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2022 ◽  
Author(s):  
Mia Elisa Martin ◽  
Ana Carolina Alonso ◽  
Janinna Faraone ◽  
Marina Stein ◽  
Elizabet L Estallo

The presence, abundance and distribution of Aedes (Stegomyia) aegypti (Linnaeus 1762) and Aedes (Stegomyia) albopictus (Skuse 1894) could be conditioned by different data obtained from satellite remote sensors. In this paper, we aim to estimate the effect of landscape coverage and spectral indices on the abundance of Ae. aegypti and Ae. albopictus from the use of satellite remote sensors in Eldorado, Misiones, Argentina. Larvae of Aedes aegypti and Ae. albopictus were collected monthly from June 2016 to April 2018, in four outdoor environments: tire repair shops, cemeteries, family dwellings, and an urban natural park. The proportion of each land cover class was determined by Sentinel-2 image classification. Furthermore spectral indices were calculated. Generalized Linear Mixed Models were developed to analyze the possible effects of landscape coverage and vegetation indices on the abundance of mosquitoes. The model's results showed the abundance of Ae. aegypti was better modeled by the minimum values of the NDVI index, the maximum values of the NDBI index and the interaction between both variables. In contrast, the abundance of Ae. albopictus has to be better explained by the model that includes the variables bare soil, low vegetation and the interaction between both variables.


2022 ◽  
Vol 951 (1) ◽  
pp. 012080
Author(s):  
A A Nasution ◽  
A M Muslih ◽  
U H Ar-Rasyid ◽  
A Anhar

Abstract Land cover information is needed by various parties as a consideration in controlling land cover changes. The latest land cover information can be obtained using remote sensing techniques in the form of image classification maps. This technique is very effective in monitoring land cover because of its ability to quickly, precisely, and easily provide spatial information on the earth’s surface. The purpose of this study was to classify land cover in West Langsa Sub district, Langsa City using Landsat 8 OLI (Operational Land Imager) imagery. The classification method used in this study is the maximum likelihood classification (MLC) method. There are several considerations of various factors in the MLC method, including the probability of a pixel to be classified into a certain type or class. The results of Landsat 8 OLI image classification in West Langsa Sub district resulted in 6 land cover classes, namely mangrove forests, settlements, rice fields, shrubs, ponds and bodies of water. The largest land cover class is ponds with an area of 1981.54 ha (38.71%) and the smallest land cover is rice fields with an area of 115.58 ha (2.26%) of the total land cover class. Classification accuracy is indicated by the overall accuracy and kappa accuracy of 91.15% and 82.75%, respectively. These results meet the requirements set by the USGS (Overall Accuracy > 85%) and indicate that the Landsat 8 OLI image classification map can be used for various purposes.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Berhanu Tamiru ◽  
Teshome Soromessa ◽  
Bikila Warkineh ◽  
Gudina Legesse ◽  
Merga Belina

Abstract Background Hangadi watershed is endowed with the only virgin forest in Odo shakisso harbouring high biodiversity, but it has been suffered from anthropogenic activities. This study was conducted to investigate composition and community diversity of woody species in restoration for posterity. Satellite images of 1988, 2008, and 2018 were used to classify and analyse trends of deforestation. For both woody species and topsoil (0–30 cm), 20 m × 20 m, 100 plots laid at every 300 m along line transects, 1 km apart from each other. In each sample plot, woody species ≥ 3 m were counted, Shannon–wiener diversity index, cluster analysis and ordination were computed. Results Agroforestry is found to be the dominant land use/land cover class followed by forest and cultivated land. A total of 61 woody species belonging to 34 families; 8.2% of the species were endemic to Ethiopia. The highest number of species was recorded from families Euphorbiaceae and Rubiaceae (5 species, 8.2%), Rutaceae, Celastraceae, and Oleaceae (3 species, 5.08%) followed by Flacourtiaceae, Meliaceae, Araliaceaae, Myrsinaceae, Moraceae, Boraginaceae, Asteraceae, Spontaceae, Lauraceae and Sapindaceae (2 species each). Four woody plant communities were identified using free statistical software R version 3.1.1. The canonical correspondence analysis result showed that EC, pH, OM, altitude, C:N, CEC, sand, silt, AvP, and TN significantly affected species distribution in the watershed. Conclusion Local people involved in cutting mature woody species for timber production, making farm implements and, cultivated land expansion. Protection of woody species diversity of forest and coffee systems with low biodiversity value conservation concepts are recommended to be executed jointly by local people and stakeholders.


2021 ◽  
Vol 20 (1) ◽  
pp. 65-75
Author(s):  
Gusti Rachmad Rabsanjani ◽  
Aji Ali Akbar ◽  
Henny Herawati

Banjir merupakan becana yang kerap sekali terjadi pada musim hujan, banjir dapat menyebabkan kehilangan harta benda maupun korban jiwa. Valuasi ekonomi akibat terjadinya banjir adalah salah satu cara yang dapat digunakan untuk menghitung kerugian akibat terjadinya bencana banjir. Tidak adanya kajian mengenai kerentanan dan kerugian ekonomi akibat banjir pada tiga desa di Kecamatan Ngabang yaitu Desa Raja, Hilir Tengah dan Hilir Kantor adalah alasan dilakukannya penelitian ini. Tujuan dilakakukan penelitian ini adalah untuk mengidentifikasi dan menginventarisasi besarnya tingkat kerentanan terhadap banjir yang terjadi dan menghitung valuasi kerugian ekonomi akibat terjadinya bencana banjir. Metode yang digunakan dalam menganalisis kerentanan banjir menggunakan software ArcMap 10.3 adalah Analisa atribut meliputi pemberian skor kelas curah hujan, pemberian skor kelas tutupan lahan, pemberian skor kelas kemiringan lahan, pembobotan dan Analisa AHP. Metode yang digunakan untuk menghitung estimasi kerugian akibat banjir menggunakan metode ECLAC. Hasil yang didapat dalam penelitian ini adalah perubahan tutupan lahan mengalami penurunan dan peningkatan luasan permukiman, pertanian/sawah, dan lahan terbuka/semak, Curah hujan yang tinggi dan kelerengan daerah yang landai menjadi parameter penyebab terjadinya banjir. Pada estimasi nilai kerugian akibat banjir dengan nilai kerugian menggunakan USD dan Emas pada tahun yang ditentukan dengan hasil total kerugian pada tahun 1973 adalah 73,7 Juta dollar, tahun 1989 180 juta dollar, tahun 1994 261 juta dollar, tahun 2000 261juta dollar, tahun 2010 1,1 miliar dollar, dan tahun 2020 1,9 miliar dollar.ABSTRACTFlood is a plan that often occurs in the rainy season, floods can cause loss of property and fatalities. Economic valuation due to flooding is one way that can be used to calculate losses due to flood disasters. The absence of studies on vulnerability and economic losses due to flooding in three villages in Ngabang Subdistrict namely Desa Raja, Hilir Tengah and Hilir Kantor is the reason for this research. The purpose of this study is to identify and inventory the level of vulnerability to floods that occur and calculate the valuation of economic losses due to flood disasters. The methods used in analyzing flood vulnerabilities using ArcMap 10.3 software are attribute analysis including rainfall class scoring, giving land cover class scores, awarding land slope class scores, weighting and AHP Analysis. The method used to calculate the estimated loss due to flooding uses the ECLAC method. The results obtained in this study are changes in land cover experiencing a decrease and increase in the area of settlements, agriculture / rice fields, and open land / bush, high rainfall and marbles of sloping areas become parameters of the cause of flooding. In the estimated value of losses due to floods with the value of losses using USD and Gold in the specified year with the total loss in 1973 was 73.7 million dollars, in 1989 180 million dollars, in 1994 261 million dollars, in 2000 261 million dollars, in 2010 1.1 billion dollars, and in 2020 1.9 billion dollars.


Author(s):  
Vaibhav A. Didore ◽  
Dhananjay B. Nalawade ◽  
Renuka B. Vaidya

Remote sensing is the prominent technology to study the ecology of the earth. Classification is a commonly used technique for quantitative analysis of remote sensing image data. It is based on the concept of segmentation of spectral regions into regions that can be associated with a soil cover class of interest for a particular application. As an advanced remote sensing tool, Hyperspectral remote sensing technology has been studied in many applications such as geology, topography, biology, soil science, hydrology, plants and ecosystems, atmospheric science. In this paper, Supervised Decision tree; Minimum distance; Maximum likelihood classification; Parallelepiped; K-nearest neighbor; and Unsupervised K-mean; & ISODATA algorithm are reviewed. This review is helpful to the researchers who are studying this emerging field i.e. HRS.


2021 ◽  
Vol 19 (2) ◽  
pp. 450-458
Author(s):  
Rahmat Fadhli ◽  
Sugianto Sugianto ◽  
Syakur Syakur

Perubahan penutupan lahan merupakan sektor penyumbang emisi gas rumah kaca terbesar, termasuk di dalamnya adalah pemanfaatan lahan. Analisis tutupan lahan menjadi bagian penting dalam menentukan jumlah potensi karbon yang tersedia. Penelitian bertujuan untuk menganalisis perubahan tutupan lahan dari tahun 2003 hingga 2018 dan menghitung potensi karbon di Taman Hutan Raya Pocut Meurah Intan dengan luas objek penelitian 6.215 ha. Penelitian dilaksanakan selama 5 (lima) bulan. Penelitian ini menggunakan metode stock difference, yaitu metode perhitungan luas tutupan lahan dan stok karbon pada dua titik waktu. Hasil penelitian menunjukkan bahwa perubahan luas tertinggi tahun 2018 seluas 263 ha dan terendah tahun 2009 seluas 108 ha. Lahan terbuka meningkat seluas 100 ha, pemukiman 81 ha, semak belukar 65 ha, pertanian lahan kering campur semak 32 ha. Sementara hutan lahan kering sekunder menurun 79 ha, hutan tanaman 76 ha, savanna 21 ha dan pertanian lahan kering 103 ha. Selama kurun waktu 15 tahun berdasarkan kelas penutupan lahan, cadangan karbon tertinggi pada tahun 2003 sebesar 656.053 ton, terendah tahun 2012 sebesar 620.992 ton. Laju serapan karbon tertinggi pada periode tahun 2015-2018 sebesar 94.615 ton CO2 dan terendah pada periode tahun 2009-2012 sebesar 1.981 ton CO2. Laju emisi tertinggi pada periode tahun 2003-2006 sebesar 79.559 ton CO2 dan terendah periode tahun 2006-2009 sebesar 9.069 ton CO2. Peningkatan serapan karbon diakibatkan oleh meningkatnya luas tutupan lahan pada hutan lahan kering sekunder dan adanya pemanfaatan lahan untuk pertanian lahan kering campur semak.ABSTRACTChanges in land cover are the largest contributor to greenhouse gas emissions, including land use. Land cover analysis is an important part in determining the potential amount of carbon available. The study aims to analyze changes in land cover from 2003 to 2018 and calculating the carbon potential in the Pocut Meurah Intan Forest Park with a research object area of 6,215 ha. The research was conducted for 5 (five) months. This research uses the stock difference method, namely the method of calculating land cover area dan stok karbon pada dua titik waktu. The results showed that the highest area change in 2018 was 263 ha and the lowest was in 2009 at 108 ha. Open land increased by 100 ha, settlement 81 ha, scrub 65 ha, dry land agriculture mixed with shrubs 32 ha. Meanwhile, secondary dry land forest decreased by 79 ha, plantation forest 76 ha, savanna 21 ha and dry land agriculture 103 ha. Over a 15 year period based on land cover class, the highest carbon stock in 2003 was 656,053 tons, the lowest was in 2012 at 620,992 tons. The highest carbon absorption rate in the 2015-2018 period was 94,615 tons of CO2 and the lowest was in the 2009-2012 period of 1,981 tons of CO2. The highest emission rate in the 2003-2006 period was 79,559 tonnes of CO2 and the lowest for the 2006-2009 period was 9,069 tonnes of CO2. The increase in carbon sequestration is caused by the increase in land cover in secondary dryland forest and the use of land for mixed dry land agriculture.


2021 ◽  
Author(s):  
Maria Lumbierres ◽  
Prabhat Raj Dahal ◽  
Moreno Di Marco ◽  
Stuart H.M. Butchart ◽  
Paul F. Donald ◽  
...  

Area of Habitat (AOH) is defined as the habitat available to a species, that is, habitat within its range and is produced by subtracting areas of unsuitable land cover and elevation from the range. Habitat associations are documented using the IUCN Habitats Classification Scheme, and unvalidated expert opinion has been used so far to match habitat to land-cover classes generating a source of uncertainty in AOH maps. We develop a data-driven method to translate IUCN habitat classes to land-cover based on point locality data for 6,986 species of terrestrial mammals, birds, amphibians and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class(es) assigned to each species occurring there. Then we modelled each land cover class as a function of IUCN habitat using logistic regression models. The resulting odds ratios were used to assess the strength of the association of each habitat land-cover class. We then compared the performance of our data-driven model with those from a published expert knowledge translation table. The results show that some habitats (e.g. forest and desert) could be more confidently assigned to land-cover classes than others (e.g. wetlands and artificial). We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH, which is in the form of a binary presence/absence , it is necessary to apply a threshold of association. This can be chosen by the user according to the required balance between omission and commission errors. We demonstrate that a data-driven translation model and expert knowledge perform equally well, but the model provides greater standardization, objectivity and repeatability. Furthermore, this approach allows greater flexibility in the use of the results and allows uncertainty to be quantified. Our model can be developed regionally or for different taxonomic groups.


2021 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Piotr Michalak ◽  
Angelina Patsili ◽  
Olga Carmen ◽  
Carsten Keßler

Abstract. Sea-level rise in Southeast Asia is a consequence of climate change that will affect almost all coastal countries in the region. The results of this phenomenon may have severe consequences, from problems with food production, through mass migration of people, to the threat to unique ecological areas. Hence, the main aim of this research was to investigate the impact of sea level rise on the land cover structure in the region and how it may affect the situation of the countries in the region. For this purpose, GlobCover 2009 data and projections of sea level rise by one meter were used and a multiband raster image was created containing information about the land cover class, country and whether the area is threatened by sea level rise. All calculations have been made on the raster prepared in this way, which shows that 4.4% of South East Asia's areas are at risk of rising sea levels. Finally, the ratio was calculated for each land cover class. This showed the unusual vulnerability of some of the classes to rising sea levels like irrigated croplands and urban areas.


2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati

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.


Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 35
Author(s):  
Dingfan Xing ◽  
Stephen V. Stehman ◽  
Giles M. Foody ◽  
Bruce W. Pengra

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.


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