Development of spontaneous vegetation on reclaimed land in Singapore measured by NDVI

Population and economic growth in Asia has led to increased urbanisation. Urbanisation has many detrimental impacts on ecosystems, especially when expansion is unplanned. Singapore is a city-state that has grown rapidly since independence, both in population and land area. However, Singapore aims to develop as a ‘City in Nature’, and urban greenery is integral to the landscape. While clearing some areas of forest for urban sprawl, Singapore has also reclaimed land from the sea to expand its coastline. Reclaimed land is usually designated for future urban development, but must first be left for many years to stabilise. During the period of stabilisation, pioneer plant species establish, growing into novel forest communities. The rate of this spontaneous vegetation development has not been quantified. This study tracks the temporal trends of normalized difference vegetation index (NDVI), as a proxy of vegetation maturity, on reclaimed land sensed using LANDSAT images. Google Earth Engine was used to mosaic cloud-free annual LANDSAT images of Singapore from 1988 to 2015. Singapore’s median NDVI increased by 0.15 from 0.47 to 0.62 over the study period, while its land area grew by 71 km2. Five reclaimed sites with spontaneous vegetation development showed variable vegetation covers, ranging from 6% to 43% vegetated cover in 2015. On average, spontaneous vegetation takes 16.9 years to develop to a maturity of 0.7 NDVI, but this development is not linear and follows a quadratic trajectory. Patches of spontaneous vegetation on isolated reclaimed lands are unlikely to remain forever since they are in areas slated for future development. In the years that these patches exist, they have potential to increase urban greenery, support biodiversity, and provide a host of ecosystem services. With this knowledge on spontaneous vegetation development trajectories, urban planners can harness the resource when planning future developments.

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An entropy-based investigation on the river recovery potential in a regulated river basin

10.5194/egusphere-egu21-9362 ◽

2021 ◽

Author(s):

Chandan Pradhan ◽

Suman Padhee ◽

Subashisa Dutta ◽

Rishikesh Bharti

Keyword(s):

Boundary Conditions ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Google Earth ◽

Regulated River ◽

System State ◽

Landsat Images ◽

Regulated Rivers ◽

Mahanadi River ◽

Recovery Potential

River recovery is the process that describes the capacity of the river to adjust to the limiting boundary conditions. In the regulated rivers, altered flow-sediment regime controls the trajectory of adjustments along the geomorphic and vegetative attributes. The present study is focused on recovery potential assessment of Mahanadi River, which shows a gradual emergence of in-channel vegetation in the post-dam period. The study area encompasses (i) 10 km reach of Mahanadi River (M1) having bedrock exposed, anabranching channel pattern and (ii) 102 km reach of Ong river (O1) with alluvial, compound channel form. In this study, Google Earth Engine cloud computing platform is used to process the Landsat images (1980-2010) and vegetation, water, and floodplain geomorphic classes are derived by Normalized Difference Vegetation Index (NDVI) and modified Normalized Difference Water Index (mNDWI). Finally, the intensity disorder index (IDI) is computed to represent the &#8216;system state&#8217; in the post-monsoon periods and the influence of vegetation growth on the channel recovery. The results show that M1 is relatively stable, with cumulative vegetation area increased from 2% in 1980 to 8% in 2010. However, O1 demonstrates an accelerated increase in vegetation area i.e., 10% in 1980 to 30% in 2010. The system state (IDI) varies between 0.2 and 0.6 and follows a decreasing trend along M1 and O1. The findings establish that both regulated reaches may approach channel recovery in the near future, and prevailing boundary conditions indirectly influence the rate and direction of IDI.&#160;&#160;&#160;&#160;&#160; &#160;&#160;

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Study of the dynamic natural forest groups of Lebanese rice Cedrus libani A. Richard in Syria: دراسة ديناميكية المجموعات الحرجية الطبيعية للأرز اللبناني Cedrus libani A. Richard في سورية

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.a090917 ◽

2017 ◽

Vol 1 (3) ◽

Author(s):

Abeer Ahmed Ibrahim

Keyword(s):

Health Status ◽

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Google Earth ◽

Landsat Images ◽

Cedrus Libani ◽

Ndvi Index ◽

Natural Stands

The aim of this study is to assess the dynamics of the forest stands of Cedrus libani A. Richard in its only natural area in Syria - Slenfeh and Jawbat Burghal. The spatial and temporal change of the natural stands of Cedrus libani during the period 1984-2011 and their health status during the period 1984-2014 were assessed using Remote Sensing and Geographic Information Systems (GIS). A high-resolution satellite image was used in 2011 and 17 Landsat images Landsat various sensors; Landsat_4, 5 and 8 and the NDVI Index were used during 1984-2014, high-resolution Google Earth (2 m). The direction and amount of the NDVI index of the Cedrus libani samples studied during the years of study were determined using ANOVA in the SPSS. The results showed a clear decrease in the Cedrus libani area size in both study sites Slenfeh and Jawbat Burghal in 2011 compared to 1984. The results also revealed a signiﬁcant increase trend of Normalized Difference Vegetation Index (NDVI) for natural stands of Cedrus libani in Slenfeh and Jawbat Burghal during 1984-2014, which reflects the good health status of the natural Cedar stands in Syria.

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Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

Remote Sensing ◽

10.3390/rs13010051 ◽

2020 ◽

Vol 13 (1) ◽

pp. 51

Author(s):

Bryn E. Morgan ◽

Jonathan W. Chipman ◽

Douglas T. Bolger ◽

James T. Dietrich

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat Imagery ◽

Temporal Trends ◽

Landsat 8 ◽

Landsat Images ◽

Fractional Vegetation Cover ◽

Aerial Vehicle

Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

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A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.3.22 ◽

2021 ◽

pp. 912-926

Author(s):

Fadel Abbas Zwain ◽

Thair Thamer Al-Samarrai ◽

Younus I. Al-Saady

Keyword(s):

Remote Sensing ◽

Land Degradation ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Arid Zone ◽

Sand Dunes ◽

Risk Index ◽

Anthropogenic Factors ◽

Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

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Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v25i8.25 ◽

2021 ◽

Vol 25 (8) ◽

pp. 1449-1452

Author(s):

P.A. Ukoha ◽

S.J. Okonkwo ◽

A.R. Adewoye

Keyword(s):

Time Series ◽

Change Point ◽

Vegetation Index ◽

Google Earth ◽

Change Point Detection ◽

Mcmc Methods ◽

Landsat Images ◽

Southwestern Nigeria ◽

Forest Reserve ◽

Point Detection

This study uses satellite acquired vegetation index data to monitor changes in Akure forest reserve. Enhanced Vegetation Index (EVI) time series datasets were extracted from Landsat images; extraction was performed on the Google Earth Engine (GEE) platform. The datasets were analyzed using Bayesian Change Point (BCP) to monitor the abrupt changes in vegetation dynamics associated with deforestation. The BCP shows the magnitude of changes over the years, from the posterior data obtained. BCP focuses on changes in the long‐range using Markov Chain Monte Carlo (MCMC) methods, this returns posterior probability at > 0.5% of a change point occurring at each time index in the time series. Three decades of Landsat data were classified using the random forest algorithm to assess the rate of deforestation within the study area. The results shows forest in 2000 (97.7%), 2010 (89.4%), 2020 (84.7%) and non-forest increase 2000 (2.0%), 2010 (10.6%), 2020 (15.3%). Kappa coefficient was also used to determine the accuracy of the classification.

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Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs13224683 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4683

Author(s):

Masoumeh Aghababaei ◽

Ataollah Ebrahimi ◽

Ali Asghar Naghipour ◽

Esmaeil Asadi ◽

Jochem Verrelst

Keyword(s):

Vegetation Index ◽

Google Earth ◽

Optimal Time ◽

Quantitative Detection ◽

Landsat 8 ◽

Vegetation Types ◽

Landsat Images ◽

Multi Temporal ◽

Google Earth Engine ◽

Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

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Monitoring of Inland Surface Water Quality Using Remote Sensing on the Example of Wigry Lake

E3S Web of Conferences ◽

10.1051/e3sconf/20186300017 ◽

2018 ◽

Vol 63 ◽

pp. 00017

Author(s):

Michał Lupa ◽

Katarzyna Adamek ◽

Renata Stypień ◽

Wojciech Sarlej

Keyword(s):

Water Quality ◽

Surface Water ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Surface Water Quality ◽

Lake Area ◽

Landsat Images ◽

Secchi Disc ◽

Water Index ◽

Normalized Difference Water Index

The study examines how LANDSAT images can be used to monitor inland surface water quality effectively by using correlations between various indicators. Wigry lake (area 21.7 km2) was selected for the study as an example. The study uses images acquired in the years 1990–2016. Analysis was performed on data from 35 months and seven water condition indicators were analyzed: turbidity, Secchi disc depth, Dissolved Organic Material (DOM), chlorophyll-a, Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The analysis of results also took into consideration the main relationships described by the water circulation cycle. Based on the analysis of all indicators, clear trends describing a systematic improvement of water quality in Lake Wigry were observed.

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Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

Remote Sensing ◽

10.3390/rs12193170 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3170

Author(s):

Zemeng Fan ◽

Saibo Li ◽

Haiyan Fang

Keyword(s):

Climate Change ◽

Human Activities ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Productivity ◽

Regression Tree ◽

Google Earth ◽

Classification And Regression Tree ◽

Support Vector ◽

Driving Mechanisms

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.

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Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040257 ◽

2020 ◽

Vol 9 (4) ◽

pp. 257 ◽

Cited By ~ 3

Author(s):

Kiwon Lee ◽

Kwangseob Kim ◽

Sun-Gu Lee ◽

Yongseung Kim

Keyword(s):

Open Source ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Atmospheric Correction ◽

Urban Forestry ◽

Google Earth ◽

Landsat 8 ◽

Surface Reflectance ◽

Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

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Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.5663 ◽

2012 ◽

Vol 518-523 ◽

pp. 5663-5667

Author(s):

Shi Wei Li ◽

Ji Long Zhang ◽

Jian Sheng Yang

Keyword(s):

Remote Sensing ◽

Water Conservation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Image ◽

Google Earth ◽

Vegetation Coverage ◽

Cultivated Land ◽

Land Occupation ◽

Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

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