M G Yuldasheva ◽  
N M Akramova ◽  
Sh A Umarov

Abstract In recent years, remote sensing data are increasingly used in the practice of oil and gas prospecting. This article discusses the main methodological aspects of identifying oil and gas promising structures by using materials for interpreting remote sensing data and a complex of geological and geophysical data. Remotely sensed data exhibit a regional review of the various geological formations and tectonic fracture zone and faults that are otherwise not possible detection by human eyes on the ground. The method of structural interpretation space image allows you to: detail the internal structure of oil and gas regions; to reveal the position and features of the tectonic blocks, structures of the second and third (anticlines, synclines, monoclines, etc.) orders; identify major disruptive violations; identify chains of local structures; fix the transverse structural elements that determine tectonic fragmentation. By deciphering the remote sensing data, the distribution and nature of the lineament network marking disjunctive dislocations and zones of increased fracturing are revealed and analyzed, as well as ring structures are detected, which in most cases indicate local structures of the sedimentary cover at different depth sections. The lithology and lineament interpreted from these multi-level data were integrated with data collected from the ground.


Author(s):  
G Rushingabigwi ◽  
W Kalisa ◽  
P Nsengiyumva ◽  
F Zimulinda ◽  
D Mukanyiligira ◽  
...  

The desert's dust and anthropogenic biomass burning's black carbon (BC) in the tropical regions are associated with many effects on climate and air quality. The dust and BC are the selected aerosols, which affect health by polluting the breathable air. This research discusses the effects of both the aerosols, especially while they interact with the clouds. The respective aerosol extinction optical thickness (AOT) extinction was analysed with the sensible heat from Turbulence. The research purposes to quantitatively study the remote sensing data for fine particulate matter, PM2.5, heterogeneously mixing both the dust and the pulverized black carbon's soot or ash, to analyse at which levels PM2.5 can endanger human health in the sub-Saharan region. The mainly analysed data had been assimilated from different remote sensing tools; the Goddard interactive online visualization and analysis infrastructure (GIOVANNI) was in the centre of data collection; GIS, the research data analysis software. In results, the rise and fall of the averaged sensible heat were associated with the rise and fall of averaged aerosol extinction AOT; the direct effects of the selected aerosols on the clouds are also presented. Regarding the health effects, PM2.5 quantities are throughout beyond the tolerably recommended quantity of 25μg/m3; thus, having referred to erstwhile research, inhabitants would consume food and drug supplements which contain vanillic acid during dusty seasons. Keywords: Geographic Information System (GIS), remotely sensed data, spatio-temporal (data) analysis


Author(s):  
R. Ghasemi Nejad ◽  
P. Pahlavani ◽  
B. Bigdeli

Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.


Author(s):  
Nataliia Pazynych

The article presents the results of the investigation of landslides in the right bank of the Kyiv, on the basis of space images, digital elevation models using two geomorphological methods. The result of the complexization of geomorphological methods was the compilation of a synthetic map of dynamic relief plastics, which reflects the structure of linear and area elements of the relief. The conducted comparison of geomorphological constructions with landslide bodies allowed to identify zones and areas of increased danger of landslide formation.


Author(s):  
M. R. Mohd Salleh ◽  
N. I. Ishak ◽  
K. A. Razak ◽  
M. Z. Abd Rahman ◽  
M. A. Asmadi ◽  
...  

<p><strong>Abstract.</strong> Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia.</p>


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