scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

scholarly journals Aplication of Landsat 8 Oli for the total suspended solid (TSS) mapping in the Tri An reservoir, Dong Nai province

2020 ◽  
Vol 4 (1) ◽  
pp. first
Author(s):  
Pham Thanh Luu ◽  
Nguyen Thi My Le ◽  
Trinh Hong Phuong ◽  
Tran Thi Hoang Yen ◽  
Tran Thanh Thai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Surface Water ◽  
Near Infrared ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Near Infrared Reflectance

Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies. Total suspended solid (TSS) is an important water quality parameter and a surrogate for the water clarity. It can be used as the indicator of sediment in the reservoir, which usually consists of silt, fine sand and microorganisms. This study aimed to utilize the remote sensing technology, in particular Landsat 8 Operational Land Imager (Landsat 8 OLI), to determine the amount of TSS concentration as well as the spatial distribution of TSS concentration in the surface water of the Tri An reservoir. The relationship between field TSS data collected in March, 2020 and the reflectance values of the the Landsat 8 Oli images was investigated. Results showed that there was a strong linear relationshiop between TSS concentration and the reflectance of the red and near infrared reflectance bands from the Landsat 8 Oli (r ranged from 0.58–0.93), in which the ratio of the red band produced the best correlation with the TSS (r = 0.93, with a standard error of 0.6–1.39 mg/L). Based on the linear regression equation, the TSS concentration calculated from the red reflectance values was used for mapping the spatial distribution of TSS in the surface water of the Tri An reservoir. Our results confirmed the accuracy and potential of using the single band from Landsat 8 OLI for mapping the spatial distribution of TSS in the Tri An reservoir.

scholarly journals Modelagem de variáveis dendrométricas em povoamentos florestais utilizando imagens de média resolução espacial (Modeling of biophysical variables in forest stands using medium spatial resolution images)

2016 ◽  
Vol 9 (6) ◽  
pp. 1969
Author(s):  
Elisiane Alba ◽  
Emanuel Araújo Silva ◽  
Juliana Marchesan ◽  
Letícia Pedrali ◽  
Rudiney Soares Pereira ◽  
...  
Keyword(s):  
Population Density ◽  
Near Infrared ◽  
Tree Density ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Forest Stands ◽  
Infrared Band ◽  
Radiometric Correction ◽  
Vegetation Indexes

Objetivou-se avaliar as imagens Landsat 8/OLI na obtenção de estimativas do volume florestal e densidade populacional de plantios de E. grandis. Para tanto, utilizaram-se 42 unidades amostrais de povoamentos com 18 e 25 anos, mensurando-se os parâmetros dendrométricos Diâmetro à Altura do Peito (DAP), altura total e densidade de árvores. Foi realizada a correção radiométrica da imagem Landsat 8/OLI, obtendo a reflectância de superfície das bandas e índices de vegetação, a qual foi relacionada com as variáveis florestais, ajustando equações de estimativas por meio do método forward. Para os plantios com 18 anos, a equação ajustada explicou 87% da variabilidade do volume com as variáveis SAVI e NDVI presentes no modelo. A densidade populacional foi explicada pelo SR e DVI (R²=0,56). Aos 25 anos, o modelo contendo a banda do infravermelho próximo (B5) e o índice SR respondeu a 92% da variação total do volume florestal.  Nesta idade, a densidade populacional não apresentou correlação positiva. As propriedades espectrais da imagem apresentaram sensibilidade às variáveis dendrométricas, permitindo o monitoramento do desenvolvimento dos povoamentos florestais, justificando a aplicabilidade deste método.    A B S T R A C T This study aims at evaluate Landsat 8/OLI images in obtaining of estimates of the volume and tree density in plantations E. grandis. Therefore, was used 42 sampling unities of stands with 18 e 25 years, measurand the dendrometric parameters Diameter at Breast Height, total height and tree density. Was performed the radiometric correction of the Landsat 8/OLI image, obtaining the surface reflectance of the bands and vegetation indexes, which was related with variables forestry, adjusting equation of estimates through of the method forward. For plantations with 18 years, adjusting equation explained 87% of the volume variability with the variables SAVI and NDVI present in the model. Already the population density was explained by indexes SR and DVI (R²= 0.56). At 25 years, the model containg the near infrared band (B5) and the SR index responded to 92% of the total variation of the volume forestry. This age, the population density showed no positive correlation. The spectral properties of the image demonstrated sensitivity to variables dendrometric, allowing the monitoring of the development of forest stands, justifying the applicability of this method. Keywords: index vegetation, spectral reflectance, wood volume.   

scholarly journals Comparison of Spectral Reflectance-Based Smart Farming Tools and a Conventional Approach to Determine Herbage Mass and Grass Quality on Farm

2020 ◽  
Vol 12 (19) ◽  
pp. 3256
Author(s):  
Leonie Hart ◽  
Olivier Huguenin-Elie ◽  
Roy Latsch ◽  
Michael Simmler ◽  
Sébastien Dubois ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Vegetation Period ◽  
Conventional Approach ◽  
Spatiotemporal Variability ◽  
Growth Stages ◽  
Harvest Scheduling ◽  
Analysis Model ◽  
Near Infrared Reflectance ◽  
Smart Farming

The analysis of multispectral imagery (MSI) acquired by unmanned aerial vehicles (UAVs) and mobile near-infrared reflectance spectroscopy (NIRS) used on-site has become increasingly promising for timely assessments of grassland to support farm management. However, a major challenge of these methods is their calibration, given the large spatiotemporal variability of grassland. This study evaluated the performance of two smart farming tools in determining fresh herbage mass and grass quality (dry matter, crude protein, and structural carbohydrates): an analysis model for MSI (GrassQ) and a portable on-site NIRS (HarvestLabTM 3000). We compared them to conventional look-up tables used by farmers. Surveys were undertaken on 18 multi-species grasslands located on six farms in Switzerland throughout the vegetation period in 2018. The sampled plots represented two phenological growth stages, corresponding to an age of two weeks and four to six weeks, respectively. We found that neither the performance of the smart farming tools nor the performance of the conventional approach were satisfactory for use on multi-species grasslands. The MSI-model performed poorly, with relative errors of 99.7% and 33.2% of the laboratory analyses for herbage mass and crude protein, respectively. The errors of the MSI-model were indicated to be mainly caused by grassland and environmental characteristics that differ from the relatively narrow Irish calibration dataset. The On-site NIRS showed comparable performance to the conventional Look-up Tables in determining crude protein and structural carbohydrates (error ≤ 22.2%). However, we identified that the On-site NIRS determined undried herbage quality with a systematic and correctable error. After corrections, its performance was better than the conventional approach, indicating a great potential of the On-site NIRS for decision support on grazing and harvest scheduling.

scholarly journals Evaluation and Normalization of Topographic Effects on Vegetation Indices

2020 ◽  
Vol 12 (14) ◽  
pp. 2290
Author(s):  
Rui Chen ◽  
Gaofei Yin ◽  
Guoxiang Liu ◽  
Jing Li ◽  
Aleixandre Verger
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Topographic Effects ◽  
Terrestrial Vegetation ◽  
Mountainous Areas ◽  
Near Infrared Reflectance ◽  
Illumination Condition ◽  
Vegetation Monitoring ◽  
Local Illumination

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

scholarly journals Effect of Image Fusion on Vegetation Index Quality—A Comparative Study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1550 ◽  
Author(s):  
Prakash Ghimire ◽  
Deng Lei ◽  
Nie Juan
Keyword(s):  
Image Fusion ◽  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pass Filter ◽  
Modis Imagery

In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).

scholarly journals The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

2019 ◽  
Vol 11 (13) ◽  
pp. 1561 ◽  
Author(s):  
Tomáš Klouček ◽  
Jan Komárek ◽  
Peter Surový ◽  
Karel Hrach ◽  
Přemysl Janata ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Near Infrared ◽  
Carbon Fixation ◽  
Water Cycle ◽  
Low Cost ◽  
Vegetation Indices ◽  
Nutrient Cycle ◽  
Visual Interpretation ◽  
Infrared Band ◽  
Individual Level

The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkonoše Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%–96% across the time periods. The performance of the indices based on near-infrared band was lower.

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 Remote Sensing for Assessing Rhizoctonia Crown and Root Rot Severity in Sugar Beet

Plant Disease ◽  
2012 ◽  
Vol 96 (4) ◽  
pp. 497-505 ◽  
Author(s):  
Gregory J. Reynolds ◽  
Carol E. Windels ◽  
Ian V. MacRae ◽  
Soizik Laguette
Keyword(s):  
Remote Sensing ◽  
Sugar Beet ◽  
Root Rot ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Root Surface ◽  
Near Infrared Reflectance ◽  
Initial Appearance ◽  
Crown And Root Rot ◽  
Field Plots

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani AG-2-2, is an increasingly important disease of sugar beet in Minnesota and North Dakota. Disease ratings are based on subjective, visual estimates of root rot severity (0-to-7 scale, where 0 = healthy and 7 = 100% rotted, foliage dead). Remote sensing was evaluated as an alternative method to assess RCRR. Field plots of sugar beet were inoculated with R. solani AG 2-2 IIIB at different inoculum densities at the 10-leaf stage in 2008 and 2009. Data were collected for (i) hyperspectral reflectance from the sugar beet canopy and (ii) visual ratings of RCRR in 2008 at 2, 4, 6, and 8 weeks after inoculation (WAI) and in 2009 at 2, 3, 5, and 9 WAI. Green, red, and near-infrared reflectance and several calculated narrowband and wideband vegetation indices (VIs) were correlated with visual RCRR ratings, and all resulted in strong nonlinear regressions. Values of VIs were constant until at least 26 to 50% of the root surface was rotted (RCRR = 4, wilting of foliage starting to develop) and then decreased significantly as RCRR ratings increased and plants began dying. RCRR also was detected using airborne, color-infrared imagery at 0.25- and 1-m resolution. Remote sensing can detect RCRR but not before initial appearance of foliar symptoms.

scholarly journals Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images

2015 ◽  
Vol 16 (10) ◽  
pp. 832-844 ◽  
Author(s):  
Jing Wang ◽  
Jing-feng Huang ◽  
Xiu-zhen Wang ◽  
Meng-ting Jin ◽  
Zhen Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Time Series Images ◽  
Rice Phenology

scholarly journals Prediction of dendrometric variables by optical data in Pinus taeda L. in the south of Brazil

2021 ◽  
Vol 8 (2) ◽  
pp. 1433-1443
Author(s):  
Carla Talita Pertille ◽  
Marcos Felipe Nicoletti ◽  
Larissa Regina Topanotti ◽  
Luís Paulo Baldiserra Schorr
Keyword(s):  
Pinus Taeda ◽  
Forest Inventory ◽  
Vegetation Indices ◽  
Basal Area ◽  
Optical Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pinus Taeda L ◽  
South Of Brazil ◽  
Pinus Spp

The objective of this work was to estimate the basal area and volume of a Pinus taeda L. settlement located in Santa Catarina, correlating data from an orbital image of the Landsat-8 / OLI sensor and forest inventory. In this sense, a forest research was carried out, with a random sampling process using the fixed area method. 20 circular parcels of 400 m² were allocated. An orbital image of the Landsat-8 / OLI sensor was used and 10 average vegetation indices per plot were calculated. These were correlated as variables of volume and basal area per plot, decorative by the forest inventory. The index with the best correlation for the volume was GNDVI with 0.47 and for a basal area, the MVI with 0.51. The adjustment of the regression models showed adjusted R² indices of 0.5639 and Syx of 13.31% for volume, and 0.5213 and 11.93% for the basal area. It was possible to estimate the volume and basal area of the stands through the spectral data, however, it is recommended that this same technique be tested in other species of the genus Pinus spp. and with high spatial resolution media.

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