Aplikasi Sistem Informasi Geografis untuk Kajian Perencanaan Rehabilitasi Hutan Mangrove di Kecamatan Punduh Pedada, Lampung

Mangrove forests are one of the coastal ecosystems that are under pressure due to antrophogenic activities, including land conversions to fishponds or aquaculture. On the other hand, the needs for seafood products from these aquaculture activities tend to increase annually. These situations become paradoxes that are often encountered within aquaculture management in Indonesia. Punduh Pidada Subdistrict is one of the areas in Pesawaran Regency, Lampung, that severely suffered from mangrove forest conversion into fishponds. The purposes of this study were to identify the trends in mangrove cover changes over 30 years and to design a mangrove rehabilitation plan in Punduh Pidada Subdistrict. This study used a time-series data of Landsat satellite imagery over 30 years, from 1989 to 2019. The results showed a significant decrease in the mangrove cover areas over 30 years at 83.34 Ha with an average mangrove cover losses at 30.5%. Three zones that are suitable for mangrove rehabilitation plan was then choosen based on the levels of mangrove loss areas, namely first priority rehabilitation plan covering zone 1 (1.7 Ha) and zone 2 (1.5 ha) and the second priority rehabilitation plan covering zone 3 (7.5 Ha). With a planting distance of 1 x 2 meter, the total of seeds needed for the rehabilitation planning are 8,500 seeds for zone 1, 7,500 seeds for zone 2, and 27,500 seeds for zone 3.

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Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Remote Sensing ◽

10.3390/rs12172735 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2735 ◽

Cited By ~ 16

Author(s):

Carlos M. Souza ◽

Julia Z. Shimbo ◽

Marcos R. Rosa ◽

Leandro L. Parente ◽

Ane A. Alencar ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Data Series ◽

Forest Conversion ◽

Pixel Resolution ◽

Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

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Spatio-temporal assessment of ecological disturbance and its intensity in the Mangrove forest using MODIS derived disturbance index

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-555-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 555-559 ◽

Cited By ~ 1

Author(s):

D. Dutta ◽

P. K. Das ◽

S. Paul ◽

J. R. Sharma ◽

V. K. Dadhwal

Keyword(s):

Time Series ◽

Time Series Data ◽

Mangrove Ecosystem ◽

Series Data ◽

Landsat 8 ◽

Mangrove Forests ◽

Ecological Disturbance ◽

Disturbance Index ◽

Disturbed Areas ◽

Spatio Temporal

The mangrove ecosystem of Sundarbans region plays an important ecological and socio-economical role in both India and Bangladesh. The ecological disturbance in the coastal mangrove forests are mainly attributed to the periodic cyclones caused by deep depression formed over the Bay of Bengal. In the present study, three of the major cyclones in the Sundarbans region were analyzed to establish the cause-and-effect relationship between cyclones and the resultant ecological disturbance. The Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data was used to generate MODIS global disturbance index (MGDI) and its potential was explored to assess the instantaneous ecological disturbance caused by cyclones with varying landfall intensities and at different stages of mangrove phenology. The time-series MGDI was converted into the percentage change in MGDI using its multi-year mean for each pixel, and its response towards several cyclonic events was studied. The affected areas were identified by analyzing the Landsat-8 satellite data before and after the cyclone and the MGDI values of the affected areas were utilized to develop the threshold for delineation of the disturbed pixels. The selected threshold was applied on the time-series MGDI images to delineate the disturbed areas for each year individually to identify the frequently disturbed areas. The classified intensity map could able to detect the chronically affected areas, which can serve as a valuable input towards modelling the biomigration of the invasive species and efficient forest management.

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Analysing the rate of mangrove forest conversion in South Konawe, Southeast Sulawesi

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/886/1/012126 ◽

2021 ◽

Vol 886 (1) ◽

pp. 012126

Author(s):

Dewi Nurhayati Yusuf ◽

LB Prasetyo ◽

C Kusmana ◽

Machfud ◽

Ritabulan

Keyword(s):

Mangrove Forest ◽

Rapid Development ◽

Forest Conversion ◽

Multiple Time ◽

Mangrove Forests ◽

Multiple Time Series ◽

The Past ◽

Aquaculture Ponds ◽

Open Land ◽

Landsat Satellite

Abstract The degradation of mangrove forests in the Konawe Selatan District has been very rapid over the past three decades. Increases in population growth in coastal areas have been associated with rapid development, including the need for land for housing and livelihood. This development has led to land conversion from mangrove forests to other uses. The aim of the research was to identify the pattern of spatial change of mangrove forests in South Konawe using a geospatial approach from 1984 to 2014. Landuse classification was generated through the processing of Landsat satellite imagery in multiple time series. The research showed that that between 1984 and 1993 in South Konawe District about 9.9% of mangrove forest was converted into open land, 2.3% into aquaculture ponds, and 0.4% into settlements. From 1993 to 2003, the rate of conversion increased rapidly as 13.8% of the remaining mangrove forest was cleared for aquaculture ponds and 1.5% into a settlement. Over the past three decades, 39.9% of mangrove forest in the district has been converted to other uses, and some of this conversion has occurred in protected areas. It’s recommended that the stronger enforcement of regulations pertaining to the protection of mangrove forests in South Konawe.

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Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud

Professional Paper ◽

10.3133/pp1868 ◽

2021 ◽

Author(s):

Prasad S. Thenkabail ◽

Pardhasaradhi G. Teluguntla ◽

Jun Xiong ◽

Adam Oliphant ◽

Russell G. Congalton ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Data ◽

Learning Algorithms ◽

Google Earth ◽

Machine Learning Algorithms ◽

Series Data ◽

Google Earth Engine ◽

Landsat Satellite

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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