scholarly journals Differentials in Metropolitanisation Trends in Lagos Peri-Urban Settlements

2017 ◽  
Vol 10 (6) ◽  
pp. 14 ◽  
Author(s):  
Funmilayo Mokunfayo Adedire
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Secondary Data ◽  
Google Earth ◽  
Regional Government ◽  
Metropolitan Regions ◽  
Digital Format ◽  
Locational Quality ◽  
Urban Settlements ◽  
Household Heads

This paper examines the differential in the metropolitanisation of Lagos peri-urban settlements and the policy implication on locational quality of the emerging settlements. Two case studies of Ibeju-Lekki and Ikorodu were selected to represent the peri-urban settlements outside Lagos metropolitan regions. Using purposive sampling, thirty four settlements were selected which comprise sixteen and eighteen in Ibeju-Lekki and Ikorodu respectively. Data was sourced primarily through administration of 370 and 384 questionnaires to household heads in the selected settlements in Ibeju-Lekki and Ikorodu. Secondary data was sourced by conversion of analogue spatial images, the land use maps and satellite images of the study area to digital format. Spatial images from 1980 through 2016 were acquired for this study. Acquired satellite images from Google Earth archive were brought into ArcGIS environment for geo-referencing. Quantitative data was analysed using descriptive statistics while qualitative data was analysed using time series and satellite image analysis. Findings show a differential in transformation of the two cases due to varying demographic characteristics of residents, the locational convenience, level of linkages and the regional government housing policy. It is recommended that the regional planning should create a balance between the pace of development and infrastructural provision in the peri-urban to limit the disparity in development in Lagos peri-urban settlements.

scholarly journals Analysis of Animal Accidents along the Road of Padamara-Karangcegak in Geospatial Perspective

2019 ◽  
Vol 33 (1) ◽  
pp. 39-50
Author(s):  
Sakinah Fathrunnadi Shalihati ◽  
Esti Sarjanti
Keyword(s):  
Physical Environment ◽  
Satellite Image ◽  
Secondary Data ◽  
Google Earth ◽  
Qualitative Description ◽  
Primary Data ◽  
The Road ◽  
Primary Data Analysis ◽  
Animal Habitat ◽  
Potential Width

The occurrence of animal accidents is one of the consequences of physical environmental interaction of animal in terms of their movement from one place to another and non physical environment in the form of transportation usage by human. Accidents happened to animals can affect the structure of the food chain. It would be a matter if it occurred continuously because some species of animals that were important to the ecosystem would not exist anymore. The records of the frequency of animal accident and habitat along Jalan Padamara-Karangcegak by geospatial could be used to analyze the correlation between the dominance of animal species getting the accidents and the potential width of the habitat owned along the road. Qualitative description was used as the method of the research. It was from the primary data analysis of the distribution of accident and secondary data of the land use from Google Earth of  satellite image which was then processed by Geographic Information System. The results obtained during observations from April to July 2016 showed that there was a correlation between the animal habitat and the accidents happened. The animal with a wider habitat dominated the accidents occurred than animals with no extensive habitat.

Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Bernt E. Johansen ◽  
Stein Rune Karlsen ◽  
Hans Tømmervik
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Landsat Tm ◽  
Inland Waters ◽  
Vegetation Density ◽  
Ancillary Data ◽  
Vegetation Map ◽  
Digital Format ◽  
Different Types ◽  
Classified Image

ABSTRACTThe overall objective of this paper is to present and discuss the most recently developed vegetation map for Svalbard, Arctic Norway. The map is based on satellite images in which several Landsat TM/ETM+ images were processed through six operational stages involving: (1) automatic image classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardisation of the final map products. The developed map is differentiated into 18 map units interpreted from 37 spectral classes. Among the 18 units separated, six of the units comprise rivers, lakes and inland waters, glaciers, as well as non- to sparsely vegetated areas. The map unit 7 is a result of shadow effects and different types of distortions in the satellite image. The vegetation of the remaining eleven units varies from dense marshes and moss tundra communities to sparsely vegetated polar deserts and moist gravel snowbeds. The accuracy of the map is evaluated in areas were access to traditional maps have been available. The vegetation density and fertility is reflected in computed NDVI values. The map product is in digital format, which gives the opportunity to produce maps in different scales. A map sheet portraying the entire archipelago is one of the main products from this study, produced at a scale of 1:500,000.

scholarly journals PEMETAAN POTENSI BIOGAS DAN PUPUK DARI KOTORAN SAPI UNTUK MENDUKUNG WISATA PAMELO DI DESA BAGENG KECAMATAN GEMBONG KABUPATEN PATI

2018 ◽  
Vol 11 (2) ◽  
pp. 103-112
Author(s):  
Hermain Teguh Prayitno
Keyword(s):  
Data Processing ◽  
Environmental Pollution ◽  
Satellite Images ◽  
Public Information ◽  
Secondary Data ◽  
Google Earth ◽  
Primary Data ◽  
Energy Needs ◽  
Map Data

ENGLISHBageng village is a village which has pummelo plant and the highest number of cattle in Gembong Subdistrict. Therefore, this area known as the Pummelo Tourism Village. High number of cattle not followed by good handling of cow waste so that in some areas experience the environmental pollution. To reach the better pummelo tourism village, it necessary to improve the quality of the environment. One of the way is by mapping the potential of biogas from cow waste. This study aims to: 1) describe the condition of cattle in Bageng village; 2) mapping the potential of biogas and manure from cow waste for development of Pummelo. This research is exploratory. The primary data required includes cage density, number of cattle, the amount of pummelo and public information obtained from the questionnaire. Secondary data were obtained from village and subdistrict monograph. Data processing is also reinforced with satellite images of google earth, the google MAP data. The results showed that : 1) The number of cattle counted 508-720 head with 8.7 tons of cow waste/day; 2) The largest biogas potency contained in Rubiyah hamlet Bageng village. From this research are expected the use of cow waste as biogas to meet the energy needs of the communities as well as to meet the needs of Pummelo plant manure. INDONESIADesa Bageng merupakan desa yang memiliki tanaman jeruk pamelo dan jumlah ternak sapi tertinggi di Kecamatan Gembong. Oleh karenanya, daerah ini dikenal sebagai Desa Wisata Pamelo. Jumlah ternak sapi yang tinggi tidak diikuti dengan penanganan kotoran sapi yang baik sehingga di beberapa daerah mengalami pencemaran lingkungan. Untuk menuju Desa Wisata Pamelo yang lebih baik, diperlukan adanya peningkatan kualitas lingkungan. Salah satunya dengan cara pemetaan potensi biogas dari kotoran sapi. Penelitian ini bertujuan untuk: 1) menggambarkan kondisi ternak sapi Desa Bageng; 2) memetakan potensi biogas dan pupuk dari pemanfaatan kotoran sapi untuk perkembangan Jeruk Pamelo. Penelitian ini merupakan penelitian eksploratif. Data primer yang dibutuhkan meliputi kerapatan kandang, jumlah ternak, jumlah jeruk pamelo dan informasi masyarakat yang diperoleh dari kuesioner. Data sekunder diperoleh dari Monografi Desa dan Kecamatan. Pengolahan data juga diperkuat dengan foto citra satelit google earth, data google MAP. Hasil penelitian ini menunjukkan bahwa: 1) Jumlah ternak sapi terhitung 508 – 720 ekor dengan 8,7 ton kotoran sapi /hari; 2) Potensi biogas terbesar terdapat di Dukuh Rubiyah Desa Bageng. Dari penelitian ini diharapkan adanya pemanfaatan kotoran sapi sebagai biogas untuk mencukupi kebutuhan masyarakat akan energi sekaligus untuk memenuhi kebutuhan pupuk tanaman Jeruk Pamelo.

scholarly journals PERKEMBANGAN MANGROVE DAN PRODUKSI IKAN LAUT DI PATI UTARA

2017 ◽  
Vol 13 (2) ◽  
pp. 93-104
Author(s):  
Hermain Teguh Prayitno
Keyword(s):  
Marine Fish ◽  
Mangrove Forest ◽  
Satellite Images ◽  
Satellite Image ◽  
Google Earth ◽  
Landsat 8 ◽  
Fish Production ◽  
Growth Data ◽  
Google Maps ◽  
The Impact

ENGLISHThe fishing activities along the coastal area in Northern Pati potentially cause mangrove destruction. The objectives of this study were: (1) to discuss the growth of mangrove area in Northern Pati and (2) to describe the impact of the mangrove forest on marine fish production. This study used quantitative-descriptive research design. The study locations were Dukuhseti and Tayu subdistrict. It was conducted from August to September 2017. It used raster data obtained from Google Earth and Google Maps, formed Landsat 8 satellite images. The other was data of mangrove forest area in Pati Regency. The data analysis was conducted using the satellite images in 2011, 2013, 2015, and 2017. Then, the mangrove growth data were weighted and classified using scores. Validation was done by comparing the mangrove data from Statistic Agency of Pati Regency and those of the satellite image measuring results. The study concluded: (1) The mangrove forest in Northern Pati was in a poor condition. (2) The growth of mangrove forest fluctuated, which the lowest growth happened in 2013, while the highest was found in 2017. (3) The growth of mangrove forest was followed by the increase of marine fish production. INDONESIAKabupaten Pati berada di bagian utara Pulau Jawa memiliki aktivitas masyarakat sebagai pertambak dan nelayan. Penambahan tambak ke arah laut menyebabkan kerusakan mangrove. Tujuan penelitian ini adalah : (1) untuk mengetahui pertumbuhan mangrove di pesisir Pati utara dan (2) untuk menggambarkan dampak hutan mangrove terhadap produktivitas ikan laut. Penelitian ini merupakan penelitian deskriptif kuantitatif. Lokasi penelitian yaitu Kecamatan Dukuhseti dan Kecamatan Tayu. Waktu Penelitian Agustus sampai dengan September 2017. Sumber data berasal dari data raster dari Google Earth dan Google Maps berupa image satelit Landsat 8. Data lain yaitu data luas hutan mangrove di Kabupaten Pati. Analisis data dengan pengukuran image satelit tahun 2011, 2013, 2015 dan 2017. Selanjutnya data perkembangan mangrove dilakukan pembobotan dan diklasifikasikan berdasarkan skor. Verifikasi hasil dilakukan dengan cara membandingkan data perkembangan mangrove dari BPS Kabupaten Pati dengan hasil pengukuran image satelit. Hasil penelitian yaitu (1) hutan mangrove di Pati utara dalam kondisi berkembang buruk. (2) Perkembangan hutan mangrove fluktuatif, dimana perkembangan terendah terjadi pada 2013, sedangkan perkembangan tertinggi pada 2017; 2) perkembangan mangrove di Pati utara mengalami penurunan pada tahun 2013 dan meningkat kembali pada tahun 2015 dan 2017. (3) Perkembangan mangrove diiringi dengan peningkatan produksi ikan segar di Pati Utara.

scholarly journals Ecological Monitoring with Spy Satellite Images—The Case of Red Wood Ants in Romania

2021 ◽  
Vol 13 (3) ◽  
pp. 520
Author(s):  
Dietrich Klimetzek ◽  
Petru Tudor Stăncioiu ◽  
Marius Paraschiv ◽  
Mihai Daniel Niță
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Satellite Image ◽  
Canopy Cover ◽  
Ecological Monitoring ◽  
Google Earth ◽  
Ecosystem Dynamics ◽  
Small Scale ◽  
Habitat Conditions ◽  
Sources Of Information

Dynamics of habitat conditions drive important changes in distribution and abundance of animal species making monitoring an important but also a challenging task when data from the past are scarce. We compared the distribution of ant mounds in the 1960s with recent inventories (2018), looking at changes in canopy cover over time, in a managed forest. Both historical and recent sources of information were used. Habitat suitability at present was determined using a Normalized Difference Vegetation Index (NDVI) image as a proxy for stand canopy cover. The NDVI product was obtained using Google Earth Engine and Sentinel 2 repository. For past conditions (no spectral information available), presence of edges and more open canopies was assessed on a Corona spy-satellite image and based on information from old forest management plans. A threshold distance of 30 m was used to assess location of ant nests compared to favorable habitats. Both old and new information sources showed that ants prefer intermediate canopy cover conditions in their vicinity. Nests remained clustered because of the heterogeneous habitat conditions, but spatial distribution has changed due to canopy alteration along time. The analysis on the NDVI was effective for 82% of cases (i.e., nests occurred within 30 m from favorable habitats). For all the remaining nests (18%), the Google Earth high resolution satellite image revealed in their vicinity the presence of small canopy gaps (undetected by the NDVI). These results show that historical satellite images are very useful for explaining the long-term dynamics of ant colonies. In addition, the use of modern remote sensing techniques provides a reliable and expedite method in determining the presence of favorable small-scale habitat, offering a very useful tool for ecological monitoring across large landscapes and in very different areas, especially in the context of ecosystem dynamics driven and exacerbated by climate change.

Image Segmentation Based Approach for the Purpose of Developing Satellite Image Spatial Information Extraction for Forestation and River Bed Analysis

2019 ◽  
Vol 19 (01) ◽  
pp. 1950002 ◽  
Author(s):  
P. K. Dutta
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Satellite Images ◽  
Spatial Information ◽  
Satellite Image ◽  
Graph Cuts ◽  
Google Earth ◽  
Image Resolution ◽  
Registration Method ◽  
River Bed

Classification of remote sensing spatial information from multi spectral satellite imagery can be used to obtain multiple representation of the image and capture different structure lineaments. Pixels are grouped using clustering and morphology based segmentation for region based spatial information. This is used to calculate the spatial features of the contiguous regions by classifying the region into the statistics of the pixel properties. In the proposed work, analysis of Google Earth images for identification of morphological patterns of the river flow is done for remote sensing image using graph-cuts. Multi-temporal satellite images acquired from Google Earth to identify the digital elevation is used to formulate the energy function from images to compare the displacement in pixel value using similarity measure. A method is proposed to solve non-rigid image transformation via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement vector associated to each pixel in the source image indicates the corresponding position in the moving image. The transformation matrix produced from change in the intensity of the pixels for a region is then optimized for energy minimization by using the graph-cuts algorithm and demon registration technique. The proposed study enhances the advantages of regional segmentation in order to know homogeneous areas for optimal image segmentation and digital footprints for change in the river bed patterns by identifying the change in LANDSAT data from temporal satellite images. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. The results demonstrate that the lineament detection for better accuracy compared to traditional sources of lineament identification methods. It has provided better geotectonic understanding of Cudappah rock in Ahobhilam with Quartzite. The imprints of Eastern Ghat orogeny are seen in upper stream section through a graph cut based segmentation approach.

PENGARUH PDRB DAN UKURAN TERHADAP INTERNAL CONTROL PEMERINTAH DAERAH DENGAN PAD SEBAGAI INTERVENING

ProBank ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 64-75
Author(s):  
Prima Utama Wardoyo Putro
Keyword(s):  
Internal Control ◽  
Central Government ◽  
Control Variable ◽  
Secondary Data ◽  
Internal Controls ◽  
Regional Government ◽  
Path Testing ◽  
Size Growth ◽  
Fiscal Balance ◽  
Research Populations

Law No 32 year on 2004 about Regional Government and Law No 33 years on 2004 about Fiscal Balance between the Central Government and Regional Government are a new regulation relating to the implementation of regional autonomy in Indonesia. Giving the authority to manage its own region required an internal control system that can monitor of all by central government. The problem in this study is: Is there are any influence between growth, size, and PAD through Regional Government Internal Controls weakness with PAD as an intervening variable. The research populations are the financial statements and reports on the results of the entire province in Indonesia totaling 33 provinces. Source of data which are used are secondary data, and data collection by using the documentation method. The results of partial testing showed that PAD and Growth have significant affects to the Internal Controls, whereas size has not significant effect. Simultaneous testing showed a significant effect between the independent and dependent variables. The test results path testing showed that growth has no significant effect to internal control through PAD as an intervening variable and size has a significant effect to internal control variable through PAD as an intervening variable. The results of determinant coefficient by simultan test amount 28.7%. Its mean that Internal Control can be explained by Growth, Size and PAD, the remaining 71.3% influenced by factors other than study. Keyword: Internal Control, Size, Growth, and Income

Mapping of African urban settlements using Google Earth images

2021 ◽  
Vol 42 (13) ◽  
pp. 4886-4901
Author(s):  
José Luis San Emeterio ◽  
Catherine Mering
Keyword(s):  
Google Earth ◽  
Urban Settlements

Regional-scale analysis of dune-beach systems using Google Earth Engine

2021 ◽  
Author(s):  
Melissa Latella ◽  
Arjen Luijendijk ◽  
Carlo Camporeale
Keyword(s):  
Ecosystem Services ◽  
Satellite Images ◽  
Regional Scale ◽  
Google Earth ◽  
Classification Algorithm ◽  
Mitigation Strategies ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Beach Width ◽  
Google Earth Engine

<p>Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services.</p><p>Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only. </p><p>According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities. </p>

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

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