Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines

Abstract Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.

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Thermal and multispectral images from Unmanned Aerial Vehicles (UAVs) for water presence detection in temporary streams: first results

10.5194/egusphere-egu21-9546 ◽

2021 ◽

Author(s):

Massimo Micieli ◽

Gianluca Botter ◽

Giuseppe Mendicino ◽

Alfonso Senatore

Keyword(s):

Unmanned Aerial Vehicles ◽

Near Infrared ◽

Infrared Band ◽

Temporary Streams ◽

Water Index ◽

Long Wavelength ◽

Research Activities ◽

Aerial Vehicles ◽

Normalized Difference Water Index ◽

First Results

<p>UAVs (Unmanned Aerial Vehicles) are increasingly used for monitoring river networks with a broad range of purposes. In this contribution, we focus on the use of multispectral sensors, either in the thermal infrared band LWIR (Long-wavelength infrared, 8-15 &#181;m) or in the infrared band NIR (Near-infrared, 0.75-1.4 &#181;m) to map network dynamics in temporary streams. Specifically, we discuss the first results of a set of surveys carried out in 2020 within a small river catchment located in northern Calabria (southern Italy), as part of the research activities of the ERC-funded DyNET project. Preliminary, a rigorous methodology was identified to perform on-site surveys and to process and analyse the acquired images. Experimental results show that the combined use of LWIR and NIR sensors is a suitable solution for detecting water presence in channels characterized by different hydraulic and morphologic conditions. LWIR sensors alone allow one to discriminate water presence only when the thermal contrast with the surrounding environment is high. On the other hand, NIR sensors permit to detect the presence of water in most of the analyzed settings through the estimate of the Normalized Difference Water Index (NDWI). However, NIR sensors can be misled in case of shallow water depth, due to the NIR radiation emitted by the riverbed merging with that of the water. Overall, the study demonstrates that a combined LWIR/NIR approach allows addressing a broader range of conditions. Moreover, the information provided can be further enhanced by combining it with geomorphologic information and basic hydraulic concepts.</p>

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Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

Remote Sensing ◽

10.3390/rs10081248 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1248 ◽

Cited By ~ 9

Author(s):

Hua Sun ◽

Qing Wang ◽

Guangxing Wang ◽

Hui Lin ◽

Peng Luo ◽

...

Keyword(s):

Vegetation Cover ◽

Near Infrared ◽

Good Alternative ◽

Landsat 8 ◽

Arid Areas ◽

Landsat Images ◽

Infrared Band ◽

K Value ◽

Mean Square Errors ◽

Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

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Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2017001100013 ◽

2017 ◽

Vol 52 (11) ◽

pp. 1072-1079 ◽

Cited By ~ 4

Author(s):

Elisiane Alba ◽

Eliziane Pivotto Mello ◽

Juliana Marchesan ◽

Emanuel Araújo Silva ◽

Juliana Tramontina ◽

...

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Pinus Elliottii ◽

Growth Stages ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Infrared Band ◽

Near Infrared Reflectance ◽

Planning And Management ◽

Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

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A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2021.62(1).01 ◽

2021 ◽

Vol 62 (1) ◽

pp. 1-9

Author(s):

Hung Le Trinh ◽

Ha Thu Thi Le ◽

Loc Duc Le ◽

Long Thanh Nguyen ◽

Keyword(s):

Land Cover ◽

Satellite Imagery ◽

Near Infrared ◽

Urban Land ◽

Urban Land Use ◽

Landsat 8 ◽

Infrared Band ◽

Bare Land ◽

Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

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The urban areas classification methodology according to multi-zone images of Sentinel 2 and Landsat 8 (on the example of the city of Thanh Hoa, Vietnam)

Geodesy and Cartography ◽

10.22389/0016-7126-2020-956-2-40-49 ◽

2020 ◽

Vol 956 (2) ◽

pp. 40-49

Author(s):

Le Hung Trinh ◽

Dinh Sinh Mai ◽

V.R. Zablotskii

Keyword(s):

Land Cover ◽

Urban Areas ◽

Satellite Images ◽

Near Infrared ◽

Landsat 8 ◽

Land Use Classification ◽

Infrared Band ◽

The Status ◽

The Impact ◽

Sentinel 2

In recent years, land cover changes very quickly in urban areas due to the impact of population growth and socio-economic development. The authors present the method of land cover/land use classification based on the combination of Sentinel 2 and Landsat 8 multi-resolution satellite images. A middle infrared band (band 11), a near infrared (band 8) of Sentinel 2 image and a thermal infrared one (band 10) of Landsat 8 image were used to calculate EBBI (Enhanced Built-up and Barreness Index). The EBBI index and Sentinel 2 spectral bands with spatial resolution 10 m (band 2, 3, 4, 8) were used to classify the land cover. The obtained results showed that, the method of land cover classification based on combination of Sentinel 2 and Landsat 8 satellite images improves the overall accuracy by about 5 % compared with the one using only Sentinel 2 data. The results obtained at the study can be used for the management, assessment and monitoring the status and dynamics of land cover in urban areas.

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Assessing drought from satellite data to support agricultural production

Science & Technology Development Journal - Science of The Earth & Environment ◽

10.32508/stdjsee.v4i1.510 ◽

2020 ◽

Vol 4 (1) ◽

pp. First

Author(s):

Van Tran Thi ◽

Toi Nguyen Duong Lam ◽

Huynh Phan Thi Diem ◽

Ha Nguyen Ngan ◽

Bao Ha Duong Xuan

Keyword(s):

Surface Temperature ◽

Social Life ◽

Vegetation Index ◽

Near Infrared ◽

Global Climate ◽

Landsat 8 ◽

Agricultural Crop ◽

Infrared Band ◽

Drought Assessment ◽

Optical Satellite Images

Drought is one of the disasters causing the problems to the economy and social life of people, especially where agriculture is the main source of income. The paper presents the results of studying the application of optical satellite images to investigate the drought situation for the southern part of Binh Phuoc province for perennial cropland, the main agricultural crop of the province. The image used is Landsat 8 of the dry season month 2015. The method of drought assessment is based on the relationship of surface temperature, and the Normalization Difference Vegetation Index (NDVI) integrated into the Temperature-Vegetation Dryness Index TVDI. In particular, the NDVI index is determined from the red and near-infrared bands, and the surface temperature is determined from the thermal infrared band of Landsat 8 images. The results show that the whole area of southern Binh Phuoc has drought area accounting for 54.9% of the total area, of which the majority is mild drought level 38.3%, high and serious level is 16.7%. About the area of perennial land has drought area accounted for 33.76% of the total area, of which Dong Xoai town has the highest percentage of drought-affected areas compare to other districts. The results of the study aimed to identify drought areas with different levels so that managers can promptly take measures to protect agricultural crops and to ensure people's livelihoods in the global climate change trend seriously affecting the localities today.

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Influence of aerosols and thin cirrus clouds on the GOSAT-observed CO<sub>2</sub>: a case study over Tsukuba

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-29883-2011 ◽

2011 ◽

Vol 11 (11) ◽

pp. 29883-29914 ◽

Cited By ~ 2

Author(s):

O. Uchino ◽

N. Kikuchi ◽

T. Sakai ◽

I. Morino ◽

Y. Yoshida ◽

...

Keyword(s):

Near Infrared ◽

Cirrus Clouds ◽

Total Carbon ◽

Observation Data ◽

Vertical Profiles ◽

Lidar System ◽

Infrared Band ◽

The Difference ◽

Lidar Observations

Abstract. Lidar observations of vertical profiles of aerosols and thin cirrus clouds were made at Tsukuba (36.1° N, 140.1° E), Japan, to investigate the influence of aerosols and thin cirrus clouds on the column-averaged dry-air mole fraction of carbon dioxide (XCO2) retrieved from observation data of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer, measured in the Short-Wavelength InfraRed band (TANSO-FTS SWIR), onboard the Greenhouse gases Observing SATellite (GOSAT). The lidar system measured the backscattering ratio, depolarization ratio, and/or the wavelength exponent of atmospheric particles. The lidar observations and ground-based high-resolution FTS measurements at the Tsukuba Total Carbon Column Observing Network (Tsukuba TCCON) site were recorded simultaneously during passages of GOSAT over Tsukuba. GOSAT SWIR XCO2 data (version 01.xx) released in August 2010 were compared with the lidar and Tsukuba TCCON data. High-altitude aerosols and thin cirrus clouds had a large impact on the GOSAT SWIR XCO2 results. By taking into account the observed aerosol/cirrus vertical profiles and using a more adequate solar irradiance database in the GOSAT SWIR retrieval, the difference between the GOSAT SWIR XCO2 data and the Tsukuba TCCON data was greatly reduced.

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The Simple Method to Assess Land Quality of Paddy Field Using Spectral, Soil pH and Statistical Regression Technique (Case Study of Paddy Field in Majalaya Subdistrict, Bandung Region)

KnE Life Sciences ◽

10.18502/kls.v2i6.1040 ◽

2017 ◽

Vol 2 (6) ◽

pp. 194

Author(s):

Mochamad Firman Ghazali ◽

Agung Budi Harto ◽

Ketut Wikantika

Keyword(s):

Regression Model ◽

Soil Ph ◽

Paddy Field ◽

Near Infrared ◽

Landsat 8 ◽

Statistical Regression ◽

Simple Method ◽

Land Quality

Assessing land quality has important use in understanding the capability of soil in producing food. The area of paddy fields in Majalaya Subdistrict is located around the industrial zone and this situation is urgent to understand the land quality of paddy field due to the influence effect of industrial waste to its growth. A combination of regression model and Landsat 8 image to estimate soil pH distribution is used to predict the land quality. The result of this study is shown that the regression model of red and near infrared (NIR) band combination is used to predict soil pH has been successfully given the smallest error (RMSe) as the soil pH accuracy is 1.18 and related to the land quality assessment based on predicted soil pH is shown that in the whole area of paddy field has the acid situation of soil pH.Keywords: Spectral, Soil pH; Regression, Land Quality; Land Suitability

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Influence of aerosols and thin cirrus clouds on the GOSAT-observed CO2: a case study over Tsukuba

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-3393-2012 ◽

2012 ◽

Vol 12 (7) ◽

pp. 3393-3404 ◽

Cited By ~ 30

Author(s):

O. Uchino ◽

N. Kikuchi ◽

T. Sakai ◽

I. Morino ◽

Y. Yoshida ◽

...

Keyword(s):

Near Infrared ◽

Cirrus Clouds ◽

Total Carbon ◽

Observation Data ◽

Vertical Profiles ◽

Lidar System ◽

Infrared Band ◽

The Difference ◽

Lidar Observations

Abstract. Lidar observations of vertical profiles of aerosols and thin cirrus clouds were made at Tsukuba (36.05° N, 140.12° E), Japan, to investigate the influence of aerosols and thin cirrus clouds on the column-averaged dry-air mole fraction of carbon dioxide (XCO2) retrieved from observation data of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer, measured in the Short-Wavelength InfraRed band (TANSO-FTS SWIR), onboard the Greenhouse gases Observing SATellite (GOSAT). The lidar system measured the backscattering ratio, depolarization ratio, and/or the wavelength exponent of atmospheric particles. The lidar observations and ground-based high-resolution FTS measurements at the Tsukuba Total Carbon Column Observing Network (Tsukuba TCCON) site were recorded simultaneously during passages of GOSAT over Tsukuba. GOSAT SWIR XCO2 data (Version 01.xx) released in August 2010 were compared with the lidar and Tsukuba TCCON data. High-altitude aerosols and thin cirrus clouds had a large impact on the GOSAT SWIR XCO2 results. By taking into account the observed aerosol/cirrus vertical profiles and using a more adequate solar irradiance database in the GOSAT SWIR retrieval, the difference between the GOSAT SWIR XCO2 data and the Tsukuba TCCON data was reduced. The 3-band retrieval approach where the aerosol and cirrus profiles were retrieved gave us the best results and the retrieved XCO2 data followed the seasonal cycle of ~8 ppm observed at Tsukuba TCCON site.

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