scholarly journals Spatio-temporal pattern of urban forest vegetation density, Medan Baru city, Indonesia

2021 ◽  
Vol 918 (1) ◽  
pp. 012021
Author(s):  
Samsuri ◽  
C A B Ginting ◽  
A Zaitunah ◽  
A Susilowati
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Forest ◽  
Vegetation Indices ◽  
Stand Density ◽  
Development Planning ◽  
Vegetation Density ◽  
Dense Vegetation ◽  
Spatio Temporal

Abstract The growth of urban areas and the population generally requires the guarantee of a healthy and comfortable environment. The expansion of physical developments and urban areas, year after year, can no longer support human existence. In Indonesia, the city should have at least 10% of its surface area committed to private urban forest and 20% for public urban forest. Jakarta is Indonesia’s largest city, and has only 9.98% urban forest coverage. Medan Baru city is facing the same issue, as it continues to grow year after year. The population requires a comfortable environment, which includes safe drinking water and clean, fresh air. As a result, vegetation is an important component of Medan Baru sub-district that offers numerous benefits. It is necessary to conduct research on the analysis of vegetation density in the Medan Baru, using vegetation indices such as Normalized Difference Vegetation Index (NDVI). The research aimed to analyze vegetation density change and mapping the vegetation density of Medan Baru city. The research found the largest area was relatively dense vegetation, about 262.00 hectares (47.87%). The research also found a decrease in urban forest quality, indicated by an increase in the sparse density class of 41.90 hectares and a decrease in the relative-dense vegetation class with 51.65 hectares. This reduction of vegetation density will reduce the urban forest quality by influencing urban forest capability in absorbing carbon dioxide and alleviating the oxygen productivity volume. Areas with lower stand density must be considered in future urban development planning. Moreover, decrease in vegetation density and urban forest area should be a primary consideration in Medan urban forest management.

scholarly journals An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

2021 ◽  
Vol 13 (4) ◽  
pp. 766
Author(s):  
Yuanmao Zheng ◽  
Qiang Zhou ◽  
Yuanrong He ◽  
Cuiping Wang ◽  
Xiaorong Wang ◽  
...  
Keyword(s):  
Urban Development ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Development Planning ◽  
Urban Land ◽  
Sustainable Urban Development ◽  
Land Use Patterns ◽  
Water Index ◽  
Nighttime Light

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

scholarly journals A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

2018 ◽  
Vol 37 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Khalid Mahmood ◽  
Zia Ul-Haq ◽  
Fiza Faizi ◽  
Syeda A. Batol
Keyword(s):  
Hazard Assessment ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Vegetation Indices ◽  
Temporal Analysis ◽  
Zone Size ◽  
Vegetation Health ◽  
Spatio Temporal ◽  
Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

scholarly journals A Calculation and Compiling Models of Land Cover Quality Index 2019 uses the Geographic Information System in Pariaman City, West Sumatra Province, Indonesia

2019 ◽  
Vol 8 (3) ◽  
pp. 6406-6411
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Quality Index ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Director General ◽  
Environmental Damage ◽  
Vegetation Density ◽  
Forest Region

The purpose of calculation and compiling the Land Cover Quality Index (LCQI) is to evaluate the value of natural and environmental resources based on land cover conditions in an administrative region such as city, regency and province in Indonesia referring to the Regulation Director General of Pollution Control and Environmental Damage Number P.1/PPKL/PKLA.4/2018. The analytical method used in the calculation of the Normalized Difference Vegetation Index (NDVI), the Maximum likelihood classification approach, and the preparation of LCQI calculation methods based on 1) sufficiency area (forest region) and forest cover at minimal 30% on rivers and islands; 2) Ability and suitability of land minimal 25%; and 3) a link with the direction of land use in urban areas of at minimal 30%. The results showed the vegetation density index value in Pariaman city was classified as a good category with a value of 0.474903 μm, the results of a land cover classification in Pariaman City with the largest region are found in mixed gardens land of 2,736.57 ha or 37%. Whereas the smallest region is found in cypress vegetation land as a greenbelt at the coastal border 12.06 ha or 0,16%. and the results of the LCQI calculation indicate the LCQI value in 2019 (24,06) which is in the alert classification (<50). The increase in land cover outside the forest region is mainly directed at increasing green open space because Pariaman City does not have natural forest which are vulnerable to changes in land cover because of its high population density

scholarly journals VENµS-Derived NDVI and REIP at Different View Azimuth Angles

2022 ◽  
Vol 14 (1) ◽  
pp. 184
Author(s):  
Manuel Salvoldi ◽  
Yaniv Tubul ◽  
Arnon Karnieli ◽  
Ittai Herrmann
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Crop ◽  
Bidirectional Reflectance ◽  
Reflected Light ◽  
Change Vector ◽  
The Stability ◽  
Bidirectional Reflectance Distribution

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles.

scholarly journals Spatiotemporal Assessment of Vegetation Indices and Land Cover for Erbil City and Its Surrounding Using Modis Imageries

2017 ◽  
Vol 10 (1-2) ◽  
pp. 31-39 ◽  
Author(s):  
Shwan O. Hussein ◽  
Ferenc Kovács ◽  
Zalán Tobak
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Natural Vegetation ◽  
Visual Interpretation ◽  
Surrounding Areas ◽  
The Impact

Abstract The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), in addition land cover in and around Erbil city area between the years 2000 and 2015. MODIS satellite imagery and GIS techniques were used to determine the impact of urbanization on the surrounding quasi-natural vegetation cover. Annual mean vegetation indices were used to determine the presence of a spatiotemporal trend, including a visual interpretation of time-series MODIS VI imagery. Dynamics of vegetation gain or loss were also evaluated through the study of land cover type changes, to determine the impact of increasing urbanization on the surrounding areas of the city. Monthly rainfall, humidity and temperature changes over the 15-year-period were also considered to enhance the understanding of vegetation change dynamics. There was no evidence of correlation between any climate variable compared to the vegetation indices. Based on NDVI and EVI MODIS imagery the spatial distribution of urban areas in Erbil and the bare around it has expanded. Consequently, the vegetation area has been cleared and replaced over the past 15 years by urban growth.

scholarly journals ANALISIS TINGKAT KEHIJAUAN HUTAN DAERAH PERTAMBANGAN SAWAHLUNTO DENGAN METODE NDVI BERDASARKAN CITRA LANDSAT TAHUN 2006-2016

2018 ◽  
Vol 3 (1) ◽  
pp. 37-46
Author(s):  
Bowo Eko Cahyono ◽  
Yazella Feni Frahma ◽  
Agung Tjahjo Nugroho
Keyword(s):  
Land Cover ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Mining Area ◽  
Vegetation Density ◽  
Dense Vegetation ◽  
Value Range ◽  
Landsat Satellite

Abstrak Pembukaan lahan hutan yang dijadikan lokasi pertambangan merupakan salah satu kegiatan yang dapat merubah jenis tutupan lahan atau sering disebut dengan konversi lahan. Salah satu daerah yang telah mengalami konversi lahan tersebut adalah Sawahlunto. Konversi lahan yang tidak menggunakan prinsip kelestarian lingkungan dapat mengakibatkan banyak hal negatif misalnya degradasi atau penurunan kualitas hutan. Tujuan dari penelitian ini adalah melakukan analisis tingkat degradasi hutan daerah pertambangan Sawahlunto tahun 2006 sampai 2016. Penelitian ini menggunakan teknologi penginderaan jauh berbasis citra satelit landsat. Citra satelit landsat ini diklasifikasikan dengan metode Normalized Difference Vegetation Index (NDVI) berdasarkan kerapatan vegetasi. Kemudian hasil klasifikasi ini dibuat dalam bentuk pemetaan. Klasifikasi pertama dikategorikan menjadi dua yakni hutan dan non hutan. Hasil yang didapatkan dari penelitian ini menunjukkan bahwa terjadi perubahan tutupan lahan yang semula hutan menjadi non hutan meningkat sebesar 7,5% selama kurun waktu sepuluh tahun. Klasifikasi selanjutnya yakni berdasarkan enam kategori yakni vegetasi sangat rapat, rapat, cukup rapat, non vegetasi 1, 2 dan 3. Dari klasifikasi ini, juga terlihat perubahan nilai NDVI maksimum maupun minimumnya. Tahun 2006 memiliki kisaran nilai NDVI maksimum 0,71 dan tahun 2016 memiliki kisaran nilai NDVI maksimum 0,56. Hal ini mengidentifikasi bahwa tingkat kehijauan yang ada di daerah pertambangan Sawahlunto menurun. Kata Kunci : degradasi, hutan, landsat, ndvi, klasifikasi, Sawahlunto.  Abstract The clearing of forest land that is used as a mining site is one of the activities that can change the type of land cover or often called land conversion. One of the forest areas that convert the land is Sawahlunto. Conversion of land that does not use the principles of environmental sustainability can lead to many negative things one of which is the degradation. The purpose of this research is to analyze the level of forest degradation of Sawahlunto mining area in 2006 until 2016. This research uses a remote sen sing technology based on landsat satellite imagery. This landsat satellite image is classified by Normalized Difference Vegetation Index (NDVI) method based on vegetation density. Then the results of this classification is made in the form of mapping. The first classification is categorized into two namely forest and non forest. The results obtained from this study indicate that a change in land cover from forest to non-forest increased by 7.5% over a period of ten years. The next classification is based on six categories namely very dense vegetation, dense vegetation, fairly dense, non vegetation 1, 2 and 3. From this classification, also seen the change in NDVI maximum and minimum value. The year 2006 has a maximum NDVI value range of 0.71 and 2016 has a maximum NDVI value range of 0.56. This identifies that the existing greenness in the mining area of Sawahlunto is decreasing.  Keyword : degradation, forest, landsat, ndvi, classification, Sawahlunto.

scholarly journals CLASSIFYING AND MAPPING OF VEGETATED AREA IN AL- BAHA REGION, SAUDI ARABIA USING REMOTE SENSING. I. EXTENT AND DISTRIBUTION OF GROUND VEGETATED COVER CATEGORIES.

2020 ◽  
pp. 75-80
Author(s):  
Abdullah Saleh Al-Ghamdi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Type ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Vegetation Mapping ◽  
Primary Objective ◽  
High Density ◽  
Vegetation Density

Classifying and mapping vegetation is an important technical task for managing natural resources; the primary objective of the vegetation-mapping inventory is to produce high quality, standardized maps and associated data sets of vegetation. Satellite remote sensing has proven to be effective technology for mapping forest vegetation at the landscape to regional scale. In the remote sensing technique, vegetation density can be directly indicated by vegetation indices. Although there are several vegetation indices, the most widely used is the Normalized Difference Vegetation Index (NDVI), formulated by transforming raw satellite data into NDVI values, ranging from -1 to 1. NDVI enables the creation of images and other products that provide a rough measure of vegetation type, amount, and condition on land surfaces. The results show that medium to high density vegetation is mostly found in the central part of Al-Baha region separating the highlands and lowlands. The relationship study between NDVI and vegetation cover percentage in this study depicts an NDVI value of only 0.20–1.00, which indicates that vegetation covers over 60% of Al-Baha. This is probably because vegetation here may not only comprise trees but also other plant forms such as herbs and shrubs. However, only 862.5 km2 (7.7%) of Al-Baha is covered with medium-high density vegetation, found mainly at the 6 –15km width horizontal central belt (in the Al-Mandaq, Al-Baha, and south Baljurashi districts) along a high, foggy mountainous plateau. Conversely, about 65% of Al-Baha region has very low to no vegetation density; vegetation is found extensively in the Tihama low plain towards the Red Sea and in the north-eastern desert plain. This study has provided a comprehensive report on vegetation mapping in the Al-Baha region.

scholarly journals Space-temporal evaluation of changes in temperature and soil use and cover in the metropolitan region of baixada santista

2019 ◽  
Vol 35 (5) ◽  
Author(s):  
Laurizio Emanuel Ribeiro Alves ◽  
Washington Luiz Félix Correia Filho ◽  
Heliofábio Barros Gomes ◽  
José Francisco de Oliveira-Junior ◽  
Fabio de Oliveira Sanches ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Soil Surface ◽  
Urban Heat Islands ◽  
Metropolitan Region ◽  
Vegetation Density ◽  
Biophysical Parameters ◽  
Dense Vegetation

The Metropolitan Region of Baixada Santista (MRBS) harbors one of the main port areas of Brazil: the Port of Santos. Due to the accelerated urban development in this region, the monitoring of biophysical parameters is fundamental. Therefore, this paper aims to i) estimate the soil surface temperature (Ts) and identify the Urban Heat Islands (UHI) formation; and ii) compare the Ts and the normalized difference vegetation index (NDVI) for MRBS from 1986 to 2016 using Landsat 5 and 8 images. Remote sensing tools are essential to meet the objectives of this work for providing both the spatial and temporal evaluation of a region. The spatial analysis was based on the NDVI to evaluate the vegetation density and size from five previously established classes (i.e., water bodies, urban grid, exposed soil and road corridors, shrub, and dense vegetation). The NDVI mapping showed a significant reduction in the cover area referred to the dense vegetation class (91.7%), while the urban grid category increased by 29.4%, resulting from the urban expansion and green cover reduction over the region during this period. Surface temperature thematic maps showed high-temperature values related to increased urbanization and decreased rainfall. Moreover, an 8°C rise in surface temperature over the last 30 years was registered due to the regional development, which has replaced natural soils by anthropic materials and reduced dense vegetation. This phenomenon has resulted in the formation and intensification of UHI, especially after the 2000s.

scholarly journals ULTRA-HIGH SPATIAL RESOLUTION UAV-BASED IMAGERY TO PREDICT BIOMASS IN TEMPERATE GRASSLANDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 443-447 ◽  
Author(s):  
U. Lussem ◽  
A. Bolten ◽  
J. Menne ◽  
M. L. Gnyp ◽  
G. Bareth
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Low Cost ◽  
Rapid Assessment ◽  
Vegetation Indices ◽  
Multispectral Data ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Cost Efficient

<p><strong>Abstract.</strong> Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site is an experimental grassland field in Germany with four nitrogen fertilizer levels. Biomass yield and UAV-based data for the first cut in May 2018 was analysed in this study. From the RGB-data the Plant Pigment Ratio Index (PPR) and the Normalized Green Red Difference Index (NGRDI) and from the multispectral data the Normalized Difference Vegetation Index (NDVI) are calculated as predictors for dry biomass yield. The NGRDI and NDVI perform moderately well with cross-validation R<sup>2</sup> of 0.57 and 0.63 respectively, while the PPR performs better with an R<sup>2</sup> of 0.70. These results indicate the potential of low-cost UAV-based methods for rapid assessment of grasslands.</p>

scholarly journals A Spatio-Temporal Analysis of Rainfall and Drought Monitoring in the Tharparkar Region of Pakistan

2020 ◽  
Vol 12 (3) ◽  
pp. 580
Author(s):  
Muhammad Usman ◽  
Janet E. Nichol
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Variability ◽  
Vegetation Indices ◽  
Biomass Productivity ◽  
Temporal Analysis ◽  
Annual Rainfall ◽  
Spatial Estimates ◽  
Spatio Temporal ◽  
Rainfall Estimates

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall.

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