scholarly journals A multi-biome study of tree cover detection using the Forest Cover Index

2021 ◽  
Author(s):  
Sarah Becker ◽  
Megan Maloney ◽  
Andrew Griffin
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Tree Cover ◽  
Future Research ◽  
Vegetative Cover ◽  
Ancillary Data ◽  
Land Covers ◽  
Sentinel 2

Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.

scholarly journals Effects of Windthrows on Forest Cover, Tree Growth and Soil Characteristics in Drought-Prone Pine Plantations

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 817
Author(s):  
Jesús Julio Camarero ◽  
Michele Colangelo ◽  
Antonio Gazol ◽  
Manuel Pizarro ◽  
Cristina Valeriano ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Pinus Halepensis ◽  
Soil Characteristics ◽  
Tree Cover ◽  
Mediterranean Forests ◽  
Soil Microbial Community Structure ◽  
Soil Features ◽  
Disturbance Intensity

Windstorms are forest disturbances which generate canopy gaps. However, their effects on Mediterranean forests are understudied. To fill that research gap, changes in tree, cover, growth and soil features in Pinus halepensis and Pinus sylvestris plantations affected by windthrows were quantified. In each plantation, trees and soils in closed-canopy stands and gaps created by the windthrow were sampled. Changes in tree cover and radial growth were assessed by using the Normalized Difference Vegetation Index (NDVI) and dendrochronology, respectively. Soil features including texture, nutrients concentration and soil microbial community structure were also analyzed. Windthrows reduced tree cover and enhanced growth, particularly in the P. halepensis site, which was probably more severely impacted. Soil characteristics were also more altered by the windthrow in this site: the clay percentage increased in gaps, whereas K and Mg concentrations decreased. The biomass of Gram positive bacteria and actinomycetes increased in gaps, but the biomass of Gram negative bacteria and fungi decreased. Soil gaps became less fertile and dominated by bacteria after the windthrow in the P. halepensis site. We emphasize the relevance of considering post-disturbance time recovery and disturbance intensity to assess forest resilience within a multi-scale approach.

scholarly journals Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

2020 ◽  
Vol 12 (18) ◽  
pp. 2980
Author(s):  
Jae-Hyun Ryu ◽  
Sang-Il Na ◽  
Jaeil Cho
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Correlation Coefficients ◽  
The Other ◽  
Terrestrial Vegetation ◽  
Pixel Size ◽  
Spectrometer Data ◽  
Sentinel 2

Remote sensing techniques using visible and near-infrared wavelengths are useful for monitoring terrestrial vegetation. The normalized difference vegetation index (NDVI) is a widely used proxy of vegetation conditions, and it has been measured at various footprint sizes using satellite, unmanned aerial vehicle (UAV), and ground-installed sensors. The goal of this study was to analyze the spatial characteristics of NDVI data by comparing the values obtained at different footprint sizes. In particular, the NDVI was evaluated in garlic and onion fields that featured ridges and furrows. The evaluation was performed using data from a leaf spectrometer, field spectrometers, ground-installed spectral reflectance sensors, a multispectral camera onboard a UAV, and Sentinel-2 satellites. The correlation coefficients between NDVIs evaluated from the various sensors (excluding the satellite-mounted sensors) ranged from 0.628 to 0.944. The UAV-based NDVI (NDVIUAV) exhibited the lowest root mean square error (RMSE = 0.088) when compared with field spectrometer data. On the other hand, the satellite-based NDVI data (NDVISentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVIUAV data to the pixel size of Sentinel-2, the comparison was improved, and the following statistics were obtained: correlation coefficient: 0.504–0.785; absolute bias: 0.048–0.078; RMSE: 0.063–0.094. According to the aforementioned results, ground-based NDVI data can be used to validate NDVIUAV data without further processing and NDVIUAV data can be used to validate NDVISentinel-2 data after upscaling to the Sentinel-2 pixel size. Overall, the results presented in this study may be helpful to understand and integrate NDVI data at different spatial scales.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

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.

scholarly journals Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

2018 ◽  
Vol 7 (10) ◽  
pp. 405 ◽  
Author(s):  
Urška Kanjir ◽  
Nataša Đurić ◽  
Tatjana Veljanovski
Keyword(s):  
Land Use ◽  
Time Series ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Control ◽  
Sentinel 2

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

scholarly journals Lower Noise Annoyance Associated with GIS-Derived Greenspace: Pathways through Perceived Greenspace and Residential Noise

2018 ◽  
Vol 15 (7) ◽  
pp. 1533 ◽  
Author(s):  
Angel M. Dzhambov ◽  
Iana Markevych ◽  
Boris Tilov ◽  
Zlatoslav Arabadzhiev ◽  
Drozdstoj Stoyanov ◽  
...  
Keyword(s):  
Noise Level ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Buffer Zone ◽  
Tree Cover ◽  
Noise Sources ◽  
Noise Abatement ◽  
Noise Annoyance ◽  
Lower Noise

Growing amounts of evidence support an association between self-reported greenspace near the home and lower noise annoyance; however, objectively defined greenspace has rarely been considered. In the present study, we tested the association between objective measures of greenspace and noise annoyance, with a focus on underpinning pathways through noise level and perceived greenspace. We sampled 720 students aged 18 to 35 years from the city of Plovdiv, Bulgaria. Objective greenspace was defined by several Geographic Information System (GIS)-derived metrics: Normalized Difference Vegetation Index (NDVI), tree cover density, percentage of green space in circular buffers of 100, 300 and 500 m, and the Euclidean distance to the nearest structured green space. Perceived greenspace was defined by the mean of responses to five items asking about its quantity, accessibility, visibility, usage, and quality. We assessed noise annoyance due to transportation and other neighborhood noise sources and daytime noise level (Lday) at the residence. Tests of the parallel mediation models showed that higher NDVI and percentage of green space in all buffers were associated with lower noise annoyance, whereas for higher tree cover this association was observed only in the 100 m buffer zone. In addition, the effects of NDVI and percentage of green space were mediated by higher perceived greenspace and lower Lday. In the case of tree cover, only perceived greenspace was a mediator. Our findings suggest that the potential for greenspace to reduce noise annoyance extends beyond noise abatement. Applying a combination of GIS-derived and perceptual measures should enable researchers to better tap individuals’ experience of residential greenspace and noise.

Sign in / Sign up

Export Citation Format

Share Document