Monitoring post-fire regeneration in Mediterranean ecosystems by employing multitemporal satellite imagery

2009 ◽  
Vol 18 (6) ◽  
pp. 648 ◽  
Author(s):  
Rocío Hernández Clemente ◽  
Rafael María Navarro Cerrillo ◽  
Ioannis Z. Gitas
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Mediterranean Ecosystems ◽  
Remotely Sensed ◽  
Vegetation Recovery ◽  
Remotely Sensed Data ◽  
Trend Function ◽  
The One

Fire-damaged ecosystems have often been monitored by applying a combination of field survey information and vegetation indices derived from remotely sensed data. Furthermore, it has been demonstrated that remotely sensed data can be integrated as a useful tool in predicting the recovery of fire-damaged ecosystems over time. Using regression models, the present study analyzes the trend function described by the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) 7 and 12 years after the fire. The method was performed through (i) permanent plot collection per plant community type and data reduction; (ii) comparison of the correlation established between FVC with different vegetation index contrasted with the NDVI; (iii) monitoring vegetation recovery; and (iv) a supervised classification of FVC. The NDVI was the one that correlated most with the FVC. In both the seventh and twelfth year after fire, the linear regression model was used to accurately quantify FVC based on the NDVI. Results show that 12 years after the fire, the recovery rate of the FVC associated with scrub was higher than that of the FVC of other forest classes. Although vegetation recovery is taking place, the continuing increase in the FVC associated with shrub land classes could create a state of successional stagnation.

scholarly journals Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

2020 ◽  
Vol 12 (18) ◽  
pp. 2970
Author(s):  
Anna C. Talucci ◽  
Elena Forbath ◽  
Heather Kropp ◽  
Heather D. Alexander ◽  
Jennie DeMarco ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Basal Area ◽  
Vegetation Recovery ◽  
Climate Conditions ◽  
Northeastern Siberia ◽  
Larch Forests

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

scholarly journals Determination of Crop Health Monitoring in MPKV – Rahuri, Using Remote Sensing Approach

2021 ◽  
pp. 11-16
Author(s):  
Ankita P. Kamble ◽  
A. A. Atre ◽  
Payal A. Mahadule ◽  
C. B. Pande ◽  
N. S. Kute ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Monitoring ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Remotely Sensed Data ◽  
Pests And Diseases ◽  
Crop Health ◽  
Sentinel 2

Pests and diseases cause major harm during crop development. Also plant stress affects crop quality and quantity. Recent developments in high resolution remotely sensed data has seen a great potential in mapping cropland areas infected by pests and diseases, as well as potential vulnerable areas over expansive areas. Crop health monitoring in this study was carried out using remote sensing techniques. The present study was carried out in MPKV, Rahuri, Ahmednagar District, Maharashtra. Vegetation indices like Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to classify the crops into healthy and dead or unhealthy one. Sentinel-2 image data from October 2019 to January 2020 processed in Arc GIS 10.1 were used for this study. Vegetation is a key component of the ecosystem and plays an important role in stabilizing the global environment. The result showed that the average vegetation cover was decreased in the month of November and healthy vegetation was found more in month of October as compared to December and January. This shows that NDVI and SAVI indices for Sentinel-2 images can be used for crop health monitoring.

scholarly journals Use of remote sensing to understand the terroir of the Niagara Peninsula. Applications in a Riesling vineyard

OENO One ◽  
2015 ◽  
Vol 49 (1) ◽  
pp. 1 ◽  
Author(s):  
Matthieu Marciniak ◽  
Ralph Brown ◽  
Andrew Reynolds ◽  
Marilyne Jollineau
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Status ◽  
Remote Sensing Data ◽  
Titratable Acidity ◽  
Weather Conditions ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Berry Composition

<p style="text-align: justify;"><strong>Aim:</strong> The purpose of this study was to determine if multispectral high spatial resolution airborne imagery could be used to segregate zones in vineyards to target fruit of highest quality for premium winemaking. We hypothesized that remotely sensed data would correlate with vine size and leaf water potential (ψ), as well as with yield and berry composition.</p><p style="text-align: justify;"><strong>Methods and results:</strong> Hypotheses were tested in a 10-ha Riesling vineyard [Thirty Bench Winemakers, Beamsville (Ontario)]. The vineyard was delineated using GPS and 519 vines were geo-referenced. Six sub-blocks were delineated for study. Four were identified based on vine canopy size (low, high) with remote sensing in 2005. Airborne images were collected with a four-band digital camera every 3-4 weeks over 3 seasons (2007-2009). Normalized difference vegetation index (NDVI) values (NDVI-red, green) and greenness ratio were calculated from the images. Single-leaf reflectance spectra were collected to compare vegetation indices (VIs) obtained from ground-based and airborne remote-sensing data. Soil moisture, leaf ψ, yield components, vine size, and fruit composition were also measured. Strong positive correlations were observed between VIs and vine size throughout the growing season. Vines with higher VIs during average to dry years had enhanced fruit maturity (higher °Brix and lower titratable acidity). Berry monoterpenes always had the same relationship with remote sensing variables regardless of weather conditions.</p><p style="text-align: justify;"><strong>Conclusions:</strong> Remote sensing images can assist in delineating vineyard zones where fruit will be of different maturity levels, or will have different concentrations of aroma compounds. Those zones could be considered as sub-blocks and processed separately to make wines that reflect those terroir differences. Strongest relationships between remotely sensed VIs and berry composition variables occurred when images were taken around veraison.</p><strong>Significance and impact of the study:</strong> Remote sensing may be effective to quantify spatial variation in grape flavour potential within vineyards, in addition to characteristics such as water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and using protocols for excluding spectral reflectance contributed by inter-row vegetation.

Prediction of spatial ET-fluxes using remote sensing and field data of selected areas in the Eastern Part of Abu Dhabi, United Arab Emirates

Soil Research ◽  
2006 ◽  
Vol 44 (8) ◽  
pp. 759
Author(s):  
Fares M. Howari ◽  
Ahmed Murad ◽  
Hassan Garamoon
Keyword(s):  
Remote Sensing ◽  
United Arab Emirates ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hydrological Cycle ◽  
Remotely Sensed ◽  
Arid Environments ◽  
Remotely Sensed Data ◽  
Considerable Work

Evapotranspiration (ET) is a major source of water depletion in arid and semi-arid environments; and it is a poorly quantified variable in the hydrological cycle. Remote sensing has the potential application to quantify this variable especially at large scale. The present study reports methodology useful to determine whether derived variables from remotely sensed data, such as vegetation and soil brightness indices, could be used to predict ET. To achieve this goal, various regression analyses were conducted between data derived from satellites, field meteorological stations, and ET values. Selected sub-scenes of Landsat Enhanced Thematic Mapper images free of cloud were used to derive Normalized Difference Vegetation Index (NDVI) and Soil Brightness Index using ER-Mapper and JMP software packages. From the obtained relationship between NDVI and ET, it was observed that ET increases sharply with increase in NDVI. The predicted ET results obtained from the multiple regression functions of field ET, NDVI, solar radiation, wind velocity, and/or temperature are comparable with the ET values obtained by Penman-Monteith method. The results showed that a remotely sensed vegetation index could be used, indirectly, to determine ET values. However, there is still considerable work to be done before simple and full automated extraction of ET from the reported methods can be achieved for large-scale applications.

scholarly journals Change of Vegetation Cover at Rohingya Refugee Occupied Areas in Cox’s Bazar District of Bangladesh: Evidence from Remotely Sensed Data

2019 ◽  
Vol 11 (1-2) ◽  
pp. 9-16
Author(s):  
M Rahman ◽  
MS Islam ◽  
TA Chowdhury
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remotely Sensed ◽  
Refugee Camps ◽  
Remotely Sensed Data ◽  
Index Method ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Before And After ◽  
One Year

Nearly one million Rohingya Refugees are living in Cox’s Bazar—a south-eastern district of Bangladesh; among them, more than half a million have fled Myanmar since August 2017. There are always some impacts of refugee settlements on the host environment. Hence, this study has made an initiative to investigate the changes of vegetation covers in four refugee occupied Unions of Teknaf and Ukhia Upazila. Analysing the remotely sensed Landsat imageries using Normalized Difference Vegetation Index method, the spatial extent of sparse vegetation, moderate vegetation, and dense vegetation before and after the occurrence of 2017 Influx have been quantified. The result reveals that nearly 21,000 acres of dense vegetation and more than 1700 acres of moderate vegetation have been reduced within the period of one year in-between 2017 and 2018. On the other hand, during the same period, the refugee sites have been expanded by almost 6000 acres. The main reasons for this drastic reduction of vegetation include the construction of refugee camps by felling the forest and consumption of firewood by refugees from the surrounding forest of their camps. Arrangement of alternative cooking fuel, relocation of refugees, reforestation, and accelerating the repatriation process may reduce the further degradation of vegetation. J. Environ. Sci. & Natural Resources, 11(1-2): 9-16 2018

scholarly journals Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Land ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 58 ◽  
Author(s):  
Nicola Puletti ◽  
Marco Bascietto
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Central Italy ◽  
Remotely Sensed ◽  
Google Earth ◽  
Large Set ◽  
Remotely Sensed Data ◽  
Forest Inventories ◽  
Automated Method ◽  
Forested Areas

Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 “forest” points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km2 in Tuscany, central Italy.

Use of linguistic estimates and vegetation indices to assess post-fire vegetation regrowth in woodland areas

2010 ◽  
Vol 19 (1) ◽  
pp. 94 ◽  
Author(s):  
Carol R. Jacobson
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Moisture Index ◽  
Index Data ◽  
Subset Regression ◽  
Simple Ratio ◽  
The Relationship ◽  
Non Destructive

This study examined an area of woodland that was recovering from severe fire in Royal National Park (NSW, Australia). A non-destructive method of field sampling is required for vulnerable recovering vegetation and therefore classification of digital photographs using linguistic terms was trialled. The linguistic data for three vegetation strata (canopy, shrub and ground) were converted to crisp scores and compared with vegetation index data derived from remotely sensed imagery. All possible subset regression was used to test the proposition that the combined vegetation scores (independent variables) would explain the values of NDVI (Normalized Difference Vegetation Index) and NDMI (Normalized Difference Moisture Index). Vegetation scores for the three strata were also combined using simplified weighting ratios to assess broad relationships between the indices and field data. The combined vegetation scores explained ~60% of the variation in the vegetation index data and inclusion of variables representing multiple strata explained more of the variation than any single variable. The precise value of the weights used to combine the layers did not affect the strength of the association. A simple ratio is proposed that may be useful to estimate woodland parameters under similar conditions, by inversion of the relationship with vegetation index data.

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 Spring phenology and phenology-climate links inferred from two remotely sensed vegetation indices across regions and biomes

2018 ◽  
Author(s):  
Xiyan Xu ◽  
William J. Riley ◽  
Charles D. Koven ◽  
Gensuo Jia
Keyword(s):  
North America ◽  
Temperature Sensitivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Remotely Sensed ◽  
Slight Reduction ◽  
Eastern United States ◽  
Spring Phenology

Abstract. The timing of spring greenup (SG) as inferred by remotely sensed vegetation indices have showed contrasting dynamics across the same region and periods. Assessing the uncertainty in SG associated with different Normalized Difference Vegetation Index (NDVI) products is essential for robustly interpreting the links between climate and phenological dynamics. We compare SG inferred from two NDVI products over the period 2001–2013: (1) Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) National Oceanic and Atmospheric Administration's (NOAA's) Advanced Very High Resolution Radiometer (AVHRR) instruments processed by the Global Inventory Monitoring and Modeling Studies (GIMMS) to explore confidence and uncertainty in the NDVI-inferred SG trend and its links to climate variability. Both MODIS and GIMMS agreed in showing an advancement of SG in northern Canada, the eastern United States, and Russia, as well as a delay in SG in western North America, parts of Baltic Europe and East Asia. In the regions with advanced SG, GIMMS inferred much weaker advancement whereas in the regions with delayed SG, GIMMS inferred much stronger delay than MODIS. This resulted in a GIMMS SG delay in both North America and Eurasia. MODIS data show no significant SG shift in North American for spatial heterogeneity in SG shift, but dominant SG advancement in Eurasia. The SG advancement inferred from MODIS is associated with a stronger coupling between SG and temperature and a stronger sensitivity across biomes as compared to GIMMS. The main uncertainty in the SG trend and SG-temperature sensitivity are in northern high latitudes (>50° N) where GIMMS and MODIS show different magnitude and sign of the annual SG anomalies. Compared to 1988–2000, inter-biome GIMMS SG-temperature sensitivity is stable and the SG-temperature sensitivity increased in the boreal and Arctic biomes despite a slight reduction in the SG-temperature coupling over the period 2001–2013. The explanation for the increased SG-temperature sensitivity remains unclear and requires further investigation. We suggest broader evaluation of the NDVI products against field measurements and inter-validation for robust assessment of vegetation dynamics.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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