scholarly journals Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping

2018 ◽  
Vol 50 (2) ◽  
pp. 184 ◽  
Author(s):  
Zylshal Zylshal ◽  
Rachmad Wirawan ◽  
Dony Kushardono
Keyword(s):  
Near Infrared ◽  
Agricultural Land ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Radiometric Correction ◽  
Multispectral Data ◽  
Blurring Effect

LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract  10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC. 

scholarly journals Modelagem de variáveis dendrométricas em povoamentos florestais utilizando imagens de média resolução espacial (Modeling of biophysical variables in forest stands using medium spatial resolution images)

2016 ◽  
Vol 9 (6) ◽  
pp. 1969
Author(s):  
Elisiane Alba ◽  
Emanuel Araújo Silva ◽  
Juliana Marchesan ◽  
Letícia Pedrali ◽  
Rudiney Soares Pereira ◽  
...  
Keyword(s):  
Population Density ◽  
Near Infrared ◽  
Tree Density ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Forest Stands ◽  
Infrared Band ◽  
Radiometric Correction ◽  
Vegetation Indexes

Objetivou-se avaliar as imagens Landsat 8/OLI na obtenção de estimativas do volume florestal e densidade populacional de plantios de E. grandis. Para tanto, utilizaram-se 42 unidades amostrais de povoamentos com 18 e 25 anos, mensurando-se os parâmetros dendrométricos Diâmetro à Altura do Peito (DAP), altura total e densidade de árvores. Foi realizada a correção radiométrica da imagem Landsat 8/OLI, obtendo a reflectância de superfície das bandas e índices de vegetação, a qual foi relacionada com as variáveis florestais, ajustando equações de estimativas por meio do método forward. Para os plantios com 18 anos, a equação ajustada explicou 87% da variabilidade do volume com as variáveis SAVI e NDVI presentes no modelo. A densidade populacional foi explicada pelo SR e DVI (R²=0,56). Aos 25 anos, o modelo contendo a banda do infravermelho próximo (B5) e o índice SR respondeu a 92% da variação total do volume florestal.  Nesta idade, a densidade populacional não apresentou correlação positiva. As propriedades espectrais da imagem apresentaram sensibilidade às variáveis dendrométricas, permitindo o monitoramento do desenvolvimento dos povoamentos florestais, justificando a aplicabilidade deste método.    A B S T R A C T This study aims at evaluate Landsat 8/OLI images in obtaining of estimates of the volume and tree density in plantations E. grandis. Therefore, was used 42 sampling unities of stands with 18 e 25 years, measurand the dendrometric parameters Diameter at Breast Height, total height and tree density. Was performed the radiometric correction of the Landsat 8/OLI image, obtaining the surface reflectance of the bands and vegetation indexes, which was related with variables forestry, adjusting equation of estimates through of the method forward. For plantations with 18 years, adjusting equation explained 87% of the volume variability with the variables SAVI and NDVI present in the model. Already the population density was explained by indexes SR and DVI (R²= 0.56). At 25 years, the model containg the near infrared band (B5) and the SR index responded to 92% of the total variation of the volume forestry. This age, the population density showed no positive correlation. The spectral properties of the image demonstrated sensitivity to variables dendrometric, allowing the monitoring of the development of forest stands, justifying the applicability of this method. Keywords: index vegetation, spectral reflectance, wood volume.   

scholarly journals Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

2018 ◽  
Vol 10 (11) ◽  
pp. 1687 ◽  
Author(s):  
Joan-Cristian Padró ◽  
Francisco-Javier Muñoz ◽  
Luis Ávila ◽  
Lluís Pesquer ◽  
Xavier Pons
Keyword(s):  
Near Infrared ◽  
Field Measurements ◽  
Systematic Sampling ◽  
Landsat 8 ◽  
Radiometric Correction ◽  
Multispectral Data ◽  
Central Hypothesis ◽  
Sentinel 2A ◽  
Sentinel 2

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

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 MAPPING OF THE SEAGRASS COVER ALONG THE MEDITERRANEAN COAST OF TURKEY USING LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1103-1105 ◽  
Author(s):  
T. Bakirman ◽  
M. U. Gumusay ◽  
I. Tuney
Keyword(s):  
Accuracy Assessment ◽  
Mediterranean Coast ◽  
Environmental Data ◽  
Benthic Habitat ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Carbon Dioxide Absorption ◽  
Landsat Images ◽  
Marine Animals ◽  
Seagrass Cover

Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5&ndash;10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.

scholarly journals An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs

2020 ◽  
Vol 12 (9) ◽  
pp. 1514 ◽  
Author(s):  
Carmen Cillero Castro ◽  
Jose Antonio Domínguez Gómez ◽  
Jordi Delgado Martín ◽  
Boris Alejandro Hinojo Sánchez ◽  
Jose Luis Cereijo Arango ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Water Reservoir ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Chl A ◽  
Multispectral Data ◽  
Small Reservoirs ◽  
In Situ Data ◽  
Drinking Water Reservoir

A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The goal is to evaluate the performance of a multi-platform approach for the trophic state monitoring with images obtained with MultiSpectral Sensors on board satellites Sentinel 2 (S2A and S2B), Landsat 8 (L8) and UAV. We assessed the performance of three different sensors (MultiSpectral Instrument (MSI), Operational Land Imager (OLI) and Rededge Micasense) for retrieving the pigment chlorophyll-a (chl-a), as a quantitative descriptor of phytoplankton biomass and trophic level. The study was conducted in a waterbody affected by cyanobacterial blooms, one of the most important eutrophication-derived risks for human health. Different empirical models and band indices were evaluated. Spectral band combinations using red and near-infrared (NIR) bands were the most suitable for retrieving chl-a concentration (especially 2 band algorithm (2BDA), the Surface Algal Bloom Index (SABI) and 3 band algorithm (3BDA)) even though blue and green bands were useful to classify UAV images into two chl-a ranges. The results show a moderately good agreement among the three sensors at different spatial resolutions (10 m., 30 m. and 8 cm.), indicating a high potential for the development of a multi-platform and multi-sensor approach for the eutrophication monitoring of small reservoirs.

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

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
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.

Determination of Drought Threshold for Agricultural Land-use using Satellite Image Analysis Techniques

2020 ◽  
Author(s):  
Jieun Kim ◽  
Jaehyung Yu ◽  
Sang Kee Seo ◽  
Jin-Hee Baek ◽  
Byung Chil Jeon
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Near Infrared ◽  
Agricultural Land ◽  
Irrigation Management ◽  
Satellite Image ◽  
Agricultural Drought ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Eastern Area

&lt;p&gt;The climate change causes major problems in natural disasters such as storms and droughts and has significant impacts on agricultural activities. Especially, global warming changed crops cultivated causing changes in agricultural land-use, and droughts along with land-use change accompanied serious problems in irrigation management. Moreover, it is very problematic to detect drought impacted areas with field survey and it burdens irrigation management. In South Korea, drought in 2012 occurred in western area while 2015 drought occurred in eastern area. The drought cycle in Korea is irregular but the drought frequency has shown an increasing pattern. Remote sensing approaches has been used as a solution to detect drought areas in agricultural land-use and many approaches has been introduced for drought monitoring. This study introduces remote sensing approaches to detect agricultural drought by calculation of local threshold associated with agricultural land-use. We used Landsat-8 satellite images for drought and non-drought years, and Vegetation Health Index(VHI) was calculated using red, near-infrared, and thermal-infrared bands. The comparative analysis of VHI values for the same agricultural land-use between drought year and non-drought year derived the threshold values for each type of land-use. The results showed very effective detection of drought impacted areas showing distinctive differences in VHI value distributions between drought and non-drought years.&lt;/p&gt;

scholarly journals Spatial Monitoring of Urban Development Direction using Landsat 7 ETM+ and Landsat 8 OLI/TIRS in Balikpapan City, Indonesia

2020 ◽  
Vol 9 (4) ◽  
pp. 2703-2711
Keyword(s):  
Urban Development ◽  
Urban Areas ◽  
Control Function ◽  
Temporal Analysis ◽  
Bare Soil ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Landsat 8 Oli ◽  
Density Class ◽  
Landsat 7

The development of urban areas in the city of Balikpapan increases over time and is characterized by increasing population. The growth and development of urban areas needs to be monitored so that the control function on area spatial can be implemented. This research aims to determine the direction of urban areas and measure the density of the built-up as a leading indicator of the development of urban areas in Balikpapan. The method used in this study is the multispasio-temporal analysis of remote sensing data of Landsat 7 ETM+ and Landsat 8 OLI/TIRS which contain a combination of spectral transformation, classification supervised Maximum Likelihood, accuracy assessment and statistical analysis. The results showed the trend of urban development from 2001 to 2019 towards east and northeast with the highest built-up density located in the sub-district of Balikpapan Tengah by 82.07% and followed by the sub-district of Balikpapan Kota by 76.94%. The largest land conversion took place on the bare soil with low vegetation density class to be vegetation with the converted area of 7095.91 ha or approximately 14.10% followed by the bare soil with low vegetation density class to be built-up with the converted area of 5826.86 ha or about 11.58% of the total area of Balikpapan city during the period from 2001 to 2019. The accuracy of urban development map in 2001 reaches 92.39 % and the year 2019 reaches 95.69 %, while the accuracy of land cover map in 2001 reaches 85.57% and the year 2019 reaches 87.28 %.

scholarly journals Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kim-Anh Nguyen ◽  
Yuei-An Liou ◽  
Ha-Phuong Tran ◽  
Phi-Phung Hoang ◽  
Thanh-Hung Nguyen
Keyword(s):  
Soil Salinity ◽  
Vegetation Index ◽  
Near Infrared ◽  
Mekong Delta ◽  
The Other ◽  
Landsat 8 ◽  
Salinity Intrusion ◽  
Landsat 8 Oli ◽  
Environmental Indices ◽  
Salinity Index

AbstractSalinity intrusion is a pressing issue in the coastal areas worldwide. It affects the natural environment and causes massive economic loss due to its impacts on the agricultural productivity and food safety. Here, we assessed the salinity intrusion in the Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI image was utilized to derive indices for soil salinity estimate including the single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Statistical analysis between the electrical conductivity (EC1:5, dS/m) and the environmental indices derived from Landsat 8 OLI image was performed. Results indicated that spectral values of near-infrared (NIR) band and VSSI were better correlated with EC1:5 (r2 = 0.8 and r2 = 0.7, respectively) than the other indices. Comparative results show that soil salinity derived from Landsat 8 was consistent with in situ data with coefficient of determination, R2 = 0.89 and RMSE = 0.96 dS/m for NIR band and R2 = 0.77 and RMSE = 1.27 dS/m for VSSI index. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by mapping the soil salinity contamination for better selection of accomodating crop types to reduce economical loss in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be potentially useful as a fast-approach to detect the soil salinity in the other regions with low cost and considerable accuracy.

scholarly journals DETECTING THE SPATIAL DISTRIBUTION OF SETTLEMENTS ON VOLCANIC REGION USING IMAGE LANDSAT-8 OLI IMAGERY

2017 ◽  
Vol 11 (1) ◽  
pp. 63
Author(s):  
. Suwarsono ◽  
M. Rokhis Khomarudin
Keyword(s):  
Spatial Distribution ◽  
Volcanic Eruption ◽  
Actual Condition ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Radiometric Correction ◽  
Volcanic Region ◽  
Prone Area ◽  
Sufficient Degree ◽  
Time Information

Geologically, Indonesia region is on track ring of fire, brings the consequence that the danger of volcanic eruption could occur at any time. Information sites where the settlement is located in the affected areas on emergency response process is needed in quick time. The availability of up to date data is important because it illustrates the actual condition of the region. Active volcanic landforms ranging from the crater to footslope in general is prone area to volcanic eruption, either by the threat of lava flows, pyroclastic falls, or lahars. This study aims to detect the spatial distribution of the settlement on volcanic region using Landsat-8 OLI. Parameters used for the detection of settlements is Normalized Difference Build-up Index (NDBI). Research methods include radiometric correction, delineation of the boundaries of volcanic landforms, NDBI value extraction, extraction of settlement areas, as well as the accuracy assesment.  Study area  is  Sinabung Volcano region located in the province of North Sumatera. Recently, the volcano experienced a devastating and catastrophic eruption. The results showed that the spatial distribution of settlements on volcanic landforms can be detected quickly from Landsat-8 OLI based on NDBI parameters with a sufficient degree of accuracy.

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