scholarly journals Vegetation monitoring using multispectral sensors — best practices and lessons learned from high latitudes

2019 ◽  
Vol 7 (1) ◽  
pp. 54-75 ◽  
Author(s):  
Jakob J. Assmann ◽  
Jeffrey T. Kerby ◽  
Andrew M. Cunliffe ◽  
Isla H. Myers-Smith
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Control Point ◽  
Lessons Learned ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Vegetation Monitoring ◽  
Technological Advances ◽  
Normalised Difference Vegetation Index ◽  
Multispectral Sensors

Rapid technological advances have dramatically increased affordability and accessibility of unmanned aerial vehicles (UAVs) and associated sensors. Compact multispectral drone sensors capture high-resolution imagery in visible and near-infrared parts of the electromagnetic spectrum, allowing for the calculation of vegetation indices, such as the normalised difference vegetation index (NDVI) for productivity estimates and vegetation classification. Despite the technological advances, challenges remain in capturing high-quality data, highlighting the need for standardized workflows. Here, we discuss challenges, technical aspects, and practical considerations of vegetation monitoring using multispectral drone sensors and propose a workflow based on remote sensing principles and our field experience in high-latitude environments, using the Parrot Sequoia (Pairs, France) sensor as an example. We focus on the key error sources associated with solar angle, weather conditions, geolocation, and radiometric calibration and estimate their relative contributions that can lead to uncertainty of more than ±10% in peak season NDVI estimates of our tundra field site. Our findings show that these errors can be accounted for by improved flight planning, metadata collection, ground control point deployment, use of reflectance targets, and quality control. With standardized best practice, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.

scholarly journals Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes

2018 ◽  
Author(s):  
Jakob J Assmann ◽  
Jeffrey T Kerby ◽  
Andrew M Cunliffe ◽  
Isla H Myers-Smith
Keyword(s):  
Best Practices ◽  
Spatial Data ◽  
Ecological Monitoring ◽  
Lessons Learned ◽  
Ease Of Use ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
High Latitudes ◽  
Normalised Difference Vegetation Index ◽  
Multispectral Sensors

AbstractEmerging drone technologies have the potential to revolutionise ecological monitoring. The rapid technological advances in recent years have dramatically increased affordability and ease of use of Unmanned Aerial Vehicles (UAVs) and associated sensors. Compact multispectral sensors, such as the Parrot Sequoia (Paris, France) and MicaSense RedEdge (Seattle WA, USA) capture spectrally accurate high-resolution (fine grain) imagery in visible and near-infrared parts of the electromagnetic spectrum, providing supplement to satellite and aircraft-based imagery. Observations of surface reflectance can be used to calculate vegetation indices such as the Normalised Difference Vegetation Index (NDVI) for productivity estimates and vegetation classification. Despite the advances in technology, challenges remain in capturing consistently high-quality data, particularly when operating in extreme environments such as the high latitudes. Here, we summarize three years of ecological monitoring with drone-based multispectral sensors in the remote Canadian Arctic. We discuss challenges, technical aspects and practical considerations, and highlight best practices that emerged from our experience, including: flight planning, factoring in weather conditions, and geolocation and radiometric calibration. We propose a standardised methodology based on established principles from remote sensing and our collective field experiences, using the Parrot Sequoia sensor as an example. With these good practises, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.

scholarly journals Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5487 ◽  
Author(s):  
Tomáš Klouček ◽  
David Moravec ◽  
Jan Komárek ◽  
Ondřej Lagner ◽  
Přemysl Štych
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Spatial Data ◽  
Vegetation Index ◽  
Identification System ◽  
Landsat 8 ◽  
Economic Point ◽  
Normalised Difference Vegetation Index ◽  
Cropland Change

Grassland is one of the most represented, while at the same time, ecologically endangered, land cover categories in the European Union. In view of the global climate change, detecting its change is growing in importance from both an environmental and a socio-economic point of view. A well-recognised tool for Land Use and Land Cover (LULC) Change Detection (CD), including grassland changes, is Remote Sensing (RS). An important aspect affecting the accuracy of change detection is finding the optimal indicators of LULC changes (i.e., variables). Inappropriately selected variables can produce inaccurate results burdened with a number of uncertainties. The aim of our study is to find the most suitable variables for the detection of grassland to cropland change, based on a pair of high resolution images acquired by the Landsat 8 satellite and from the vector database Land Parcel Identification System (LPIS). In total, 59 variables were used to create models using Generalised Linear Models (GLM), the quality of which was verified through multi-temporal object-based change detection. Satisfactory accuracy for the detection of grassland to cropland change was achieved using all of the statistically identified models. However, a three-variable model can be recommended for practical use, namely by combining the Normalised Difference Vegetation Index (NDVI), Wetness and Fifth components of Tasselled Cap. Increasing number of variables did not significantly improve the accuracy of detection, but rather complicated the interpretation of the results and was less accurate than detection based on the original Landsat 8 images. The results obtained using these three variables are applicable in landscape management, agriculture, subsidy policy, or in updating existing LULC databases. Further research implementing these variables in combination with spatial data obtained by other RS techniques is needed.

scholarly journals Thermal and Multispectral Remote Sensing for the Detection and Analysis of Archaeologically Induced Crop Stress at a UK Site

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 61
Author(s):  
Katherine James ◽  
Caroline J. Nichol ◽  
Tom Wade ◽  
Dave Cowley ◽  
Simon Gibson Poole ◽  
...  
Keyword(s):  
Data Collection ◽  
Vegetation Index ◽  
Agricultural Practices ◽  
Archaeological Site ◽  
Electromagnetic Spectrum ◽  
Thermal Imager ◽  
Thermal Sensors ◽  
Large Area ◽  
Normalised Difference Vegetation Index

In intensively cultivated landscapes, many archaeological remains are buried under the ploughed soil, and detection depends on crop proxies that express subsurface features. Traditionally these proxies have been documented in visible light as contrasting areas of crop development commonly known as cropmarks. However, it is recognised that reliance on the visible electromagnetic spectrum has inherent limitations on what can be documented, and multispectral and thermal sensors offer the potential to greatly improve our ability to detect buried archaeological features in agricultural fields. The need for this is pressing, as ongoing agricultural practices place many subsurface archaeological features increasingly under threat of destruction. The effective deployment of multispectral and thermal sensors, however, requires a better understanding of when they may be most effective in documenting archaeologically induced responses. This paper presents the first known use of the FLIR Vue Pro-R thermal imager and Red Edge-M for exploring crop response to archaeological features from two UAV surveys flown in May and June 2019 over a known archaeological site. These surveys provided multispectral imagery, which was used to create vegetation index (VI) maps, and thermal maps to assess their effectiveness in detecting crop responses in the temperate Scottish climate. These were visually and statistically analysed using a Mann Whitney test to compare temperature and reflectance values. While the study was compromised by unusually damp conditions which reduced the potential for cropmarking, the VIs (e.g., Normalised Difference Vegetation Index, NDVI) did show potential to detect general crop stress across the study site when they were statistically analysed. This demonstrates the need for further research using multitemporal data collection across case study sites to better understand the interactions of crop responses and sensors, and so define appropriate conditions for large-area data collection. Such a case study-led multitemporal survey approach is an ideal application for UAV-based documentation, especially when “perfect” conditions cannot be guaranteed.

scholarly journals Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

2020 ◽  
Vol 12 (3) ◽  
pp. 514 ◽  
Author(s):  
Dominic Fawcett ◽  
Cinzia Panigada ◽  
Giulia Tagliabue ◽  
Mirco Boschetti ◽  
Marco Celesti ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Radiometric Calibration ◽  
Quality Of Data ◽  
Vegetation Monitoring ◽  
Operational Conditions ◽  
Spatial Consistency ◽  
Normalised Difference Vegetation Index ◽  
Airborne Imaging

Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals Greenspace, Air Pollution, Neighborhood Factors, and Preeclampsia in a Population-Based Case-Control Study in California

2021 ◽  
Vol 18 (10) ◽  
pp. 5127
Author(s):  
Kari A. Weber ◽  
Wei Yang ◽  
Evan Lyons ◽  
David K. Stevenson ◽  
Amy M. Padula ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Low Income ◽  
Vegetation Index ◽  
Census Data ◽  
Effect Modification ◽  
Severe Preeclampsia ◽  
Quality Data ◽  
Poverty Level ◽  
Neighborhood Factors

To investigate preeclampsia etiologies, we examined relationships between greenspace, air pollution, and neighborhood factors. Data were from hospital records and geocoded residences of 77,406 women in San Joaquin Valley, California from 2000 to 2006. Preeclampsia was divided into mild, severe, or superimposed onto pre-existing hypertension. Greenspace within 100 and 500 m residential buffers was estimated from satellite data using normalized difference vegetation index (NDVI). Air quality data were averaged over pregnancy from daily 24-h averages of nitrogen dioxide, particulate matter <10 µm (PM10) and <2.5 µm (PM2.5), and carbon monoxide. Neighborhood socioeconomic (SES) factors included living below the federal poverty level and median annual income using 2000 US Census data. Odds of preeclampsia were estimated using logistic regression. Effect modification was assessed using Wald tests. More greenspace (500 m) was inversely associated with superimposed preeclampsia (OR = 0.57). High PM2.5 and low SES were associated with mild and severe preeclampsia. We observed differences in associations between greenspace (500 m) and superimposed preeclampsia by neighborhood income and between greenspace (500 m) and severe preeclampsia by PM10, overall and among those living in higher SES neighborhoods. Less greenspace, high particulate matter, and high-poverty/low-income neighborhoods were associated with preeclampsia, and effect modification was observed between these exposures. Further research into exposure combinations and preeclampsia is warranted.

Foundations of Advanced Neuroanatomy: Technical Guidelines for Specimen Preparation, Dissection, and 3D-Photodocumentation in a Surgical Anatomy Laboratory

2019 ◽  
Author(s):  
Luciano César PC Leonel ◽  
Lucas P. Carlstrom ◽  
Christopher S. Graffeo ◽  
Avital Perry ◽  
Carlos Diogenes Pinheiro-Neto ◽  
...  
Keyword(s):  
Oral Tradition ◽  
Dynamic Range ◽  
Tap Water ◽  
Three Dimensional ◽  
High Dynamic Range ◽  
Surgical Anatomy ◽  
Lessons Learned ◽  
Specimen Preparation ◽  
Technological Advances ◽  
Key Steps

Abstract Objective This study was aimed to provide a key update to the seminal works of Prof. Albert L. Rhoton Jr., MD, with particular attention to previously unpublished insights from the oral tradition of his fellows, recent technological advances including endoscopy, and high-dynamic range (HDR) photodocumentation, and, local improvements in technique, we have developed to optimize efficient neuroanatomic study. Methods Two formaldehyde-fixed cadaveric heads were injected with colored latex to demonstrate step-by-step specimen preparation for microscopic or endoscopic dissection. One formaldehyde-fixed brain was utilized to demonstrate optimal three-dimensional (3D) photodocumentation techniques. Results Key steps of specimen preparation include vessel cannulation and securing, serial tap water flushing, specimen drainage, vessel injection with optimized and color-augmented latex material, and storage in 70% ethanol. Optimizations for photodocumentation included the incorporation of dry black drop cloth and covering materials, an imaging-oriented approach to specimen positioning and illumination, and single-camera stereoscopic capture techniques, emphasizing the three-exposure-times-per-eye approach to generating images for HDR postprocessing. Recommended tools, materials, and technical nuances were emphasized throughout. Relative advantages and limitations of major 3D projection systems were comparatively assessed, with sensitivity to audience size and purpose specific recommendations. Conclusion We describe the first consolidated step-by-step approach to advanced neuroanatomy, including specimen preparation, dissection, and 3D photodocumentation, supplemented by previously unpublished insights from the Rhoton fellowship experience and lessons learned in our laboratories in the past years such that Prof. Rhoton's model can be realized, reproduced, and expanded upon in surgical neuroanatomy laboratories worldwide.

Sign in / Sign up

Export Citation Format

Share Document