Dense Class Research Articles

Utilisation of Sentinel-2A Imagery for Estimation of Mangrove Carbon Stock in Mamminasata Area, South Sulawesi

Population growth and land conversion have led to the degradation of mangrove forests on the southern coast of South Sulawesi, especially the Mamminasata area. Reduced mangroves increase carbon dioxide in the atmosphere. However, data on the potential carbon absorption of mangroves is still lacking. To overcome this, remote sensing is used to estimate carbon reserves. This reseach utilises Sentinel-2A imagery to estimate mangrove carbon stocks in Mamminasata. The image processing process includes radiometric correction, atmospheric correction, image classification, and extraction of NDVI values. The NDVI value is used to classify the density of mangroves into sparse, medium, and dense, covering 1,244.75 hectares. Field data collection was carried out through a survey of forest stand measurements. The results of NDVI transformation show a value range of 0.2 to 0.8 for mangrove objects in the Mamminasata area. The NDVI data on the analysed images were then made into three density classes. The rare density class has a carbon value of 3.56 – 21.16 Ton C/ha, the medium density class is between 21.17 – 31.49 Ton C/ha, and the dense density class is between 31.50 – 39.18 Ton C/ha. Regression analysis shows a strong correlation between NDVI and carbon stock (R² = 0.7134). This study confirms the effectiveness of remote sensing in environmental monitoring and mangrove conservation. These findings support conservation efforts and sustainable management policies by highlighting areas with high carbon sequestration potential.

Spatio-temporal dynamic analysis of greenness base on NDVI equation for change detection from 2004 to 2024 at River Nile State, Atbara City, Sudan by using remote sensing and geographical information system

Urban regions' ability to maintain high environmental standards is largely dependent on their vegetation. A decrease in the amount of vegetated area on land has resulted from city expansion and population growth. An examination of land cover change in urban settings is necessary, particularly for urban regional planning that takes green, open space into account. The purpose of this study was to examine changes in urban vegetation cover in two Medan sub-districts from 2004 to 2014. Change analysis and the Normalized Difference Vegetation Index (NDVI) were performed in the study. The range of plant diversity in these locations was Observed. The results showed changes in vegetation cover areas in the mentioned years. In 2004, most of the areas were under a highly dense vegetation class while in 2014, they were under a low-density vegetation class with new sub-class. This indicates a decrease in vegetation cover due to changes to non-vegetation cover or land cover areas with less vegetation.

Analysis of the environmental damage and agricultural utilization in Forest Area of Dieang Plateau, Central Java, Indonesia

Dieng Plateau is a renowned tourist attraction due to its captivating panoramas, protected forest, agricultural viability, and geothermal resources. However, encouraging social life forces the local community to depend on the surrounding forest by utilizing the forest area as agricultural land. These activities harm the environment. This study aimed to determine the level of environmental damage and agricultural utilization in forest area of Dieng Plateau. Data collection methods were observation and interviews, with a purposive determination of the sample. Data analysis was carried out spatially to determine the level of environmental damage. The results showed that the erosion rate in the Dieng Plateau was in the moderate (33-35 cm), heavy (86-100 cm) and very heavy class (92-235 cm). The level of canopy cover is in the moderately dense class (60%), sparse class (20-40%) and very sparse class (<20%). The level of land productivity is in the low class (40%), moderate class (45-53%), high class (70%) and very high (93%). The level of environmental management ranged from poor to excellent. The level of environmental damage in total is in the rather severe and damaged class. For this reason, it is necessary to implementing environmental damage control strategies by land rehabilitation and closely monitoring agricultural activities by the forest area management and local government authorities are essential measures.

Empirical Measures, Geodesic Lengths, and a Variational Formula in First-Passage Percolation

This monograph resolves—in a dense class of cases—several open problems concerning geodesics in i.i.d. first-passage percolation on Z d \mathbb {Z}^d . Our primary interest is in the empirical measures of edge-weights observed along geodesics from 0 0 to n ξ n{\boldsymbol \xi } , where ξ {\boldsymbol \xi } is a fixed unit vector. For various dense families of edge-weight distributions, we prove that these empirical measures converge weakly to a deterministic limit as n → ∞ n\to \infty , answering a question of Hoffman. These families include arbitrarily small L ∞ L^\infty -perturbations of any given distribution, almost every finitely supported distribution, uncountable collections of continuous distributions, and certain discrete distributions whose atoms can have any prescribed sequence of probabilities. Moreover, the constructions are explicit enough to guarantee examples possessing certain features, for instance: both continuous and discrete distributions whose support is all of [ 0 , ∞ ) [0,\infty ) , and distributions given by a density function that is k k -times differentiable. All results also hold for ξ {\boldsymbol \xi } -directed infinite geodesics. In comparison, we show that if Z d \mathbb {Z}^d is replaced by the infinite d d -ary tree, then any distribution for the weights admits a unique limiting empirical measure along geodesics. In both the lattice and tree cases, our methodology is driven by a new variational formula for the time constant, which requires no assumptions on the edge-weight distribution. Incidentally, this variational approach also allows us to obtain new convergence results for geodesic lengths, which have been unimproved in the subcritical regime since the seminal 1965 manuscript of Hammersley and Welsh.

Spatial Analysis of Changes in Normalization Differences Vegetation Index in Protected Forest Areas of South Lore District, Poso Regency

Detection of changes in vegetation density generally uses the vegetation index parameter. The value of the vegetation index can provide information on the proportion of vegetation cover, live plant index, plant biomass, cooling capacity, and estimation of carbon dioxide absorption. This study aims to analyze changes in the level of vegetation density using Sentinel 2-A imagery in the protected forest area of South Lore District. This study used the method of calculating the Normalized Difference Vegetation Index (NDVI) to identify changes in density over 5 years. The results of the NDVI analysis are the largest in the range of -0.92960 to 0.871725. The vegetation density class in the Protected Forest Area of South Lore District in 2017 is in the dense class with an area of 15,322.24 Ha or around 47.66%, while the smallest in the non-vegetation class, which is 103.11 Ha or 0.32%, while the largest vegetation density class is in the Protected Forest Area of South Lore District in 2022, namely in the medium/quite dense class with an area of 19,948.18 Ha or 62.01% while the smallest in the non-vegetation class of 219.17 Ha or 0.68%. The largest increase in area was in the moderate/quite dense class of 4,892.33 Ha or 15.20% while the largest decrease in area was in the dense class with an area of 6,651.16 Ha or 20.67% of the total area of the Protected Forest Area of South Lore District.

Analysis of changes in vegetation density with sentinel-2a image on Malino sub-watershed

Changes in vegetation density, especially in sub-watershed areas, will continue to occur over time. This is due to increasing human activities impacting land use change. The Malino sub-watershed one part of the Jeneberang watershed, almost all located in Tinggimoncong District. Monitoring changes in vegetation density in the Malino Sub-watershed is carried out by utilizing remote sensing technology in the form of sentinel-2A imagery and using the NDVI type of vegetation index method. This study aims to determine the distribution and changes in vegetation density in the Malino Sub-watershed in 2017, 2019, and 2021. From the analysis results conducted using the NDVI vegetation index, the distribution of vegetation density in the Malino Sub-watershed is divided into four classes, namely the non- vegetation, sparse vegetation, medium vegetation, and dense vegetation. Changes in vegetation density in the Malino sub-watershed from 2017 to 2019 occurred in the dense vegetation density class of 2.21% or 192.69 ha, while the smallest occurred in the non-vegetation density class of 0.24% or 20. 97 ha. Meanwhile, the most significant change in vegetation density in the Malino sub-watershed from 2019 to 2021 occurred in the dense vegetation density class of 11.64% or 1,016.60 ha, while the smallest occurred in the non-vegetation density class of 0.12% or 10.46 ha.

The Analysis of Land Use Change of Mangrove Ecosystem in Langkat, North Sumatera, Indonesia

Mangroves are subtropical and warm temperate intertidal ecosystems that sustain the well-being of humans and wildlife through a variety of services. However, the existence of mangroves is presently threatened by alterations in land use. From 2010 to 2022, mangrove areas in Pasar Rawa Village, Gebang District, Langkat Regency, North Sumatra province have decreased significantly. The destruction of mangrove forests is primarily caused by the neighboring community's activities. The purpose of this study was to assess the level of damage and density of mangroves in Pasar Rawa Village, as well as to examine the perceptions of locals regarding the management of mangrove ecosystems. The investigation was carried out from January to March 2023 by distributing the questionnaires to the 286 respondents. Based on the vegetation analysis and indexing vegetation analysis through Landsat 7 and Landsat 8 image processing, the amount of mangrove areas in Pasar Rawa Village decreased by 52.97 ha between 2010 and 2022, with the majority of mangrove areas being converted into oil palm plantations and fish farming ponds. It is dominated by the dense class of the Normalized Difference Vegetation Index (NDVI) in 2022, despite the condition of the mangroves in Pasar Rawa Village still being classified as damaged during this recovery process. In addition, the outcome of public perception demonstrates that the management of the mangrove ecosystem is suitable and supports the capacity to create a sustainable mangrove ecosystem.

UTILIZATION OF LANDSAT 7 AND 8 IMAGES FOR MAPPING OF CHANGES IN THE NORTH COAST MANGROVE AREA, AESESA DISTRICT

&lt;p&gt;In 2019 there was damage to the mangrove forest which was converted into fish ponds by cutting down 200 hectares of mangrove land in Aeramo Village, which is the northern coastal area of Aesesa District, Nagekeo Regency. This study aims to map changes in mangrove areas and determine mangrove density values.This study uses the NDVI (Normalized Difference Vegetation Index) analysis technique to determine the density of mangrove vegetation in 2011, 2016 and 2021 using Landsat 7 and Landsat 8 imagery and uses an overlay analysis technique from the results of classifying mangrove density classes to determine changes in the area of mangrove areas. The results of the study show that the mangrove area on the north coast of Aesesa District shows density values that vary with The range of vegetation density index in Landsat 7 imagery in 2011 is from -0.573770464 to 0.54666682, in 2016 it has a vegetation density index range from -0.291984737 to 0.609456897 and in 2021 it has a vegetation density index range from -0.067266561 to 0.579148531 which is divided into 5 density classes namely; very rare, rare, moderate, rare and very tight. Based on the classification results, the results of changes in the mangrove area decreased to the medium class with forest area from 102.35 hectares to 66.86 hectares from 2011 to 2016 and experienced the largest increase in the very dense class in 2011-2016 where the forest area was in the very dense class. meeting from 128.86 hectares to 210.65 hectares.&lt;/p&gt;

ANALISIS PERUBAHAN TUTUPAN LAHAN DI HUTAN LINDUNG NGGALAK REGO PASCA KONSESI PERTAMBANGAN DI KECAMATAN REOK KABUPATEN MANGGARAI PROVINSI NUSA TENGGARA TIMUR

The mining concession in the Protected Forest Area that occurred in 1994 resulted in forest ecological damage. The damage occurs by dismantling trees and soil to take the manganese in them. This research was conducted in the Nnggalak Rego RTK 103 Protected Forest Area, Reok District, Manggarai Regency, East Nusa Tenggara Province with the aim of the research being to explain changes in land cover before and after mining in the Protected Forest Area. Data analysis used in this study is a quantitative analysis that utilizes remote sensing data using satellite imagery interpretation techniques. The method used in this research is OBIS (Object Based Image Segmentation) were analyzed to determine the area of ​​land cover in 1992, 2002, 2012 and 2022 as well as changes in land cover from 1992 to 2022 in mining concession areas. The results showed that there were changes in the area of ​​protected forest in 2002, 2012 and 2022. In 1992 the mining class was 0 Ha, the mining concession entered in 1994 the forest area changed, and in 2002 the mining class was 22.14 Ha or 3.87% , in 2012 61.69 Ha or 10.78%, in 2022, 15.45 Ha or 2.70%. The cessation of mining activities in 2013 to 2022 will result in a change in land cover class, namely the dense forest class will experience an increase in area due to succession and climate that occurs in the Manggarai district. Efforts to restore protected forests to their main function, it is suggested that reclamation and rehabilitation of post-mining land should be carried out.

Satellite based analysis of mangrove cover and density change in mangroves of Tulang Bawang District, Lampung Province, Indonesia

Abstract. Kaskoyo H, Hartati F, Bakri S, Febryano IG, Dewi BS, Nurcahyani N. 2023. Satellite based analysis of mangrove cover and density change in mangroves of Tulang Bawang District, Lampung Province, Indonesia. Biodiversitas 24: 3019-3028. Being a threatened ecosystem of the world, mangroves require regular monitoring to identify areas that need improvement in conservation measures. Considering this, the present study aimed to assess the mangrove cover and density change in mangrove forest of Tulang Bawang District, Lampung Province, Indonesia. Data was collected by remote sensing techniques using satellite imagery viz., Landsat 5 in 2000 and 2010 and Landsat 8 in 2020. It was then analyzed using remote sensing method, namely Object-Based Image Analysis techniques using the unguided classification method to obtain mangrove cover data; while mangrove density information was obtained using the Normalized Difference Vegetation Index algorithm. The results revealed the decrease in mangrove cover and density between 2000 and 2020. The mangrove cover decreased from 7529 ha in 2000 to 5551 ha in 2020. Furthermore, density of mangrove forest has also witnessed change from dense density class in 2000 to medium density class in 2020. From this result, it is evident that mangroves of East Rawajitu Sub-district are degrading and require focused efforts to prevent the degradation. Communities, entrepreneurs, and governments have different motives for utilizing mangrove forests that result in damage, so reasonable efforts are needed to manage mangrove forests sustainably.

Geometric similarity invariants of Cowen-Douglas operators

In 1978, M. J. Cowen and R.G. Douglas introduced a class of operators B_n(\Omega) (known as Cowen-Douglas class of operators) and associated a Hermitian holomorphic vector bundle to such an operator. They gave a complete set of unitary invariants in terms of the curvature and its covariant derivatives. At the same time they asked whether one can use geometric ideas to find a complete set of similarity invariants of Cowen-Douglas operators. We give a partial answer to this question. In this paper, we show that the curvature and the second fundamental form completely determine the similarity orbit of a norm dense class of Cowen-Douglas operators. As an application we show that uncountably many (non-similar) strongly irreducible operators in B_n(\mathbb{D}) can be constructed from a given operator in B_1(\mathbb{D}) . We also characterize a class of strongly irreducible weakly homogeneous operators in B_n(\mathbb{D}) .

Kajian Perubahan Kerapatan Vegetasi Pada Kawasan Taman Wisata Alam Ruteng Site Hutan Lok Pahar Akibat Perambahan Hutan

The activity of encroaching on the forest that occurs in the Ruteng Natural Park area site LokPahar forest to be used as agricultural land has had an impact, namely changes in the density ofvegetation in the forest area. This study aims to examine changes in vegetation density in the RutengNature Park area site Lok Pahar forest due to forest encroachment and examine efforts to prevent andcontrol forest encroachment. The results showed that there was a change in vegetation density in theRuteng Nature Park area based on the results of overlay NDVI (Normalized Difference VegetationIndex) on 2 Landsat 8 images, namely Landsat 8 Imagery in 2014 and in 2021. Changes in vegetationdensity in the Tourism Park area Alam Ruteng based on the results of the NDVI (NormalizedDifference Vegetation Index) overlay on 2 Landsat 8 images, namely Landsat 8 Imagery in 2014 andLandsat 8 Imagery 2021. The results of the 2014 Landsat Image NDVI calculation, the forest densityarea in the sparse vegetation density class is 49 .95 ha, and decreased by 1.29% in 2021 to 32.46 ha.In 2014 the medium-level vegetation density class was 384.39 ha, and in 2021 it increased by 2.74%,the medium-level vegetation density class was 421.64 ha. Then, in the dense vegetation density classof 926.73 ha, it decreased by 1.45% so that the area of dense vegetation density class became 906.97ha.

PENILAIAN KESESUAIAN LAHAN KEBUN RAYA BANUA BERBASIS DATA PENGINDERAAN JAUH

Banua Botanical Gardens is an urban forest located within the office complex of South Kalimantan Province. The Banua Botanical Gardens area occupies an area of approximately 100 hectares, right across from the office of the governor of South Kalimantan Province. The Banua Botanical Gardens area is an open expanse with a relatively flat land surface, red-yellow podsolid soil type, hard texture, and gravel. Before being made a botanical garden, this area was a former traditional diamond mining area that the community had abandoned several decades ago. The condition is mostly marginal land which is characterized by a very thin topsoil. The aims of this study are to analyze the land in the Banua Botanical Gardens based on data from the interpretation of Sentinel 2 Imagery in 2021 and Drone coverage on October 14, 2021; 2) to recommend the development of vegetation in the Banua Botanical Gardens based on the results of the land suitability analysis. The approach used in this research is quantitative methods and descriptive analysis. The object of this research is the soil sample at 6 (six) points according to the density class and the type of land cover along with its spatial parameters, namely area and length, tree inventory data, and market prices. The results showed that the land suitability assessment in dense land class was S1 for sengon plants, S2 rc nr for acacia plants, N nr for trembesi plants, and S2 rc nr for rubber plants. The results of land suitability assessment on medium land class S2 nr for sengon plants, S2 rc nr for acacia plants, N nr for trembesi plants, and S3 nr for rubber plants. The results of the land suitability assessment on rare land classes were S2 nr for sengon plants, S2 rc nr for acacia plants, N nr for trembesi plants, and S3 nr for rubber plants. Recommendations for the development of vegetation in the Banua Botanical Gardens are carried out with improvements to the characteristics of the land.

Mapping class groups with the Rokhlin property

We classify the connected orientable 2-manifolds whose mapping class groups have a dense conjugacy class. We also show that the mapping class group of a connected orientable 2-manifold has a comeager conjugacy class if and only if the mapping class group is trivial.

Land cover change across 45 years in the world’s largest mangrove forest (Sundarbans): the contribution of remote sensing in forest monitoring

ABSTRACT This study explored the land use land cover (LULC) change over 45 years (1975 -2020) in the world’s largest mangrove forest, Sundarbansusing Landsat imagery. LULC maps were created with same-season imagery with the lowest cloud cover at four intervals: 1975, 1990, 2005, and 2020. Maximum likelihood classification (MLC) was applied to assign five classes: dense forest, moderate forest, sparse forest, barren land, and water body. Accuracy assessment was carried out with 250 control points for each year resulting in overall accuracy and kappa coefficient ranging from 84.8% to 90.0% and 0.81 to 0.87, respectively. Results show dense forest at its highest cover in 1975 and then decreasing by an estimated annual rate of 1.3% from 1975 to 2020, but not consistently. Dense forest class mostly turned moderate and sparse; most of the sparse forest class turned to barren land. Most of the barren lands were located near the boundary between forest and human settlement, and these two classes were more frequent in the Indian part of Sundarbans than in the Bangladesh part. The conclusion is that the time-series of remote sensing data can validly support effective forest management by identifying space and time changes in the biodiversity of Sundarbans.

Enumeration for FO Queries over Nowhere Dense Graphs

We consider the evaluation of first-order queries over classes of databases that are nowhere dense . The notion of nowhere dense classes was introduced by Nešetřil and Ossona de Mendez as a formalization of classes of “sparse” graphs and generalizes many well-known classes of graphs, such as classes of bounded degree, bounded tree-width, or bounded expansion. It has recently been shown by Grohe, Kreutzer, and Siebertz that over nowhere dense classes of databases, first-order sentences can be evaluated in pseudo-linear time (pseudo-linear time means that for all \( \epsilon \) there exists an algorithm working in time \( O(n^{1+\epsilon }) \) , where \( n \) is the size of the database). For first-order queries of higher arities, we show that over any nowhere dense class of databases, the set of their solutions can be enumerated with constant delay after a pseudo-linear time preprocessing. In the same context, we also show that after a pseudo-linear time preprocessing we can, on input of a tuple, test in constant time whether it is a solution to the query.

Mapping mangrove distribution using remote sensing technology in Harapan, Kelapa and Pamegaran Seribu Islands National Park

The existence of mangrove ecosystems is increasingly threatened due to the rapid development of tourist destinations and the increasing number of residents in Harapan, Kelapa and Pamegaran island, so that monitoring of mangrove ecosystems is necessary. The purpose of the research is to map the distribution of mangroves using remote sensing technology in Harapan, Kelapa and Pamegaran island. The field survey was conducted on April 1-10, 2021, taking 189 sample points using a hemispherical photography method. The maximum likelihood classification method is used to classify mangrove and non-mangrove vegetation. Normalized Difference Vegetation Index (NDVI) is an algorithm used to calculate vegetation indexes from satellite imagery. The Sentinel-2A image was classified into 3 classes of mangrove density, namely dense, moderate, and rare density classes, with the dominant class being the dense class. The total mangrove area on Pamegaran Island in 2015 amounted to 1.81 ha and the total mangrove area in 2021 amounted to 2.97 ha. The area of mangrove distribution in Harapan and Kelapa Island in 2015 amounted to 4.1 ha and in 2021 amounted to 6.56 ha. Mangrove density classification accuracy test using confusion matrix showed an accuracy of 82.95%.

Green open space analysis in West Binjai, North Sumatra

West Binjai is a sub-district located in Binjai City, North Sumatra. Green Open Space is also part of the Binjai city’s planning scheme which has many benefits for the community and the environment. This research used Normalized Difference Vegetation Index (NDVI) analysis and NDVI value classification results in the distribution of vegetation density. Analysis of changes in vegetation density was carried out between 2015 and 2020 in West Binjai. The largest change in the area of vegetation density classes in the West Binjai between 2015 and 2020 was the increase in the area of the high dense class to 19.13%. The sub-district has green open spaces in the form of sub-district parks, public cemeteries, road green lane, river bank and private green open spaces. These green open spaces were in the low dense, medium, dense and high dense classes. There is a need for rearrangement of green open spaces, especially those within low dense class. Replanting trees are also essential to increase the quality of the green area. Improving the quality of green space will lead to the enhancement of quality of environment.

Vegetation density analysis using normalized difference vegetation index in East Jakarta, Indonesia

East Jakarta, which is included in the DKI Jakarta Province, continues to grow in population. As a result, the demand for settlement land increases. The presence of plants is critical for environmental equilibrium. The purpose of this study was to determine the vegetation density and its variations in East Jakarta year 2020. The method used the Normalized Difference Vegetation Index (NDVI) analysis and classification. In 2020, the highest NDVI value in East Jakarta was 0.1–0.2, covering 7,952.64 ha (43.07 %) of the entire area, while the lowest value was >0.6, covering 0.06 ha of the total area. The highest vegetation density class in East Jakarta was low dense class, accounting for 7,951.26 ha (43.06 percent) of the whole area, while the lowest density class was under high dense class accounted for 1,116.41 ha (6.04 percent) of the total area. In terms of green open space, there were a city park, a cemetery, a green lane on a road, and a river bank. The municipal park was classified as dense, while the rest were classified as medium dense. The presence of trees within the green space has aided in the area’s vegetation density. It also refers to the role of open green space in enhancing the community’s life and environment’s quality. The importance of educating and guiding the surrounding community about the benefits of vegetation or green open space, then replanting less vegetated land, as well as an integrated land use planning and implementation.The first section in your paper

Vegetation distribution analysis of green open space in Binjai Timur, North Sumatera Province using normalized difference vegetation index

As the city grows, more and more vegetated land is converted to non-vegetated land. This also occurred at Binjai, a city in the North Sumatera Province, Indonesia. The aim of this study was to examine the urban vegetation cover and its changes between 2015 and 2019. The research was carried out in Binjai Timur, which is one of Binjai’s sub-districts. The distribution of vegetation density was measured using the Normalized Difference Vegetation Index (NDVI) value classification. The decrease in the dense class to 10.08 percent was the most significant change in vegetation density class between 2015 and 2019. This was followed by an 8.87 percent increase in the high-density class. This indicates that there is an area with vegetation density increased from lower density to high density. The district has green open spaces in the form of a neighborhood park, cemetery, sub-district park, greenbelt along the road and river, and house yards, according to the field check. These green open spaces were located in low and medium-density areas. The findings suggest that planting trees in those locations and arrangement of vegetation within parks could improve its quality and function. For good quality of urban environments, it is optimizing the use of house yards as vegetated land and boosting green open space quality is required.