scholarly journals Mapping and Monitoring the Canopy Cover and Greenness of Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines) in Central-Eastern Tennessee Using Multi-Temporal Landsat Satellite Data

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 499
Author(s):  
Clement Akumu ◽  
Raphael Smith ◽  
Solomon Haile
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Canopy Cover ◽  
Forest Products ◽  
The United States ◽  
Pinus Echinata ◽  
Random Forest Classification ◽  
Central Eastern ◽  
Landsat Satellite

Southern yellow pines such as loblolly, Virginia and shortleaf pines constitute forest products and contribute significantly to the economy of the United States (U.S.). However, little is understood about the temporal change in canopy cover and greenness of southern yellow pines, especially in Tennessee where they are used for timber and pulpwood. This study aims to map and monitor the canopy cover and greenness of southern yellow pines i.e., loblolly (Pinus taeda), shortleaf (Pinus echinata), and Virginia (Pinus Virginiana) pines in the years 1988, 1999 and 2016 in central-eastern Tennessee. Landsat time-series satellite data acquired in December 1988, November 1999 and February 2016 were used to map and monitor the canopy cover and greenness of loblolly, shortleaf and Virginia pines. The classification and mapping of the canopy cover of southern yellow pines were performed using a machine-learning random forest classification algorithm. Normalized Difference Vegetation Index (NDVI) was used to monitor the temporal variation in canopy greenness. In total, the canopy cover of southern yellow pines decreased by about 35% between December 1988 and February 2016. This information could be used by foresters and forest managers to support forest inventory and management.

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

2022 ◽  
Vol 88 (1) ◽  
pp. 29-38
Author(s):  
Clement E. Akumu ◽  
Eze O. Amadi
Keyword(s):  
Satellite Data ◽  
Forest Inventory ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Canopy Cover ◽  
Optical Data ◽  
Modified Soil

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.

scholarly journals Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States

2021 ◽  
Author(s):  
Erica N. Spotswood ◽  
Matthew Benjamin ◽  
Lauren Stoneburner ◽  
Megan M. Wheeler ◽  
Erin E. Beller ◽  
...  
Keyword(s):  
United States ◽  
Low Income ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Incidence Rates ◽  
Lower Income ◽  
Urban Nature ◽  
Urbanized Areas ◽  
Block Groups

AbstractUrban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

Environmental benefits of traditional irrigation ditches in the Sierra Nevada (Spain) ecosystem by analysing the spatial-temporal evolution of NDVI on different time scales

2021 ◽  
Author(s):  
Javier Aparicio ◽  
Rafael Pimentel ◽  
María José Polo
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Global Analysis ◽  
Environmental Benefits ◽  
Mountain Range ◽  
Mountain Area ◽  
Traditional Irrigation ◽  
Landsat Satellite

<p>In Mediterranean mountain regions, traditional irrigation systems still persist in areas where the  modernization approaches do not succeed in being operational. It is common that these systems alter the soil uses, vegetation distribution and hydrological natural regime. </p><p>This is the case of the extensive network of irrigation ditches in the Sierra Nevada Mountain Range in southeastern Spain (an UNESCO  Reserve of the Biosphere, with areas as Natural and National Park), which originated in Muslim times, and is still operational in some areas. These ditches have contributed to maintaining local agricultural systems and populations in basins dominated by snow conditions, and they constitute a traditional regulation of water resources in the area. The network is made up of two types of irrigation ditches: “careo” and irrigation ditches. The first, the "careo", collects the meltwater and infiltrates it along its course, maintaining a high level of soil moisture and favouring deep percolation volumes that can be later consumed by the population through springs and natural fountains. The second, the irrigation ones, are used to transport water from the natural sources to the agricultural plots downstream the mountain area. In 2014, several irrigation ditches were restored in the Natural Park. This is a chance to further explore and quantify the role of this network in the hydrological budget on a local basis.  </p><p>The aim of this work is to evaluate to what extent the existence of these intermittent water networks affects the evolution of the surrounding vegetation. For this, one of the restored systems,  the Barjas Ditch in the village of Cañar, with a successful water circulation along its way, was selected from the increase of the soil water content in the ditch influence area and, indirectly a differential development of vegetation. Two analyses are performed using remote sensing information. The Normalized Difference Vegetation Index, NDVI, which is a spectral index used to estimate the quantity, quality and development of vegetation that can therefore be used indirectly as an indicator of the state of soil moisture, was used as the indicator of evolution. For this purpose, a historical set of LandSat satellite images  (TM, ETM+ and OLI) has been used. On the one hand, a global analysis on the whole mountainous range was carried out, comparing NDVI patterns in areas affected and non-affected by the ditches. On the other hand, the restored  Barjas ditch is used to assess vegetation changes before and after the restoration.</p>

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 Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 940
Author(s):  
Rocío Ballesteros ◽  
Miguel A. Moreno ◽  
Fellype Barroso ◽  
Laura González-Gómez ◽  
José F. Ortega
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Multilinear Regression ◽  
Area Index ◽  
Canopy Development

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

Factors influencing the pattern of fire severities in a large wildfire under extreme meteorological conditions in the Mediterranean basin

2009 ◽  
Vol 18 (7) ◽  
pp. 755 ◽  
Author(s):  
Imma Oliveras ◽  
Marc Gracia ◽  
Gerard Moré ◽  
Javier Retana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Fire Behavior ◽  
Negative Relationship ◽  
Canopy Cover ◽  
Mediterranean Ecosystems ◽  
Meteorological Conditions ◽  
Landsat Images ◽  
The Difference

In Mediterranean ecosystems, large fires frequently burn under extreme meteorological conditions, but they are usually characterized by a spatial heterogeneity of burn severities. The way in which such mixed-severity fires are a result of fuels, topography and weather remains poorly understood. We computed fire severity of a large wildfire that occurred in Catalonia, Spain, as the difference between the post- and pre-fire Normalized Difference Vegetation Index (NDVI) values obtained through Landsat images. Fuel and topographic variables were derived from remote sensing, and fire behavior variables were obtained from an exhaustive reconstruction of the fire. Results showed that fire severity had a negative relationship with percentage of canopy cover, i.e. green surviving plots were mainly those with more forested conditions. Of the topographic variables, only aspect had a significant effect on fire severity, with higher values in southern than in northern slopes. Fire severity was higher in head than in flank and back fires. The interaction of these two variables was significant, with differences between southern and northern aspects being small for head fires, but increasing in flank and back fires. The role of these variables in determining the pattern of fire severities is of primary importance for interpreting the current landscapes and for establishing effective fire prevention and extinction policies.

scholarly journals Soil use and occupation in Caracol Settlement: a multitemporal assessment using remote sensing and geoprocessing techniques

2020 ◽  
Vol 9 (12) ◽  
pp. e30891211029
Author(s):  
Odemir Coelho da Costa ◽  
José Francisco dos Reis Neto ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
The United States ◽  
United States Geological Survey ◽  
Vegetation Landscape ◽  
Digital Elevation ◽  
Elevation Model ◽  
Gis Software

This study focused on the application of remote sensing and geoprocessing techniques to quantify the agroecological use of Caracol settlement area in order to quantify the vegetated areas, as well as the use and occupation of the soil in the years 2000, 2010 and 2020, in the months of May of each year. To achieve the objectives, computational tools (Quantum GIS software) were used, as well as data from Landsat 5 and 8 satellites, bands 3 and 4, 4 and 5 respectively. Vector data from the database of the Brazilian Institute of Geography and Statistics (IBGE), a Digital Elevation Model (DEM), from the United States Geological Survey (USGS/NASA) for evaluation of the watersheds were also used. For vegetation analysis, as well as temporal evolution, the Normalized Difference Vegetation Index (NDVI) was used, with this it was possible to evaluate by means of thematic maps and tables containing the quantification and classification of vegetation and soil cover. It was evident in the present study that there were significant changes in the vegetation landscape over two decades, through anthropic activity by settled families, that were responsible for such changes in the use and soil cover of Caracol settlement.

scholarly journals Cartographie De L’état Du Couvert Végétal Du Nord De La Côte D’ivoire À Partir D’images Satellites: Exemple De La Zone De Korhogo

2016 ◽  
Vol 12 (29) ◽  
pp. 204
Author(s):  
Avy StéphaneKoff ◽  
Abderrahman Ait Fora ◽  
Hicham Elbelrhiti
Keyword(s):  
Cote D'ivoire ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Côte D’Ivoire ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
The State ◽  
Cote D’Ivoire ◽  
Côte D'ivoire ◽  
Landsat Satellite

The purpose of this study is to determine the state of the vegetation cover in the region of Korhogo through remote sensing. Nowadays, the problem of desertification in the Sahel is serious. This could be explained by the phenomenon of climate change. We want to map the state of the vegetation cover in the study area. This study therefore focuses on the state of the vegetation cover in the region of Korhogo in northern Côte d’Ivoire. We will use one Landsat satellite image from December 16th 2000 and proceed with image processing. Processing techniques by the normalized difference vegetation index, the index armor and colorful composition 472. After these treatments in our pictures, we observe the behavior of vegetation. We can then get an overview of the vegetation in this area.

scholarly journals Rainwater Harvesting Using GIS Technique: A Case Study of Diyala Governorate, Iraq

2021 ◽  
Vol 32 (2) ◽  
pp. 96
Author(s):  
Dhuha S. Al-Khafaji ◽  
Asraa Khtan Abdulkareem ◽  
Qusai Y. Al-Kubaisi
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainwater Harvesting ◽  
Cost Effective ◽  
Stream Order ◽  
Time Saving ◽  
Harvesting Systems ◽  
Gis Environment ◽  
Landsat Satellite

To improve the management of water resources in Iraq, there are several methods, including the use of rainwater harvesting techniques. In this study, the Digital Elevation Model (DEM) and Landsat satellite imagery were used under the GIS environment to identify the suitable zones for rainwater harvesting. The accomplishment of rainwater harvesting systems strongly depends on their technical designing and identifying the suitable sites. Six criteria have been used to identify the rainwater harvesting sites in the Diyala governorate. The procedure of identifying the suitable sites for rainwater harvesting was applied twice for the Diyala governorate. Firstly, it was applied by using the criteria of rainfall, slope, stream order, distance to roads, and land use, and secondly, rainfall, slope, stream order, distance to roads, and Normalized Difference Vegetation Index (NDVI) criteria were used for this purpose. As a result, the study area was divided into three suitability zones: low, moderate, and high according to the specific criteria that were used to identify the rainwater harvesting suitable sites. It was found that in the application of land use criterion the low suitability zone represents 26%, 58% represents the moderate, and 16% for the high suitability zone, while in the method of NDVI it was found that 29% represents the zone that has low suitability, 57% represents the moderate, and 14% represents the high suitability zone. The compared results led to conclude that the land use is the most influential criterion for identifying the rainwater harvesting suitability sites and found that most of the Eastern parts of Diyala governorate are promising areas for rainwater harvesting and ArcGIS is a very useful, time-saving, and cost-effective tool for identifying the rainwater harvesting suitable sites.

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