scholarly journals Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing

2016 ◽  
Vol 52 (No. 4) ◽  
pp. 270-276 ◽  
Author(s):  
Gooshbor Leila ◽  
Bavaghar Mahtab Pir ◽  
Amanollahi Jamil ◽  
Ghobari Hamed
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Insect Herbivory ◽  
Oak Forests ◽  
Landsat 8 ◽  
Landsat Images ◽  
Leaf Roller ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Tortrix Viridana

We tested the suitability of Landsat images to track defoliation by insect herbivory with focus on the oak leaf roller, Tortrix viridana (Lep.: Tortricidae). Landsat images from the period before (2002) and after the T. viridana infestation (2007, 2014) were compared in oak forests of Zagros in western Iran. The Normalised Difference Vegetation Index (NDVI) was calculated for the test area from Landsat 5, 7, and 8 images. Because the red and near-infrared spectral bands of Landsat 8 OLI sensors are different from the other two, a model for the calibration of Landsat OLI NDVI was developed. The proposed model with a correlation coefficient of 0.928 and root mean square error of 0.05 turned out to be applicable and the NDVI decreased significantly during the observation period. Taking into account the protection status of the area and small fluctuations in temperature, the decrease in NDVI could be attributed to T. viridana damage.

ESTIMATIVA DE MUDANÇAS TEMPORAIS COM O CÁLCULO DE ÍNDICES DE VEGETAÇÃO DO MUNICÍPIO DE ITABERÁ (SP)

2017 ◽  
Vol 32 (2) ◽  
pp. 195
Author(s):  
Ana Clara De Barros ◽  
Amanda Aparecida De Lima ◽  
Felipe De Souza Nogueira Tagliarini ◽  
Zacarias Xavier de Barros
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Temporal Analysis ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Chlorophyll Contents ◽  
Spectral Bands ◽  
Vegetation Water ◽  
Images Processing

O presente trabalho teve como objetivo realizar a análise temporal da cobertura vegetal, num período de 10 anos do município de Itaberá-SP, utilizando os índices de vegetação NDVI e NDWI por meio de imagens de satélite. Do ano de 2005 foram utilizadas duas imagens do Landsat 5 de órbita/ponto 221/76 e 221/77 e uma imagem de 2015 do Landsat 8, órbita/ponto 221/76. As bandas espectrais utilizadas foram: 3,4 e 5 do Landsat 5 e 4,5 e 6 do Landsat 8 que correspondem aos comprimentos de ondas do vermelho (RED), infravermelho próximo (NIR) e infravermelho médio (SWIR1), respectivamente. Através das análises dos índices, constatou que as áreas que possuem baixos valores de NDVI também possuem baixos valores de NDWI, o que indica uma vegetação que sofre estresse hídrico e com baixo teor de clorofila. Os valores mais altos indicam vegetação fotossinteticamente ativa, que contêm maior teor de umidade.PALAVRAS-CHAVE: Sensoriamento remoto, processamento de imagens, cobertura vegetal. TEMPORAL ANALYSISUSING VEGETATION INDEX OF VEGETATION COVER IN ITABERA (SP)ABSTRACT: The objective of this work was to carry out the temporal analysis of the vegetation cover, in a period of 10 years of Itaberá-SP county, making use of the vegetation index NDVI and NDWI of satellites images. Two Landsat 5’s images of 2005 with path/row 221/76 and 221/77 and one Landsat 8’s image, path/row 221/76 were used. The spectral bands used ware: 3, 4 and 5 of the Landsat 5 and 4, 5 and 6 of the Landsat 8 that correspond to red waves lengths (RED), near infrared (NIR) and medium infrared (SWIR1), respectively. It was found that areas with low NDVI values also have low NDWI values, indicating vegetation water stress and low chlorophyll contents. The highest values indicate Photosynthetically active vegetation, which contain higher moisture contents.KEYWORDS: Remote sensing, images processing, vegetal cover.

scholarly journals Performance Evaluation of Newly Proposed Seaweed Enhancing Index (SEI)

2019 ◽  
Vol 11 (12) ◽  
pp. 1434 ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z. Zaidi ◽  
Muhammad Abdullah
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Extensive Field ◽  
Reference Index ◽  
Almost All ◽  
Shortwave Infrared ◽  
Better Than

Seaweed is a valuable coastal resource for its use in food, cosmetics, and other items. This study proposed new remote sensing based seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave-infrared (SWIR) of Landsat 8 satellite data. Nine Landsat 8 satellite images of years 2014, 2016, and 2018 for the January, February, and March months were utilized to test the performance of SEI. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using the SEI, normalized difference vegetation index (NDVI), and floating algae index (FAI). Seaweed locations recorded during a field survey on February 26, 2014, were used to determine threshold values for all three indices. The accuracy of SEI was compared with NDVI while placing FAI as the reference index. The accuracy of NDVI and SEI were assessed by matching their spatial extent of seaweed cover with FAI enhanced seaweed area. SEI images of January 2016, February 2018, and March 2018 enhanced less than 50 percent of the corresponding FAI total seaweed areas. However, on these dates the NDVI performed very well, matching more than 95 percent of FAI seaweed coverage. Except for these three times, the performance of SEI in the remaining six images was either similar to NDVI or even better than NDVI. SEI enhanced 99 percent of FAI seaweed cover on January 2018 image. Overall, seaweed area not covered by FAI was greater in SEI than NDVI in almost all images, which needs to be further explored in future studies by collecting extensive field information to validate SEI mapped additional area beyond the extent of FAI seaweed cover. Based on these results, in the majority of the satellite temporal images selected for this study, the performance of the newly proposed index—SEI, was found either better than or similar to NDVI.

scholarly journals Identificación de perturbaciones en el bosque húmedo tropical colombiano usando series temporales de imágenes satelitales Landsat mediante el algoritmo Landtrendr

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Response ◽  
Short Wave ◽  
Forest Vegetation ◽  
Landsat Images ◽  
Spectral Bands

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>

scholarly journals Performance Assessment of Newly Developed Seaweed Enhancing Index

2018 ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z. Zaidi ◽  
Muhammad Abdullah
Keyword(s):  
Coastal Waters ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Field Surveys ◽  
Spectral Bands ◽  
Breeding Grounds ◽  
Shortwave Infrared ◽  
Better Than

Seaweeds are regarded as one of the valuable coastal resources because of their usage in human food, cosmetics, and other industrial items. They also play a significant role in providing nourishment, shelter, and breeding grounds for fish and many other sea species. This study introduces a newly developed seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave infrared (SWIR) of Landsat 8 satellite data. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using SEI, and its performance was compared with other commonly used indices - Normalized Difference Vegetation Index (NDVI) and Floating Algae Index (FAI). The accuracy of the mapping results obtained from SEI, NDVI, and FAI was checked with field verified seaweed locations. The purpose of the field surveys was to validate the results of this study and to evaluate the performance of SEI with NDVI and FAI. The performance of SEI was found better than NDVI and FAI in enhancing submerged patches of the seaweed pixels what other indices failed to do.

scholarly journals Monitoring the decline of Persian oak in Iran using remote sensing – Case study of Basht forest

2018 ◽  
Vol 19 (1) ◽  
pp. 67-73
Author(s):  
Sahar Haydari naserabad ◽  
◽  
Alireza Salehi ◽  
Zahra Azizi ◽  
Marjan Firoozy Nejad ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Temporal Distribution ◽  
Complex Problem ◽  
Oak Forests ◽  
Landsat 8 ◽  
Landsat Images ◽  
Dead Trees ◽  
The Status ◽  
Crown Dieback

Oak forest decline is a complex problem that it has started for a long time ago in the world. This problem is widelyspread because of lacking of efficient and reliable facilitiesfor monitoring these forests. In this research, the pattern of spatial-temporal distribution and decline of Iranian Oak stands in the Bashtforest wasmonitored using remote sensing. The area of study is located in the provinces of Kohgilouyeh and Boyer Ahmad in Iran. After selecting the study area,forest visitswere carried out and samples of dead trees were collected. Based ontheoak crown declineattack, the forest wasdivided into four categories: low (crown dieback 20-0 percent), medium (between 40-20% of crown dieback), high (between 60-40%) and severe (more than 60% crown dieback). The geographical locations of the harvested trees wererecorded using global positioning system (GPS). In order to determine the most appropriate vegetation index, root mean square error (RMSE) for 12 vegetation indiceswere calculated from Landsat 8 images. The results of this study provedthat ratio vegetation index (RVI) indicator with the lowest RMSE wasanappropriatetoolfor assessing the status of the Iranian Oak forests. Finally,in order to study temporal changes of the Oak forests,dieback canopy variance and RVI for 6 years(1987, 2000, 2003, 2009, 2013 and 2014) were computed from different Landsat images. The computed indices indicatedthat crown freshness witnessedsignificantand continuous decrease.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Tasya Vadya Sarira ◽  
Kenneth Clarke ◽  
Philip Weinstein ◽  
Lian Pin Koh ◽  
Megan Lewis
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
South Australia ◽  
Rapid Identification ◽  
Breeding Habitat ◽  
Mapping Accuracy ◽  
Normalised Difference Vegetation Index ◽  
Mosquito Breeding

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen’s Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals Pinus halepensis regeneration after a wildfire in a semiarid environment: assessment using multitemporal Landsat images

2011 ◽  
Vol 20 (2) ◽  
pp. 195 ◽  
Author(s):  
Sergio M. Vicente-Serrano ◽  
Fernando Pérez-Cabello ◽  
Teodoro Lasanta
Keyword(s):  
Spatial Pattern ◽  
Vegetation Index ◽  
Pinus Halepensis ◽  
Strong Relationship ◽  
Burn Severity ◽  
Spatial And Temporal Patterns ◽  
Landsat Images ◽  
Normalised Difference Vegetation Index ◽  
Spatial Differences ◽  
Terrain Elevation

We studied the spatial and temporal patterns of forest regeneration using a 24-year time series of Landsat images and the normalised difference vegetation index (NDVI) in a homogeneous Pinus halepensis forest, 3000 ha of which were extensively burned in 1995. We demonstrated a progressive slow and linear recovery in NDVI values, based on Landsat images between 1997 and 2007. The forest tended to recover to pre-disturbance conditions, both with respect to the magnitude of the NDVI and in terms of the spatial pattern. We found that the spatial differences in the rates of NDVI recovery were not affected by the burn severity. Moreover, burn severity did not affect the rates of NDVI recovery after the fire. Although highly homogeneous P. halepensis regeneration was the dominant pattern in the study area (more than the 70% of the burn area showed positive and significant trends), some spatial differences in the magnitude of change were observed. The forest tended to recover the spatial pattern corresponding to pre-fire conditions, although it was difficult to establish whether terrain elevation or previous tree size and density were the main governing factors, given the strong relationship between them.

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