scholarly journals Vegetation covers change and its impact on Barchan Dune morphology in Parangtritis Coast, Indonesia

2020 ◽  
Vol 200 ◽  
pp. 02026
Author(s):  
Agung Laksono ◽  
Agatha Andriantari Saputri ◽  
Calvina Izumi Bunga Pratiwi ◽  
Muhammad Zaki Arkan ◽  
Ratih Fitria Putri
Keyword(s):  
Land Use Changes ◽  
Vegetation Coverage ◽  
Actual Condition ◽  
Vegetation Density ◽  
Field Surveys ◽  
Face Height ◽  
Barchan Dune ◽  
Transport Zone ◽  
Barchan Dunes ◽  
Sentinel 2

Barchan dune is a peculiar type of dune that forms in wind corridors at the Inner Zone of Parangtritis Sand Dune. Their existence is increasingly threatened by land-use changes, especially vegetation coverage. This research illustrates the dynamics of vegetation cover change at the Inner Zone with the NDVI value approach using Sentinel-2 imagery. We also conduct field surveys to determine the actual condition of barchan dunes and compare it to previous morphology data. We only used the slip face height as a parameter of the barchan morphometric. The result showed that the vegetation coverage changed annually in different parts of the Inner Zone from 2015 until 2019. This vegetation covers controlled by restoration program in 2015 and 2016. The vegetation density on the transport zone more significantly affected the morphology of barchan than vegetation density which grow on the barchan body. Based on field data, mostly barchan dunes (10 barchans) experienced a decrease of slip face height than increased slip face height (4 barchans). All of the decreased barchans located in the middle of the Inner zone. The most decreased slip face height as low as 29.3 meters.

scholarly journals Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Two Stage ◽  
Area Index ◽  
Field Surveys ◽  
Red Edge ◽  
Calibration Approach ◽  
Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

scholarly journals Clasificación espacial del suelo urbano por el valor especulativo del suelo e imágenes MSI satelitales usando K-MEANS, Huancayo, Perú

2021 ◽  
Vol 24 (44) ◽  
pp. 70-83
Author(s):  
Gonzalo Rodolfo Peña-Zamalloa
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Market ◽  
Objective Classification ◽  
Urban Land Market ◽  
The City ◽  
Sentinel 2 ◽  
Urban Territory

The city of Huancayo, like other intermediate cities in Latin America, faces problems of poorly planned land-use changes and a rapid dynamic of the urban land market. The scarce and outdated information on the urban territory impedes the adequate classification of urban areas, limiting the form of its intervention. The purpose of this research was the adoption of unassisted and mixed methods for the spatial classification of urban areas, considering the speculative land value, the proportion of urbanized land, and other geospatial variables. Among the data collection media, Multi-Spectral Imagery (MSI) from the Sentinel-2 satellite, the primary road system, and a sample of direct observation points, were used. The processed data were incorporated into georeferenced maps, to which urban limits and official slopes were added. During data processing, the K-Means algorithm was used, together with other machine learning and assisted judgment methods. As a result, an objective classification of urban areas was obtained, which differs from the existing planning.

scholarly journals Runoff sensitivity to climate and land-use changes: A case study in the Longtan basin, Southwestern China

2020 ◽  
Author(s):  
Guiyan Mo ◽  
Ya Huang ◽  
Qing Yang ◽  
Dayang Wang ◽  
Chongxun Mo
Keyword(s):  
Land Use ◽  
Water Conservation ◽  
Assessment Tool ◽  
Land Use Changes ◽  
Annual Runoff ◽  
Vegetation Coverage ◽  
Average Annual Runoff ◽  
Annual Evaporation ◽  
Water Assessment

Abstract Based on the scenario hypothesis method, this paper applied a Soil and Water Assessment Tool (SWAT) to analyze the sensitivity of runoff to climate and land-use changes in the Longtan basin, China. Results indicated that (1) for every 1 °C increase in temperature, the average annual runoff decreased by 9.9 mm, and the average annual evaporation increased by 9.3 mm. However, for every 10% increase in rainfall, the average annual runoff and evapotranspiration increased by 96.3 mm and 11.53 mm, respectively. Obviously, runoff was more sensitive to the change in rainfall than temperature in the Longtan basin. Meanwhile, (2) forestland could conserve water resources, but its water consumption was larger. Although grassland played a relatively small role in water conservation, it consumed less water. At the same time, increasing the area of forestland and grassland could weaken peak floods, and the water retention function of vegetation could prevent runoff from increasing and decreasing steeply. Therefore, it is worth improving vegetation coverage.

scholarly journals Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

2021 ◽  
Vol 14 (21) ◽  
Author(s):  
Gáspár Albert ◽  
Seif Ammar
Keyword(s):  
Random Forest ◽  
Remotely Sensed ◽  
Geological Mapping ◽  
Vegetation Coverage ◽  
Remotely Sensed Data ◽  
Random Forest Classification ◽  
Radar Images ◽  
Forest Classification ◽  
Geological Map ◽  
Sentinel 2

Abstract Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.

scholarly journals Identification of Tyre and Plastic Waste from Combined Copernicus Sentinel-1 and -2 Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2824
Author(s):  
Robert Page ◽  
Samantha Lavender ◽  
Dean Thomas ◽  
Katie Berry ◽  
Susan Stevens ◽  
...  
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Cost Effective ◽  
Land Cover Classification ◽  
Plastic Waste ◽  
Complex Nature ◽  
Field Surveys ◽  
Plastic Materials ◽  
Optical Imagery ◽  
Sentinel 2

As a result of tightened waste regulation across Europe, reports of waste crime have been on the rise. Significant stockpiles of tyres and plastic materials have been identified as a threat to both human and environmental health, leading to water and livestock contamination, providing substantial fuel for fires, and cultivating a variety of disease vectors. Traditional methods of identifying illegal stockpiles usually involve laborious field surveys, which are unsuitable for national scale management. Remotely-sensed investigations to tackle waste have been less explored due to the spectrally variable and complex nature of tyres and plastics, as well as their similarity to other land covers such as water and shadow. Therefore, the overall objective of this study was to develop an accurate classification method for both tyre and plastic waste to provide a viable platform for repeatable, cost-effective, and large-scale monitoring. An augmented land cover classification is presented that combines Copernicus Sentinel-2 optical imagery with thematic indices and Copernicus Sentinel-1 microwave data, and two random forests land cover classification algorithms were trained for the detection of tyres and plastics across Scotland. Testing of the method identified 211 confirmed tyre and plastic stockpiles, with overall classification accuracies calculated above 90%.

Spatial distribution pattern of dryland plants: Turing pattern in water limited conditions

2019 ◽  
Vol 28 (03) ◽  
pp. 48-55
Author(s):  
Keita Shima ◽  
Buho Hoshino ◽  
Ying Tian ◽  
Zoljargal E ◽  
Saixialt Bao ◽  
...  
Keyword(s):  
Vegetation Coverage ◽  
Gobi Desert ◽  
Landsat 8 ◽  
Turing Pattern ◽  
Desert Region ◽  
True Value ◽  
Ground Measurement ◽  
Dry Soil ◽  
Very High ◽  
Sentinel 2

The plants in the Gobi desert region are sparsely distributed on a vast bare field, it is extremely difficult to accurately observe from the satellite. For the reason, the reflectance of dry soil is very high and the reflectance of slightly distributed plants is eliminated by soil reflection. As a result, the pixel’s NDVI value of desert plants shows a smaller value than the ground measurement. In this study, we succeeded to analyze Turing pattern of vegetation abundance using the method of spectral un-mixing for satellite data of the Gobi plants. It is shown that the fraction of the vegetation endmember after pixel un-mixing has a remarkably high correlation (R2=0.51 in Landsat 8 and R2=0.41 in Sentinel 2) with the ground true value of vegetation coverage. Гандуу бүсийн ургамлын орон зайн тархалт: тюрингийн хэлбэршил ус гачиг нөхцөлд илрэх нь Говь цөлийн ургамалшил нь асар уудамгазар дээр алаг цоог мөртлөө тачир сийрэг тархдаг тул хиймэл дагуулаас нарийвчлан ажиглахад хэцүү байдаг. Гол шалтгаан ньзайнаас тандсанцацраг туяа нь хуурай нүцгэн хөрсний маш өндөр ойлтын нөлөөгөөртачир сийрэг ургамлын цацрагийн ойлт сул, шингээлт нь дарагдаж илэрдэггүй байдал юм. Үүний үр дүнд цөлийн ургамлын хйимэл дагуулын зураг дээрх утга нь газрын гадаргуу дээр шууд хийсэн хэмжилтээс бага утгыг харуулж байдаг. Энэхүү судалгаанд бид хиймэл дагуулын мэдээг спектр үл холих аргыг ашиглан боловсруулжговийн ургамалшил Тюрингийн хэлбэршлийн дагуу тархаж байгаа зүйтогтлыг батлав. Спектр үл холих аргаар тогтоосон ургамлан бүрхэвчийн зайнаас тандсан мэдээ нь газрын бодит хэмжилтийн утгатай харьцангуй сайн хамааралтай (Ландсат 8-д R2 = 0.51, Сентинел 2-т R2 = 0.41) байна. Түлхүүр үг: Спектр үл холих арга,Говь цөлийн ургамлын тархац  

scholarly journals Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1666 ◽  
Author(s):  
Yong-Suk Lee ◽  
Sunmin Lee ◽  
Hyung-Sup Jung
Keyword(s):  
Satellite Images ◽  
Vertical Structure ◽  
Storage Capacity ◽  
Water Storage ◽  
Test Accuracy ◽  
Error Matrix ◽  
Field Surveys ◽  
Water Storage Capacity ◽  
Artificial Neural ◽  
Sentinel 2

As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is determined by forest structure. Typically, data on forest vertical structure have been constructed from field surveys that are both costly and time-consuming. In addition, machine learning techniques could be applied to analyze, classify, and predict the uncertainties of internal structures in forest. Therefore, this study aims to map the forest vertical structure for estimating forest water storage capacity from multi-seasonal optical satellite image and topographic data using artificial neural network (ANN) in Gongju-si, South Korea. For this purpose, the 14 input neurons of normalized difference vegetation index (NDVI), two types of normalized difference water index (NDWI), two types of Normalized Difference Red Edge Index (NDre), principal component analysis (PCA) texture, and canopy height average and standard deviation maps were generated from Sentinel-2 optical images obtained in spring and fall season and topographic height maps such as digital terrain models (DTM) and digital surface models (DSM). The training/validation and test datasets for the ANN model were derived from forest vertical structures based on field surveys. Finally, the forest vertical classification map, the result of ANN application, was evaluated by creating an error matrix compared with the field survey results. The result showed an overall test accuracy of ~65.7% based on the number of pixels. The result shows that forest vertical structure in Gong-ju, Korea can be efficiently classified by using multi-seasonal Sentinel-2 satellite images and the ANN approach.

scholarly journals Analysis of landslide potential due to changes of land use/land cover at the Kuranji watershed, Padang using normalized difference built-up index (NDBI)

2021 ◽  
Vol 331 ◽  
pp. 03007
Author(s):  
Andriani Andriani ◽  
Geri Despita Putra ◽  
Salsabila Ramadhani ◽  
Ismael ◽  
Hendri Gusti Putra
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Land Use Changes ◽  
Runoff Coefficient ◽  
Land Use Land Cover ◽  
Vegetation Density ◽  
Geospatial Information ◽  
The Relationship ◽  
The City

The earthquake and tsunami predictions in the city of Padang have caused very rapid land-use changes, especially in the Kuranji watershed, where people tend to seek locations that are safe from tsunamis and liquefaction. Changes in environmental characteristics such as slope geometry conditions, vegetation density, and changes in land use will affect runoff coefficient and rainwater filtration, triggering a potential for landslides. This study aims to analyze the potential for landslides due to changes in land use in the Kuranji Watershed. The identification of land-use change is carried out using a remote sensing approach, namely the Normalized Difference Built-Up Index (NDBI). Landslide potential is determined based on the relationship between land use and runoff coefficient from 2007 to 2019. The results showed there had been an increase in the built-up area in the Kuranji watershed from 1602.212 ha (2007) to 2897.513 ha (2019). In contrast, the vegetation area has decreased. An increase in the runoff coefficient of 3.9% from 2007 to 2019. The final results of this study are thematic geospatial information obtained in the form of the relationship between changes in land use and the potential for landslides that occurred in the Kuranji watershed during the period 2007 to 2019.

scholarly journals ECOLOGICAL CARRYING CAPACITY OF LAND USE CHANGES IN DA’AN CITY

2018 ◽  
Vol XLII-3 ◽  
pp. 1707-1712
Author(s):  
H. Wang ◽  
J. Zhang ◽  
B. Li
Keyword(s):  
Land Use ◽  
Ecological Risk ◽  
Carrying Capacity ◽  
Land Use Changes ◽  
Ecological Environment ◽  
Vegetation Coverage ◽  
Risk Level ◽  
Comprehensive Management ◽  
Ecological Carrying Capacity ◽  
Gis And Rs

Based on GIS and RS technology, this paper analyzed the land use change in Da'an city from 1995 to 2010. land-use ecological evaluation index was constructed to evaluate the land-use ecological risk of Da 'an city dynamically, and the land-use ecological risk level map was made, and then the distribution and change of the land-use ecological carrying capacity pattern of Da'an city were analyzed qualitatively. According to the evaluation results of ecological carrying capacity, the ecological environment of Da'an city has deteriorated in fifteen years. in 1995, the poor ecological environment area is mainly distributed in the northeast area of Da'an city, and the area is small, while the area of the central and southern areas is large; In 2010, the western region also appeared environmental degradation, the northeast environment deterioration is serious, the dominant area is reduced, and a small amount of deterioration in the central and southern regions. According to the study of this paper, in the future, we should strengthen the comprehensive management of this part of the area, strengthen vegetation coverage, reduce soil erosion, ensure the effective improvement of ecological environment.

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