Distribution Of Vegetation Density Research Articles

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.

Analysis of land use change using normalized difference vegetation index (NDVI) as input parameter to flood risk assessment of Krueng Kluet watershed in South Aceh, Indonesia

Flood events have been escalating in the last decade at the Kluet River, one of the major rivers in the South Aceh district, Aceh Province, Indonesia. The frequent flood events have made remarkable changes to the river morphology including river meander and delta particularly at the downstream. One of the interplaying parameters contribute to flood events is the land use changes in the watershed. This study aimed to analyze the land use changes of Krueng Kluet Watershed using the Normalized Difference Vegetation Index (NDVI). The satellite image data series were obtained to remotely monitor the land use change over 20 years period (1998-2018). NDVI was used in classifying the vegetation density distribution value of the Krueng Kluet Watershed. The output of the study explained that there were increase in the plantation, bareland, grassland, water and settlement areas, while in contrary the forest area and bush. There was an increase by 8.05%, 0.15%, 0.14%, 0.10%, and 0.07% respectively to the plantation, bareland, grassland, water, settlements/urban areas, during the period of 1998-2018. On the other hand, the forest and bush were significantly decreased by 8.25%, and 0.3, respectively in the same period. Overall, the land use changes have also increased the Curve Number (CN) of Krueng Kluet Watershed from 61.8 in 1998 to 62.1 in 2008 and to 62.5 in 2018. This suggests the land use change as an important parameter to take into account in flood risk assessment of the Krueng Kluet Watershed

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.

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.

Two novel methods for field measurements of hydrodynamic density of floodplain vegetation using terrestrial laser scanning and digital parallel photography

Hydrodynamic vegetation density, the sum of the projected plant area per unit volume, is an important parameter for floodplain flow models. This paper compares two novel techniques to quantify this parameter in the field: terrestrial laser scanning (TLS) and digital parallel photography (PP). Separate field reference data were collected for the two methods, which consisted of (1) a stem map of 650 trees, aggregated into 23 plots in a single forest patch, (2) 17 manually measured forest plots in two floodplains. PP consists of a series of digital photographic images of vegetation against a contrasting background. The centre columns of all images were merged into a single composite parallel image. This mosaic was thresholded to determine the fractional coverage of the vegetation, which is converted to vegetation density using the optical point quadrat method. A sensitivity analysis proved that PP is insensitive to small errors on the selected number of centre columns, photograph spacing should not exceed 20 cm, photograph resolution is important and the plot depth should be measured accurately. TLS was carried out using a Leica HDS3000 time‐of‐flight laser scanner. Data processing of TLS data consisted of slicing the points around breast height. In a polar grid the vegetation density was predicted using the optical point quadrat method, corrected for missing points. Both methods were compared to the field reference data. PP (EF = 0.99; bias = 8.4×10−5 m−1) showed a higher modelling efficiency than the TLS method (EF = 0.63; bias = 0.015 m−1). An advantage of the TLS method is the ability to provide a detailed 2‐dimensional or even 3‐dimensional distribution of vegetation density. PP is cheaper, faster, and data processing is limited. We conclude that TLS and PP are two complementary techniques that show high accuracies for field measurements of vegetation density.

Time changes in spatial structure of surface variability in the Southern Great Plains

This study focuses on time changes in the spatial structure of vegetation density and land surface temperature derived from satellite remote sensing in the Southern Great Plains. In this region the spatial distribution of vegetation density for a diverse land surface was anisotropic and statistically heterogeneous. As a drying period progressed, heterogeneity decreased slightly and the variability became more dependent on smaller length scales. Within similar land cover classifications, vegetation density was approximately homogeneous with a correlation scale up to 40 km. In the same region, land surface temperature exhibited anisotropy and heterogeneity with dramatic changes in structure from day-to-day. Land cover did not exert a significant control on the land surface temperature structure.