scholarly journals Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

2020 ◽  
Vol 4 (1) ◽  
pp. 256
Author(s):  
Hai-Hoa Nguyen ◽  
Lan Thi Ngoc Tran ◽  
An Thanh Le ◽  
Nghia Huu Nghia ◽  
Linh Vo Khanh Duong ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Local Community ◽  
Coastal Development ◽  
Mangrove Forests ◽  
Dramatic Decline ◽  
Scientific Papers ◽  
Breeding Grounds ◽  
Multi Temporal ◽  
Remote Sensing Techniques

Mangrove forests are important and known as one of the most productive ecosystems in the tropics. They reduce the impacts of extreme events, provide important breeding grounds for aquatic species and build the resilience of ecosystem-dependent coastal communities. On the contrary, they are also known as one of the most threatened and vulnerable ecosystems worldwide, which have experienced a dramatic decline due to extensive coastal development during the last half-century. Remote sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and its changes, which is reflected by a large number of scientific papers published on this topic. The aim of this study was to investigate the multi-decadal changes of mangrove forests selected communes in Hai Phong city, North Vietnam, based on using Landsat and Sentinel 2 data from 2000 to 2018. The study used these continuous steps: 1) data pre-processing; 2) image classification using Normalized Difference Vegetation Index; 3) accuracy assessments; and 4) multi-temporal change detection and spatial analysis of mangrove forests. The classification maps in comparison with the ground reference data showed the satisfactory agreement with the overall accuracy was higher than 80.0%. From 2000 to 2018, the areas of mangrove forests in the study regions  increased by 584.2 ha in Dai Hop and Bang La communes (Region 1) and by 124.2 ha in Tan Thanh, Ngoc Xuyen and Ngoc Hai communes (Region 2), mainly due to the boom of mangrove planting projects and good mangrove management at the local community level.

scholarly journals A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa

2021 ◽  
Vol 10 (3) ◽  
pp. 129
Author(s):  
Vincent Nzabarinda ◽  
Anming Bao ◽  
Wenqiang Xu ◽  
Solange Uwamahoro ◽  
Madeleine Udahogora ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Type ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Plant Health ◽  
Tree Cover ◽  
Climatic Zones ◽  
Dramatic Decline ◽  
Precipitation Changes

Vegetation is vital, and its greening depends on access to water. Thus, precipitation has a considerable influence on the health and condition of vegetation and its amount and timing depend on the climatic zone. Therefore, it is extremely important to monitor the state of vegetation according to the movements of precipitation in climatic zones. Although a lot of research has been conducted, most of it has not paid much attention to climatic zones in the study of plant health and precipitation. Thus, this paper aims to study the plant health in five African climatic zones. The linear regression model, the persistence index (PI), and the Pearson correlation coefficients were applied for the third generation Normalized Difference Vegetation Index (NDVI3g), with Climate Hazard Group infrared precipitation and Climate Change Initiative Land Cover for 34 years (1982 to 2015). This involves identifying plants in danger of extinction or in dramatic decline and the relationship between vegetation and rainfall by climate zone. The forest type classified as tree cover, broadleaved, deciduous, closed to open (>15%) has been degraded to 74% of its initial total area. The results also revealed that, during the study period, the vegetation of the tropical, polar, and warm temperate zones showed a higher rate of strong improvement. Although arid and boreal zones show a low rate of strong improvement, they are those that experience a low percentage of strong degradation. The continental vegetation is drastically decreasing, especially forests, and in areas with low vegetation, compared to more vegetated areas, there is more emphasis on the conservation of existing plants. The variability in precipitation is excessively hard to tolerate for more types of vegetation.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

Change on detection of vegetation cover and soil salinity using GIS technique in Diyala Governorate, Iraq

2021 ◽  
Vol 30 (1) ◽  
pp. 148-158
Author(s):  
Haneen Adeeb ◽  
Yaseen Al-Timimi
Keyword(s):  
Soil Salinity ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Inverse Correlation ◽  
Salinity Changes ◽  
Gis Technique ◽  
Salinity Index ◽  
Remote Sensing Techniques ◽  
Strong Inverse Correlation

Soil salinity is one of the most important problems of land degradation, that threatening the environmental, economic and social system. The aim of this study to detect the changes in soil salinity and vegetation cover for Diyala Governorate over the period from 2005 to 2020, through the use of remote sensing techniques and geographic information system. The normalized difference vegetation index (NDVI) and salinity index (SI) were used, which were applied to four of the Landsat ETM+ and Landsat OLI satellite imagery. The results showed an increase in soil salinity from 7.27% in the period 2005–2010 to 27.03% in 2015–2020, as well as an increase in vegetation from 10% to 24% in the same period. Also the strong inverse correlation between the NDVI and the SI showed that vegetation is significantly affected and directly influenced by soil salinity changes

scholarly journals Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Program ◽  
Land Use Land Cover ◽  
Irrigation Scheme ◽  
Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

2018 ◽  
Vol 156 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Y. Palchowdhuri ◽  
R. Valcarce-Diñeiro ◽  
P. King ◽  
M. Sanabria-Soto
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Classification Algorithms ◽  
Type Mapping ◽  
Crop Type ◽  
Multi Temporal ◽  
Very High

AbstractRemote sensing (RS) offers an efficient and reliable means to map features on Earth. Crop type mapping using RS at various temporal and spatial resolutions plays an important role spanning from environmental to economical. The main objective of the current study was to evaluate the significance of optical data in a multi-temporal crop type classification-based on very high spatial resolution and high spatial resolution imagery. With this aim, three images from WorldView-3 and Sentinel-2 were acquired over Coalville (UK) between April and July 2016. Three vegetation indices (VIs); the normalized difference vegetation index, the green normalized difference vegetation index and soil adjusted vegetation index were generated using red, green and near-infrared spectral bands; then a supervised classification was performed using ground reference data collected from field surveys, Random forest (RF) and decision tree (DT) classification algorithms. Accuracy assessment was undertaken by comparing the classified output with the reference data. An overall accuracy of 91% and κ coefficient of 0·90 were estimated using the combination of RF and DT classification algorithms. Therefore, it can be concluded that integrating very high- and high-resolution imagery with different VIs can be implemented effectively to produce large-scale crop maps even with a limited temporal-dataset.

scholarly journals A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2757 ◽  
Author(s):  
Akash Ashapure ◽  
Jinha Jung ◽  
Anjin Chang ◽  
Sungchan Oh ◽  
Murilo Maeda ◽  
...  
Keyword(s):  
Comparative Study ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Canopy Cover ◽  
Unmanned Aircraft ◽  
Estimation Methods ◽  
Green Vegetation ◽  
Multi Temporal ◽  
Rgb Images ◽  
Multispectral Sensors

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

scholarly journals Deserted Medieval Village Reconstruction Using Applied Geosciences

2020 ◽  
Vol 12 (12) ◽  
pp. 1975
Author(s):  
Alexandru Hegyi ◽  
Apostolos Sarris ◽  
Florin Curta ◽  
Cristian Floca ◽  
Sorin Forțiu ◽  
...  
Keyword(s):  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Archaeological Site ◽  
Point Of View ◽  
Surface Model ◽  
Small Satellites ◽  
High Resolution Images ◽  
Remote Sensing Techniques

This study presents a new way to reconstruct the extent of medieval archaeological sites by using approaches from the field of geoinformatics. Hence, we propose a combined use of non-invasive methodologies which are used for the first time to study a medieval village in Romania. The focus here will be on ground-based and satellite remote-sensing techniques. The method relies on computing vegetation indices (proxies), which have been utilized for archaeological site detection in order to detect the layout of a deserted medieval town located in southwestern Romania. The data were produced by a group of small satellites (3U CubeSats) dispatched by Planet Labs which delivered high-resolution images of the Earth’s surface. The globe is encompassed by more than 150 satellites (dimensions: 10 × 10 × 30 cm) which catch different images for the same area at moderately short intervals at a spatial resolution of 3–4 m. The four-band Planet Scope satellite images were employed to calculate a number of vegetation indices such as NDVI (Normalized Difference Vegetation Index), DVI (Difference Vegetation Index), SR (Simple Vegetation Ratio) and others. For better precision, structure from motion (SfM) techniques were applied to generate a high-resolution orthomosaic and a digital surface model in which the boundaries of the medieval village of “Șanțul Turcilor” in Mașloc, Romania, can be plainly observed. Additionally, this study contrasts the outcomes with a geophysical survey that was attempted inside the central part of the medieval settlement. The technical results of this study also provide strong evidence from an historical point of view: the first documented case of village systematization during the medieval period within Eastern Europe (particularly Romania) found through geoscientific methods.

scholarly journals Estimation of different data compositions for early-season crop type classification

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4834 ◽  
Author(s):  
Pengyu Hao ◽  
Mingquan Wu ◽  
Zheng Niu ◽  
Li Wang ◽  
Yulin Zhan
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Classification Performance ◽  
Distribution Maps ◽  
Production Forecasting ◽  
Crop Type ◽  
Ndvi Time Series ◽  
Multi Temporal

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

scholarly journals Evolution of moult-migration is directly linked to aridity of the breeding grounds in North American passerines

2020 ◽  
Vol 16 (6) ◽  
pp. 20200155
Author(s):  
Claudie Pageau ◽  
Christopher M. Tonra ◽  
Mateen Shaikh ◽  
Nancy J. Flood ◽  
Matthew W. Reudink
Keyword(s):  
North American ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Autumn Migration ◽  
Western North America ◽  
Migration Distance ◽  
Migration Strategy ◽  
Breeding Grounds ◽  
Breeding Migration ◽  
Moult Migration

To avoid energy allocation conflicts, birds generally separate breeding, migration and moult during the annual cycle. North American passerines typically moult on the breeding grounds prior to autumn migration. However, some have evolved a moult-migration strategy in which they delay moult until stopping over during autumn migration. Rohwer et al . (2005) proposed the ‘push–pull hypothesis' as an explanation for the evolution of this moult strategy, but it has not been empirically tested. Poor conditions on the breeding grounds at the end of the summer would push birds to depart prior to moult, while productive stopover locations would pull them. We tested for a relationship between moult-migration and breeding grounds aridity as measured by the normalized difference vegetation index. Our results strongly support the ‘push' aspect of the push–pull hypothesis and indicate that arid breeding grounds, primarily in western North America, would drive species to evolve stopover moult-migration, although this relationship may depend upon migration distance.

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