scholarly journals Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

2018 ◽  
Vol 87 (4) ◽  
Author(s):  
Bogdan Zagajewski ◽  
Marlena Kycko ◽  
Hans Tømmervik ◽  
Zbigniew Bochenek ◽  
Bronisław Wojtuń ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Chlorophyll Concentration ◽  
High Arctic ◽  
Electromagnetic Spectrum ◽  
Primary Role ◽  
Salix Polaris ◽  
Red Edge ◽  
Dryas Octopetala ◽  
Bistorta Vivipara

Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub <em>Salix polaris</em>, the herb <em>Bistorta vivipara</em>, and the prostrate semievergreen shrub <em>Dryas octopetala</em>. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that <em>S. polaris</em> and <em>B. vivipara</em> were in good health, while the health status of <em>D. octopetala</em> was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas.

scholarly journals Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat

2018 ◽  
Vol 30 ◽  
pp. 63-74
Author(s):  
Ilina Kamenova ◽  
Petar Dimitrov ◽  
Rusina Yordanova
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Chlorophyll Concentration ◽  
Vegetation Indices ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Red Edge ◽  
Absorbed Photosynthetically Active Radiation

The aim of the study is to evaluate the possibility for using RapidEye data for prediction of Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR), fraction of vegetation Cover (fCover), leaf Chlorophyll Concentration (CC) and Canopy Chlorophyll Content (CCC) of winter wheat. The relation of a number of vegetation indices (VIs) with these crop variables are accessed based on a regression analysis. Indices, which make use of the red edge band, such as Chlorophyll Index red edge (CIre) and red edge Normalized Difference Vegetation Index (reNDVI), were found most useful, resulting in linear models with R2 of 0.67, 0.71, 0.72, and 0.76 for fCover, LAI, CCC, and fAPAR respectively. CC was not related with any of the VIs.

scholarly journals Satellites reveal an increase in gross primary production in a greenlandic high arctic fen 1992–2008

2010 ◽  
Vol 7 (1) ◽  
pp. 1101-1129 ◽  
Author(s):  
T. Tagesson ◽  
M. Mastepanov ◽  
M. P. Tamstorf ◽  
L. Eklundh ◽  
P. Schubert ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Field Measurements ◽  
High Arctic ◽  
Research Area ◽  
Strong Increase ◽  
Co2 Uptake ◽  
Flux Measurements

Abstract. Arctic wetlands play a key role in the terrestrial carbon cycle. Recent studies have shown a greening trend and indicated an increase in CO2 uptake in boreal and sub- to low-arctic areas. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with ground-based flux measurements of CO2 to investigate a possible greening trend and potential changes in gross primary production (GPP) between 1992 and 2008 in a high arctic fen area. The study took place in Rylekaerene in the Zackenberg Research Area (74°28' N 20°34' W), located in the National park of North Eastern Greenland. We estimated the light use efficiency (ε) for the dominant vegetation types from field measured fractions of photosynthetic active radiation (FAPAR) and ground-based flux measurements of GPP. Measured FAPAR were correlated to satellite-based NDVI. The FAPAR-NDVI relationship in combination with ε was applied to satellite data to model GPP 1992–2008. The model was evaluated against field measured GPP. The model was a useful tool for up-scaling GPP and all basic requirements for the model were well met, e.g., FAPAR was well correlated to NDVI and modeled GPP was well correlated to field measurements. The studied high arctic fen area has experienced a strong increase in GPP between 1992 and 2008. The area has during this period also experienced a substantial increase in local air temperature. Consequently, the observed greening trend is most likely due to ongoing climatic change possibly in combination with CO2 fertilization, due to increasing atmospheric concentrations of CO2.

scholarly journals Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

2019 ◽  
Vol 11 (18) ◽  
pp. 2127
Author(s):  
Polinova ◽  
Salinas ◽  
Bonfante ◽  
Brook
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Future Climate Change ◽  
Weather Forecasts ◽  
Red Edge ◽  
Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

scholarly journals Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

2019 ◽  
Vol 11 (23) ◽  
pp. 2807 ◽  
Author(s):  
Arthur Bayle ◽  
Bradley Carlson ◽  
Vincent Thierion ◽  
Marc Isenmann ◽  
Philippe Choler
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Model Performance ◽  
Mountain Range ◽  
Rhododendron Ferrugineum ◽  
Red Edge ◽  
Rocky Habitats ◽  
Edge Band ◽  
Sentinel 2

Shrub encroachment into grassland and rocky habitats is a noticeable land cover change currently underway in temperate mountains and is a matter of concern for the sustainable management of mountain biodiversity. Current land cover products tend to underestimate the extent of mountain shrublands dominated by Ericaceae (Vaccinium spp. (species) and Rhododendron ferrugineum). In addition, mountain shrubs are often confounded with grasslands. Here, we examined the potential of anthocyanin-responsive vegetation indices to provide more accurate maps of mountain shrublands in a mountain range located in the French Alps. We relied on the multi-spectral instrument onboard the Sentinel-2A and 2B satellites and the availability of red-edge bands to calculate a Normalized Anthocyanin Reflectance Index (NARI). We used this index to quantify the autumn accumulation of anthocyanin in canopies dominated by Vaccinium spp. and Rhododendron ferrugineum and compared the effectiveness of NARI to Normalized Difference Vegetation Index (NDVI) as a basis for shrubland mapping. Photointerpretation of high-resolution aerial imagery, intensive field campaigns, and floristic surveys provided complementary data to calibrate and evaluate model performance. The proposed NARI-based model performed better than the NDVI-based model with an area under the curve (AUC) of 0.92 against 0.58. Validation of shrub cover maps based on NARI resulted in a Kappa coefficient of 0.67, which outperformed existing land cover products and resulted in a ten-fold increase in estimated area occupied by Ericaceae-dominated shrublands. We conclude that the Sentinel-2 red-edge band provides novel opportunities to detect seasonal anthocyanin accumulation in plant canopies and discuss the potential of our method to quantify long-term dynamics of shrublands in alpine and arctic contexts.

scholarly journals Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1221 ◽  
Author(s):  
Jun Wang ◽  
Lichun Sui ◽  
Xiaomei Yang ◽  
Zhihua Wang ◽  
Yueming Liu ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Eastern China ◽  
Chlorophyll Concentration ◽  
False Negative ◽  
Distribution Data ◽  
Landsat 8 ◽  
Negative Results

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania

Mammalia ◽  
2020 ◽  
Vol 84 (2) ◽  
pp. 136-143 ◽  
Author(s):  
Davis J. Chidodo ◽  
Didas N. Kimaro ◽  
Proches Hieronimo ◽  
Rhodes H. Makundi ◽  
Moses Isabirye ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Strong Positive Correlation ◽  
Arid Areas ◽  
Population Abundance ◽  
Rodent Population ◽  
Rodent Abundance ◽  
Semi Arid

AbstractThis study aimed to evaluate the potential use of normalized difference vegetation index (NDVI) from satellite-derived remote sensing data for monitoring rodent abundance in semi-arid areas of Tanzania. We hypothesized that NDVI could potentially complement rainfall in predicting rodent abundance spatially and temporally. NDVI were determined across habitats with different vegetation types in Isimani landscape, Iringa Region, in the southern highlands of Tanzania. Normalized differences in reflectance between the red (R) (0.636–0.673 mm) and near-infrared (NIR) (0.851–0.879 mm) channels of the electromagnetic spectrum from the Landsat 8 [Operational Land Imager (OLI)] sensor were obtained. Rodents were trapped in a total of 144 randomly selected grids each measuring 100 × 100 m2, for which the corresponding values of NDVI were recorded during the corresponding rodent trapping period. Raster analysis was performed by transformation to establish NDVI in study grids over the entire study area. The relationship between NDVI, rodent distribution and abundance both spatially and temporally during the start, mid and end of the dry and wet seasons was established. Linear regression model was used to evaluate the relationships between NDVI and rodent abundance across seasons. The Pearson correlation coefficient (r) at p ≤ 0.05 was carried out to describe the degree of association between actual and NDVI-predicted rodent abundances. The results demonstrated a strong linear relationship between NDVI and actual rodent abundance within grids (R2 = 0.71). NDVI-predicted rodent abundance showed a strong positive correlation (r = 0.99) with estimated rodent abundance. These results support the hypothesis that NDVI has the potential for predicting rodent population abundance under smallholder farming agro-ecosystems. Hence, NDVI could be used to forecast rodent abundance within a reasonable short period of time when compared with sparse and not widely available rainfall data.

scholarly journals Simulating the Leaf Area Index of Rice from Multispectral Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3663
Author(s):  
Shenzhou Liu ◽  
Wenzhi Zeng ◽  
Lifeng Wu ◽  
Guoqing Lei ◽  
Haorui Chen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Area Index ◽  
Red Edge ◽  
Extreme Gradient Boosting

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Ứng dụng công nghệ UAV (drones) theo dõi và hỗ trợ cảnh báo sớm dịch hại vùng canh tác lúa tỉnh Sóc Trăng

2021 ◽  
Vol 57 (2) ◽  
pp. 28-38
Author(s):  
Võ Quốc Tuấn ◽  
Tấn Lợi Nguyễn ◽  
Thị Dal Quãng ◽  
Trương Chí Quang ◽  
Quốc Việt Phạm
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Red Edge ◽  
Aerial Vehicle

Đồng bằng sông Cửu Long là vùng canh tác lúa trọng điểm của cả nước, tuy nhiên việc thâm canh tăng vụ trong nhiều năm đã làm cho tình hình sâu bệnh diễn biến phức tạp. Nghiên cứu được thực hiện nhằm ứng dụng công nghệ máy bay không người lái (UAV - unmanned aerial vehicle) để theo dõi và cảnh báo sớm dịch hại.  Nghiên cứu phân tích mối quan hệ giữa mức độ nhiễm dịch hại trên lúa dựa trên chỉ số khác biệt thực vật (NDVI - normalized difference vegetation index), chỉ số khác biệt rìa đỏ (NDRE - normalized difference red edge index), và số liệu điều tra thực địa được thu thập tại thời điểm chụp ảnh. Kết quả phân tích đã phân loại được 4 mức độ nhiễm dịch hại trên lúa: nhiễm dịch hại nặng, nhiễm dịch hại trung bình, nhiễm dịch hại nhẹ và không nhiễm dịch hại với tổng diện tích nhiễm là 11,37 ha. Trong đó, nhiễm nặng chiếm 2,1 ha, nhiễm trung bình chiếm 2,76 ha, nhiễm nhẹ chiếm 6,51 ha và không nhiễm là 12,33 ha. Qua đó cho thấy khả năng ứng dụng công nghệ UAV trong theo dõi và hỗ trợ cảnh báo sớm dịch hại trên cây lúa mang lại nhiều hiệu quả, góp phần nâng cao hiệu quả sản xuất lúa tại tỉnh Sóc Trăng nói riêng và vùng Đồng bằng sông Cửu Long nói chung.

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