scholarly journals Coconut Tree Stress Detection as an Indicator of Red Palm Weevil (RPW) Attack Using Sentinel Data

2020 ◽  
Vol 7 (3) ◽  
pp. 1-9
Author(s):  
Faradina Marzukhi ◽  
Md Azlin Md Said ◽  
Amirul Audi Ahmad
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Stress Detection ◽  
Red Palm Weevil ◽  
Promising Alternative ◽  
Coconut Tree ◽  
Palm Weevil ◽  
Tree Stress ◽  
Sentinel 2A

The red palm weevil (RPW) is one of the worst destructive pests of palms in the world. This study focuses for the first time on the coconut tree stress detection and discrimination among different stages of red palm weevil (RPW) stress-attacks using vegetation indices (VI) and the percentage of accuracy assessed. Different spectral indices were assessed using Sentinel 2A data of year 2018. Based on field identification, four classes of coconut tree were considered and evaluated using visual maps of VI: severe, moderate, early and healthy coconut trees. Results showed that the vegetation indices Normalized Differenced Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), SQRT (IR/R), Difference Vegetation Index (DVI) and Green Vegetation Index (GVI) are sensitive to coconut trees caused by RPW attacks. They discriminated among the considered classes with more than 50% accurate from census data of field observation compared with remote sensing data of Sentinel 2A image.  Nevertheless, they express the healthiness of tree stress between 0.308 – 0.673 range with 55% to 91% accurate. According to these results, it was concluded that remote sensing technique using Sentinel 2A data is a promising alternative for RPW detection based on VI.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

2019 ◽  
Vol 11 (5) ◽  
pp. 1410 ◽  
Author(s):  
Suman Moparthy ◽  
Dominique Carrer ◽  
Xavier Ceamanos
Keyword(s):  
Remote Sensing ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Satellite Observations ◽  
Atmospheric Effects ◽  
Forest Regions ◽  
Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2013 ◽  
Vol 10 (10) ◽  
pp. 6279-6307 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Leaf Nitrogen ◽  
Agricultural Landscapes ◽  
Nitrogen Pools ◽  
Spectral Vegetation Indices

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

Combining tree ring analysis and remote sensing to assess the 2017 black pine dieback in Vinschgau/Val Venosta (Italy)

2020 ◽  
Author(s):  
Nikolaus Obojes ◽  
Jennifer Klemm ◽  
Ruth Sonnenschein ◽  
Francesco Giammarchi ◽  
Giustino Tonon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tree Ring ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Black Pine ◽  
Growth Data ◽  
Tree Vitality ◽  
Mountain Terrain ◽  
Ring Analysis

<p>To prevent further erosion of pastures along the south slopes of the Vinschgau/Val Venosta (South Tyrol/Italy) about 900 ha of non-native black pine (Pinus nigra) have been afforested there between 1900 and the 1960s. This drought-tolerant Mediterranean species was supposed to be able to cope with the dry climate at degraded soils in the inner-alpine dry valley. Nevertheless, black pine in the Vinschgau has been affected by reoccurring tree vitality decline and diebacks in the last 20 years linked to repeated droughts and heat waves. Observing growth trends via tree ring analysis is usually restricted to single stands. On the other hand, remote sensing data to track tree vitality was not available in sufficient spatial and temporal resolution to be applied to complex mountain terrain until recently. This has changed with the launch of the Sentinel-2 A and B satellites in 2015 and 2017 with a spatial resolution of 10 to 20 m and a revisiting period of 5 days. To analyse the accordance of remote sensing-based vegetation indices to tree-ring based growth data, we compared twelve sites across the Vinschgau/Val Venosta with a differing degree of vitality loss in 2017 for a four-year period from 2015 to 2018. In general, less vital sites were located at lower elevation and on steeper slopes. Radial tree growth was positively correlated to spring precipitation and strongly decreased during earlier hot and dry years such as 1995 and 2003. We found high and statistically significant correlations between site-average basal area increment as well as tree ring width indices and multiple vegetation indices (Normalized Difference Vegetation Index NDVI, Green Normalized Difference Vegetation Index GNDVI, Normalized Difference Infrared Index NDII, Moisture Stress Index MSI) especially for the dry 2017 growing season and the 2018 recovery year, which had large gradients in tree vitality between sites. Overall, these results show that remote sensing-based vegetation indices can be used to scale up stand level growth data also in complex mountain terrain.</p>

scholarly journals Population Abundance of Red Palm Weevil, Rhynchophorus ferrugineus (Coleoptera: Dryophthoridae) in Rhu Tapai Coconut Plantation

2020 ◽  
Vol 2 (1) ◽  
pp. 71-76
Author(s):  
Nurul Syafiqah Senafi ◽  
Nor Zalipah Mohamed ◽  
Wahizatul Afzan Azmi
Keyword(s):  
Daily Rainfall ◽  
Trapping Efficiency ◽  
Mass Trapping ◽  
Rhynchophorus Ferrugineus ◽  
Population Abundance ◽  
Red Palm Weevil ◽  
Coconut Tree ◽  
Pheromone Trapping ◽  
Palm Weevil ◽  
Significant Difference

Red Palm Weevil (RPW), Rhynchophorus ferrugineus is a lethal pest which has been described as a serious pest that reduces the production of coconut tree and other major cultivated palms. RPW infestation was initially detected by Department of Agriculture (DOA) in 2007 in all eight Terengganu districts. Rhu Tapai plantation area is one of the highly infested areas in Terengganu. Until now, limited reports on population abundance of this lethal pest have been discussed particularly in Rhu Tapai. Therefore, a study on population abundance is a must in order to understand the population dynamic of RPW which will be the first step of its control strategy. A total of 12 traps were installed and synthetic pheromones (P028 Ferrolure+) were used in attracting the RPW adults. Overall, 81 individuals of RPW were successfully collected after 16 weeks of pheromone trapping (3rd September until 28th November 2018). The trend of weekly RPW captured was eventually decreasing from Week 1 to Week 16 but showed a sudden increment in Week 10. Pandan cultivar plot captured the highest number of adult RPW compared with Mawa cultivar plots. Result showed no significant difference between RPW sexes percentage of sexes which was 53% of male and 47% of female (1:1 ratio) (U= 0.143, p > 0.05). Daily rainfall and temperature significantly reduced the trapping efficiency (R²rainfall = 0.142, p = 0.145; R²temperature = 0.0858; p = 0.258). These results will be the baseline information for future study regarding control management strategy using pheromone-based mass trapping system.

scholarly journals Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

2020 ◽  
Vol 17 (3) ◽  
pp. 194-204
Author(s):  
Indarto Indarto ◽  
Rufiani Nadzirah ◽  
Hadrian Reksa Belagama
Keyword(s):  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Vegetation Density ◽  
Residential Areas ◽  
Primary Input ◽  
Normalised Difference Vegetation Index ◽  
Sentinel 2A ◽  
Marginal Agricultural Land ◽  
Potential Use

Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values =  66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

scholarly journals Evaluation of Organic Camelina Crop Under Different Tillage Systems and Fertilization Types Using Proximal Remote Sensing

2020 ◽  
Vol 77 (1) ◽  
pp. 1
Author(s):  
Foteini ANGELOPOULOU ◽  
Evangelos ANASTASIOU ◽  
Spyros FOUNTAS ◽  
Dimitrios BILALIS
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth And Yield ◽  
Tillage Systems ◽  
Minimum Tillage ◽  
Spectral Vegetation Indices ◽  
Crop Parameters

A field experiment was conducted in Southern Greece to assess Normalized Difference Vegetation Index (NDVI) and Red-Edge Normalized Difference Vegetation Index (NDRE) in estimating Camelina’s crop growth and yield parameters under different tillage systems (conventional and minimum tillage) and organic fertilization types (compost, vermicompost and untreated control). A proximal canopy sensor was used to measure the aforementioned Spectral Vegetation Indices (SVIs) at different days after sowing (DAS). Camelina presented the highest values of NDVI and NDRE under compost fertilization (0.63 and 0.22 accordingly) and minimum tillage system (0.50 and 0.18 accordingly). Additionally, the highest correlations between the measured crop parameters and NDVI, NDRE were achieved at leaf development to early flowering stage. Moreover, NDRE presented the highest correlation with seed yield (R2=0.60, p<0.05) and thus it is suggested for estimating Camelina’s productivity instead of NDVI. Finally, further research is needed for adopting the use of remote sensing technologies on predicting Camelina’s crop growth and yield.

scholarly journals Kajian Transformasi Indeks Vegetasi Citra Satelit Sentinel-2A untuk Estimasi Produksi Daun Kayu Putih Menggunakan Linear Spectral Mixture Analysis

2020 ◽  
Vol 3 (1) ◽  
pp. 63
Author(s):  
Lilik Norvi Purhartanto ◽  
Projo Danoedoro ◽  
Pramaditya Wicaksono
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Estimation Model ◽  
Leaf Production ◽  
Melaleuca Cajuputi ◽  
Linear Spectral Mixture Analysis ◽  
Sentinel 2A ◽  
Spectral Mixture

A forest plantation area of Melaleuca cajuputi at BDH Karangmojo, BKPH Yogyakarta are 2,325.20 ha. One of the efforts to keep its sustainability is to plan the target and realization of cajuputi leaf production considerwith forest condition. Advances in remote sensing technology can be an alternative in estimating the cajuputi leaf production on large areas with an efficient time and high accuracy and able to analyze the quality of cajuputi. This study aims to examine Sentinel-2A capabilities through a relationship model of some vegetation indices integrated with vegetative factors on the production to obtain estimates of leaf production, map and test the estimation model accuracy. The method used is to classify objects in pixels with Linear Spectral Mixture Analysis and build relationship between age, number of plants and vegetation index with cajuputi leaf production. The results showed that the unmixing method has 99,66% accuracy in classifying pixels into the fraction of cajuputi. MERIS Terrestrial Chlorophyll Index of unmixing cajuputi fraction simultaneously with age and number of plants has the highest correlation with value of r = 0,668 to the production and modeled in mapping the estimated cajuputi leaf production at the research location with Standard Error of Estimate is 0,183.

scholarly journals Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

2020 ◽  
Vol 12 (21) ◽  
pp. 3558
Author(s):  
Lifeng Xie ◽  
Weicheng Wu ◽  
Xiaolan Huang ◽  
Penghui Ou ◽  
Ziyu Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Rapid Expansion ◽  
Open Pit ◽  
Open Pit Mining ◽  
Chemical Extraction

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

scholarly journals Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

2017 ◽  
Vol 6 (1) ◽  
pp. 149-158 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Arid Environments ◽  
Crop Water ◽  
Sensing Applications

Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

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