scholarly journals ANALISIS PERUBAHAN CADANGAN KARBON DI KAWASAN GUNUNG PADANG KOTA PADANG

JURNAL BUANA ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 94
Author(s):  
Rina Suksesi ◽  
Dedi Hermon ◽  
Endah Purwaningsih
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Sampling Method ◽  
Forest Cover ◽  
Sampling Technique ◽  
Residential Area ◽  
Carbon Stocks ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Quantitative Descriptive

This study aims to determine (1) changes in land cover in the Mount Padang Region in 1996, 2006 and 2016, (2) changes in carbon stocks as a result of changes in land cover in the Mount Padang Region of Padang City. The type of research is quantitative descriptive. Changes in land cover isanalyzed based on Landsat TM 5 of 1996 and 2006, as well as Landsat 8 OLI of 2016, using ENVI 4.5 and ArcGIS 10.1 and supervised classification method. Value of carbon stocks is obtained from the equation C = B ×% C (0.47), by predicting biomass on each type of carbon pool using allometric equations, which D2,62 ρ B = 0.11, B = exp {-2.134 + 2.530 × ln (D)}, B = 0.281 D2,06, and B = 0.030 D2,13, where D (diameter at breast height of trees, cm) and ρ (wood density). The sampling technique used is stratified random sampling method which refers to the technique of each plot on land cover classes which are then converted to thehectares area. The results of the analysis show that (1) the land cover in the Mount Padang Region of Padang City in 1996 has forest area of 744.23 Ha (92.6%), mixed garden area of 39.44 Ha (4.9%), shrubs of 17, 92 Ha (2.2%), and the settlement area of 2.35 Ha (0.3%). 2006 forest cover an area of 696.84 Ha (87%), mixed garden area of 18.84 Ha (2%), shrubs covering 37.55 Ha (5%), and residential area of 50.71 ha (6%). 2016 forest cover an area of 533.50 Ha (66%), mixed garden covering an area of 69.14 Ha (9%),shrubs covering 119.81 Ha (15%), and residential area of 81.49 Ha (10%). (2) the carbon stock in 1996 amounted to 495,800.03 tons, in 2006 a number of 458,165.73 tons, and in 2016 a number of 369,223.00 tons. Over the last 20 years, as a result of land cover changes in carbon stocks in Padang Mountain Region has been reduced as much as 126,577.03 tons.

scholarly journals ALTERAÇÃO NA COBERTURA FLORESTAL DA REGIÃO NOROESTE DA AMAZÔNIA MATOGROSSENSE

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 520
Author(s):  
Luani Rosa de Oliveira Piva ◽  
Rorai Pereira Martins Neto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Forest Conversion ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Landsat 8 Oli ◽  
Mato Grosso ◽  
The Past ◽  
Landsat 5 Tm

Nos últimos anos, a intensificação das atividades antrópicas modificadoras da cobertura vegetal do solo em território brasileiro vem ocorrendo em larga escala. Para fins de monitoramento das alterações da cobertura florestal, as técnicas de Sensoriamento Remoto da vegetação são ferramentas imprescindíveis, principalmente em áreas extensas e de difícil acesso, como é o caso da Amazônia brasileira. Neste sentido, objetivou-se com este trabalho identificar as mudanças no uso e cobertura do solo no período de 20 anos nos municípios de Aripuanã e Rondolândia, Noroeste do Mato Grosso, visando quantificar as áreas efetivas que sofreram alterações. Para tal, foram utilizadas técnicas de classificação digital de imagens Landsat 5 TM e Landsat 8 OLI em três diferentes datas (1995, 2005 e 2015) e, posteriormente, realizada a detecção de mudanças para o uso e cobertura do solo. A classificação digital apresentou resultados excelentes, com índice Kappa acima de 0,80 para os mapas gerados, indicando ser uma ferramenta potencial para o uso e cobertura do solo. Os resultados denotaram uma conversão de áreas florestais principalmente para atividades antrópicas agrícolas, na ordem de 472 km², o que representa uma perda de 1,3% de superfície de floresta amazônica na região de estudo.Palavras-chave: conversão de áreas florestais; uso e cobertura do solo; classificação digital; análise multitemporal. CHANGE IN FOREST COVER OF THE NORTHWEST REGION OF AMAZON IN MATO GROSSO STATE ABSTRACT: In the past few years, the intensification of anthropic activities that modify the soil-vegetation cover in Brazil’s land has been occurring on a large scale. To monitor the forest cover changes, the techniques of Remote Sensing of vegetation are essential tools, especially in large areas and with difficult access, as is the case of the Brazilian Amazon. The aim of this work was to identify the changes in land use and land cover, over the past 20 years, in the municipalities of Aripuanã and Rondolândia, Northwest of Mato Grosso State, in order to quantify the effective altered areas. Landsat 5 TM and Landsat 8 OLI digital classification images techniques were used in three different dates (1995, 2005 and 2015) and, later, the detection to the land use and land cover changes. The digital classification showed excellent results, with kappa index above 0.80 for the generated maps, indicating the digital classification as a potential tool for land use and land cover. Results reflect the conversion of forest areas mainly for agricultural activities, in the order of 472 km², representing a loss of 1.3% of Amazon forest surface in the study region.Keywords: forest conversion; land use and land cover; digital classification; multitemporal analysis.

scholarly journals PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

scholarly journals Analysis Of Elpiji Gas User Consumer Perception

2020 ◽  
Vol 2 (2) ◽  
pp. 51-56
Author(s):  
Aprian Sadlin ◽  
Sri Ernawati
Keyword(s):  
Sampling Method ◽  
Descriptive Analysis ◽  
Sampling Technique ◽  
Primary Data ◽  
Liquefied Petroleum Gas ◽  
Test Analysis ◽  
Processing Data ◽  
Cooking Process ◽  
Quantitative Descriptive ◽  
The City

This study aims to determine the perception of consumers of Liquefied Petroleum Gas (LPG) gas in Bima City. This type of research is quantitative descriptive. In this study the data used are primary data obtained from consumer LPG user questionnaires in the Bima City. Data were analyzed using univariable one-sample t-test analysis. The instrument was obtained from a questionnaire (questionnaire) that would be distributed and answered by LPG consumer users in the form of Likert scale questions. The population in this study is LPG consumer consumers in the City of Bima. The sample used is calculated using the formula Unknown Populations and the number of samples obtained is 96 consumers. As for the sampling technique using the accidental sampling method. After processing data using several statistical tools such as average values and descriptive analysis, the results of this study are the perceptions of LPG users who on average give good answers. LPG users state that the use of gas is more profitable than kerosene, in terms of cost being more economical, the cooking process is also faster and cleaner and there is no compulsion and receiving gas as a substitute for kerosene.

scholarly journals Analisis Strategi Bauran Promosi terhadap Volume Penjualan Restoran XYZ

2018 ◽  
Vol 12 (2) ◽  
pp. 161
Author(s):  
Megalia ◽  
Ujang Sumarwan ◽  
Imam Teguh Saptono
Keyword(s):  
Public Relations ◽  
Sampling Method ◽  
Personal Selling ◽  
Sampling Technique ◽  
Sales Promotion ◽  
Sales Volume ◽  
Managerial Implications ◽  
Strategic Influence ◽  
Descriptive Approach ◽  
Quantitative Descriptive

This study examines the strategic influence of promotion mix on the volume of aggregation and to know whether the marketing mix run by the Restaurant XYZ affect consumer spending. The theory used in this research is the promotion mix. The research design was conduced with a quantitative descriptive approach through an interview using quisionaire. Sampling method used is a purposive sampling technique with the number of respondents counted 200 people. In this study measure the influence caused by the promotion mix variables such as advertising, personal selling, sales promotion, public relations, and direct selling to increase sales volume. The results show that advertising variables are the most influential variabel of sales volume and the promotion mix simultaneously influences the buying decision of the customer. The findings of this research provide managerial implications that restaurant should not only focus on promotions costs for advertising alone, but also need to pay close attention to the allocation of appropriate funds to see the effectiveness of increased sales volume.

scholarly journals Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

2020 ◽  
Vol 12 (8) ◽  
pp. 1263 ◽  
Author(s):  
Yingfei Xiong ◽  
Shanxin Guo ◽  
Jinsong Chen ◽  
Xinping Deng ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
The Cross ◽  
Model Training

Detailed and accurate information on the spatial variation of land cover and land use is a critical component of local ecology and environmental research. For these tasks, high spatial resolution images are required. Considering the trade-off between high spatial and high temporal resolution in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve the spatial resolution of coarse images in both the computer vision and remote sensing fields. However, data for training and testing in these learning-based methods are usually limited to a certain location and specific sensor, resulting in the limited ability to generalize the model across locations and sensors. Recently, generative adversarial nets (GANs), a new learning model from the deep learning field, show many advantages for capturing high-dimensional nonlinear features over large samples. In this study, we test whether the GAN method can improve the generalization ability across locations and sensors with some modification to accomplish the idea “training once, apply to everywhere and different sensors” for remote sensing images. This work is based on super-resolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes model training more stable and enhances the generalization ability across locations and sensors. In the experiment, the training and testing data were collected from two sensors (Landsat 8 OLI and Chinese GF 1) from different locations (Guangdong and Xinjiang in China). For the cross-location test, the model was trained in Guangdong with the Chinese GF 1 (8 m) data to be tested with the GF 1 data in Xinjiang. For the cross-sensor test, the same model training in Guangdong with GF 1 was tested in Landsat 8 OLI images in Xinjiang. The proposed method was compared with the neighbor-embedding (NE) method, the sparse representation method (SCSR), and the SRGAN. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were chosen for the quantitive assessment. The results showed that the ISRGAN is superior to the NE (PSNR: 30.999, SSIM: 0.944) and SCSR (PSNR: 29.423, SSIM: 0.876) methods, and the SRGAN (PSNR: 31.378, SSIM: 0.952), with the PSNR = 35.816 and SSIM = 0.988 in the cross-location test. A similar result was seen in the cross-sensor test. The ISRGAN had the best result (PSNR: 38.092, SSIM: 0.988) compared to the NE (PSNR: 35.000, SSIM: 0.982) and SCSR (PSNR: 33.639, SSIM: 0.965) methods, and the SRGAN (PSNR: 32.820, SSIM: 0.949). Meanwhile, we also tested the accuracy improvement for land cover classification before and after super-resolution by the ISRGAN. The results show that the accuracy of land cover classification after super-resolution was significantly improved, in particular, the impervious surface class (the road and buildings with high-resolution texture) improved by 15%.

scholarly journals Identificação de Fragmentos Florestais Potencias para a delimitação de Corredores Ecológicos na bacia hidrográfica do Rio Itapemirim, ES por meio técnicas de Sensoriamento Remoto

2020 ◽  
Vol 13 (2) ◽  
pp. 595
Author(s):  
Ivo Augusto Lopes Magalhaes ◽  
Carlos Roberto Lima Thiago ◽  
Alexandre Rosa Dos Santos
Keyword(s):  
River Basin ◽  
Atlantic Forest ◽  
Forest Cover ◽  
Landsat 8 ◽  
Forest Fragments ◽  
Landsat 8 Oli ◽  
Ecological Corridors ◽  
Spatial Attributes ◽  
Remote Sensing Techniques ◽  
High Degree

Os corredores ecológicos surgem como alternativa para mitigar os efeitos da fragmentação florestal permitindo entre eles o fluxo gênico de fauna e flora e a recolonização de áreas degradadas. Diante do exposto o presente estudo teve como objetivo, identificar para a bacia hidrográfica do rio Itapemirim, ES, por meio de metodologia desenvolvida em Sistemas de Informações Geográficas, a delimitação de corredores ecológicos que propiciem a interligação de fragmentos florestais, identificados mediante análise das métricas da paisagem como fragmentos florestais com atributos espaciais, que sugerem maior conservação. A metodologia consistiu no mapeamento dos fragmentos florestais por meio de técnicas de classificação supervisionada utilizando imagem do satélite LANDSAT 8 OLI, obtidas junto ao Instituto Nacional de Pesquisas Espaciais. Realizou-se o cálculo dos índices de ecologia, por meio do software ArcGis 10.2, com a extensão de domínio público V-LATER 2.0. Identificou-se 11.749 fragmentos florestais, que representam 22% de cobertura florestal na bacia hidrográfica. Os fragmentos pequenos (< 5 ha) foram encontrados em maior número, 8.394, seguidos pelos fragmentos de tamanho médio (5 a 50 ha), 2.995, e grandes (> 50 ha), 360. O número de fragmentos apresentaram relação inversa com sua contribuição na área. O bioma Mata Atlântica presente na bacia hidrográfica do rio Itapemirim, é representado, em sua maioria, por fragmentos florestais pequenos, menores que 5 ha, indicando um alto grau de fragmentação.  Identification of Forest Fragments Potential for the delimitation of Ecological Corridors in the Itapemirim, ES River Basin through Remote Sensing techniques A B S T R A C TEcological corridors emerge as an alternative to mitigate the effects of forest fragmentation, allowing for the gene flow of fauna and flora and the recolonization of degraded areas. Given the above, the present study aimed to identify, for the Itapemirim river basin, ES, through a methodology developed in Geographic Information Systems, the delimitation of ecological corridors that allow the interconnection of forest fragments, identified through the analysis of the metrics. landscape as forest fragments with spatial attributes, which suggest greater conservation. The methodology consisted of mapping forest fragments by supervised classification techniques using LANDSAT 8 OLI satellite imagery, obtained from the National Institute for Space Research. Ecology indices were calculated using the ArcGis 10.2 software, with the public domain extension V-LATER 2.0. A total of 11,749 forest fragments were identified, representing 22% of forest cover in the watershed. Smaller fragments (<5 ha) were found in larger numbers, 8,394, followed by medium sized fragments (5 to 50 ha), 2,995, and large fragments (> 50 ha), 360. The number of fragments was inversely related to their size. contribution in the area. The Atlantic Forest biome present in the Itapemirim river basin is mostly represented by small forest fragments, smaller than 5 ha, indicating a high degree of fragmentation.Keywords: Indexes of landscape ecology, Atlantic Forest, Geoprocessing.

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