scholarly journals Normalized Dryness Built-up Index (NDBI) to Detect Settlement Change In Buleleng Sub-District

2021 ◽  
Vol 20 (1) ◽  
pp. 46
Author(s):  
Muhammad Rahman ◽  
A Sediyo Adi Nugraha
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Settlement Development ◽  
Landsat 7 ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

This research aims to find out the development of settlements that occur over the next 20 years. Monitoring the development of settlements is carried out by remote sensing methods using Landsat 7 ETM+ imagery and Landsat 8 OLI imagery. Landsat 7 ETM+ used in 2000, and Landsat 8 OLI used in 2019. The algorithm is used to identify settlement development using the Normalized Dryness Built-up Index (NDBI). This algorithm uses two bands, such as Near-infrared and shortwave infrared, to calculate. The results showed that the growth of settlements occurred very significant because, in 2000, the number of settlements amounted to 628.2 hectares and in 2019 amounted to 1891.8 hectares. The increase in settlements occurred throughout the region in the Buleleng sub-district. Therefore, it can be concluded that NDBI can be used to monitor the development of settlements and the increase in settlements occurring as much as 28 % over 20 years.

scholarly journals Spektroskopi Reflektansi Sampel Tanah dan Batuan yang Mengandung Mineral Pembawa Unsur Tanah Jarang dan Radioaktif

EKSPLORIUM ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 89
Author(s):  
Arie Naftali Hawu Hede ◽  
Muhammad Anugrah Firdaus ◽  
Yogi La Ode Prianata ◽  
Mohamad Nur Heriawan ◽  
Syafrizal Syafrizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Optical Sensors ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Infrared Range ◽  
Basic Principles ◽  
Spectral Signatures ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

ABSTRAKSpektroskopi reflektansi merupakan salah satu metode nondestruktif untuk identifikasi mineral dan sebagai dasar dalam analisis pengindraan jauh (indraja) sensor optik. Penelitian ini bertujuan melakukan kajian penerapan spektroskopi reflektansi pada panjang gelombang 350–2.500 nm untuk sampel tanah dan batuan pembawa unsur tanah jarang (rare earth element-REE) dan radioaktif. Sampel diambil dari beberapa lokasi di Bangka Selatan dan Mamuju yang sebelumnya telah diidentifikasi memiliki potensi REE dan unsur radioaktif. Kurva reflektansi hasil analisis sampel dari Bangka Selatan menunjukan adanya kenampakan absorpsi yang menjadi karakteristik untuk kehadiran REE, dalam bentuk mineral monasit, zirkon, dan xenotime khususnya pada sampel yang berasal dari material tailing dan konsentrat bijih timah. Panjang gelombang yang menjadi kunci khususnya berada pada rentang visible-near infrared (VNIR; 400–1.300 nm). Sedangkan untuk sampel yang berasal dari Mamuju, yang merupakan daerah prospeksi mineral radioaktif, karakteristik spektral memperlihatkan beberapa panjang gelombang kunci terutama pada rentang shortwave infrared (1.300–2.500 nm). Hasil interpretasi menunjukkan mineral mayor berupa mineral lempung, sulfat, spesies NH4, dan mineral yang mengandung Al-OH lainnya, sedangkan untuk beberapa sampel pada panjang gelombang VNIR diidentifikasi mengandung mineral besi oksida/hidroksida. Hasil penelitian ini diharapkan dapat berguna untuk pemetaan eksplorasi REE dan radioaktif dengan menggunakan metode indraja.ABSTRACTReflectance spectroscopy is one of the nondestructive methods of mineral identification and is one of the basic principles in the remote sensing analysis using optical sensors. This research aimed at applying reflectance spectroscopy at 350–2,500 nm wavelength range for samples containing rare earth elements (REE) and radioactive minerals. Samples were taken from several locations in South Bangka and Mamuju that had previously been identified as potential location of REE and radioactive-bearing minerals. Reflectance data shows that there are absorption characteristics for REE-bearing minerals; monazite, zircon, and xenotime minerals especially from tailings and tin ore concentrate for the samples from South Bangka. The key wavelengths are specifically in the visible-near infrared range (VNIR; 400–1300 nm). For the samples from Mamuju, which is known as radioactive mineral prospecting areas, spectral characteristics provide information that there are spectral signatures in the shortwave infrared range (1,300–2,500 nm). The results of major mineral interpretations include clay minerals, sulfates, NH4 species, and other minerals containing Al-OH. However, some samples at the VNIR wavelength identified as iron oxide/hydroxide minerals. It is hoped that these results can be useful for REE and radioactive exploration mapping using remote sensing methods.

scholarly journals A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya

2015 ◽  
Vol XL-7/W3 ◽  
pp. 897-903 ◽  
Author(s):  
M. W. Mwaniki ◽  
M. S. Moeller ◽  
G. Schellmann
Keyword(s):  
Remote Sensing ◽  
Knowledge Base ◽  
Remote Sensing Data ◽  
Factor Loading ◽  
Geological Mapping ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Loading Analysis

Availability of multispectral remote sensing data cheaply and its higher spectral resolution compared to remote sensing data with higher spatial resolution has proved valuable for geological mapping exploitation and mineral mapping. This has benefited applications such as landslide quantification, fault pattern mapping, rock and lineament mapping especially with advanced remote sensing techniques and the use of short wave infrared bands. While Landsat and Aster data have been used to map geology in arid areas and band ratios suiting the application established, mapping in geology in highland regions has been challenging due to vegetation land cover. The aim of this study was to map geology and investigate bands suited for geological applications in a study area containing semi arid and highland characteristics. Therefore, Landsat 7 (ETM+, 2000) and Landsat 8 (OLI, 2014) were compared in determining suitable bands suited for geological mapping in the study area. The methodology consist performing principal component and factor loading analysis, IHS transformation and decorrelation stretch of the FCC with the highest contrast, band rationing and examining FCC with highest contrast, and then performing knowledge base classification. PCA factor loading analysis with emphasis on geological information showed band combination (5, 7, 3) for Landsat 7 and (6, 7, 4) for Landsat 8 had the highest contrast and more contrast was enhanced by performing decorrelation stretch. Band ratio combination (3/2, 5/1, 7/3) for Landsat 7 and (4/3, 6/2, 7/4) for Landsat 8 had more contrast on geologic information and formed the input data in knowledge base classification. Lineament visualisazion was achieved by performing IHS transformation of FCC with highest contrast and its saturation band combined as follows: Landsat 7 (IC1, PC2, saturation band), Landsat 8 (IC1, PC4, saturation band). The results were compared against existing geology maps and were superior and could be used to update the existing maps.

scholarly journals Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

Environments ◽  
2019 ◽  
Vol 6 (7) ◽  
pp. 85 ◽  
Author(s):  
Cesar I. Alvarez-Mendoza ◽  
Ana Claudia Teodoro ◽  
Nelly Torres ◽  
Valeria Vivanco
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Pm10 Concentration ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Terra Modis

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

scholarly journals Aplication of Landsat 8 Oli for the total suspended solid (TSS) mapping in the Tri An reservoir, Dong Nai province

2020 ◽  
Vol 4 (1) ◽  
pp. first
Author(s):  
Pham Thanh Luu ◽  
Nguyen Thi My Le ◽  
Trinh Hong Phuong ◽  
Tran Thi Hoang Yen ◽  
Tran Thanh Thai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Surface Water ◽  
Near Infrared ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Near Infrared Reflectance

Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies. Total suspended solid (TSS) is an important water quality parameter and a surrogate for the water clarity. It can be used as the indicator of sediment in the reservoir, which usually consists of silt, fine sand and microorganisms. This study aimed to utilize the remote sensing technology, in particular Landsat 8 Operational Land Imager (Landsat 8 OLI), to determine the amount of TSS concentration as well as the spatial distribution of TSS concentration in the surface water of the Tri An reservoir. The relationship between field TSS data collected in March, 2020 and the reflectance values of the the Landsat 8 Oli images was investigated. Results showed that there was a strong linear relationshiop between TSS concentration and the reflectance of the red and near infrared reflectance bands from the Landsat 8 Oli (r ranged from 0.58–0.93), in which the ratio of the red band produced the best correlation with the TSS (r = 0.93, with a standard error of 0.6–1.39 mg/L). Based on the linear regression equation, the TSS concentration calculated from the red reflectance values was used for mapping the spatial distribution of TSS in the surface water of the Tri An reservoir. Our results confirmed the accuracy and potential of using the single band from Landsat 8 OLI for mapping the spatial distribution of TSS in the Tri An reservoir.

A Google Earth Engine application for mapping volcanic thermal anomalies at a global scale by means of Sentinel 2 MSI and Landsat 8 OLI data

2020 ◽  
Author(s):  
Nicola Genzano ◽  
Francesco Marchese ◽  
Alfredo Falconieri ◽  
Giuseppe Mazzeo ◽  
Nicola Pergola
Keyword(s):  
Near Infrared ◽  
Global Scale ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Infrared Radiances ◽  
Shortwave Infrared ◽  
Sentinel 2

<p>NHI (Normalized Hotspot Indices) is an original multichannel algorithm recently developed for mapping volcanic thermal anomalies in daylight conditions by means of infrared Sentinel 2 MSI and Landsat 8 OLI data. The algorithm, which uses two normalized indices analyzing SWIR (Shortwave Infrared) and NIR (Near Infrared) radiances, was tested with success in different volcanic areas, assessing results by means of independent ground and satellite-based observations.</p><p>Here we present and describe the NHI-based tool, which exploits the high computation capabilities of Google Earth Engine to perform the rapid mapping of hot volcanic features at a global scale. The tool allows the users to retrieve information also about changes of thermal volcanic activity, giving the opportunity of performing time series analysis of hotspot pixel number and total SWIR radiance. Advantages of using the NHI tool as a complement to current satellite-based volcanoes monitoring systems are then analysed and discussed, such as its future upgrades.</p>

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals FOREST DISTURBANCE ANALYSIS WITH LANDSAT-8 OLI DATA RELATED TO A PARAMETRIC WIND FIELD: A CASE STUDY FOR TYPHOON RAMMASUN (201409)

2018 ◽  
Vol XLII-3 ◽  
pp. 1629-1633
Author(s):  
C. Tan ◽  
W. Fang
Keyword(s):  
Remote Sensing ◽  
Wind Field ◽  
Forest Disturbance ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Dependence Relation ◽  
Wind Field Model ◽  
Wind Speeds ◽  
Sensing Technology ◽  
And Function

Forest disturbance induced by tropical cyclone often has significant and profound effects on the structure and function of forest ecosystem. Detection and analysis of post-disaster forest disturbance based on remote sensing technology has been widely applied. At present, it is necessary to conduct further quantitative analysis of the magnitude of forest disturbance with the intensity of typhoon. In this study, taking the case of super typhoon Rammasun (201409), we analysed the sensitivity of four common used remote sensing indices and explored the relationship between remote sensing index and corresponding wind speeds based on pre-and post- Landsat-8 OLI (Operational Land Imager) images and a parameterized wind field model. The results proved that NBR is the most sensitive index for the detection of forest disturbance induced by Typhoon Rammasun and the variation of NBR has a significant linear dependence relation with the simulated 3-second gust wind speed.

scholarly journals MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 370 ◽  
Author(s):  
Luís Flávio Pereira ◽  
Cecilia Fátima Carlos Ferreira ◽  
Ricardo Morato Fiúza Guimarães
Keyword(s):  
Remote Sensing ◽  
Atlantic Forest ◽  
Quality Index ◽  
Minas Gerais ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Mata Atlantica ◽  
Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

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