scholarly journals Archaeological Remote Sensing Using Multi-Temporal, Drone-Acquired Thermal and Near Infrared (NIR) Imagery: A Case Study at the Enfield Shaker Village, New Hampshire

2020 ◽  
Vol 12 (4) ◽  
pp. 690 ◽  
Author(s):  
Austin Chad Hill ◽  
Elise Jakoby Laugier ◽  
Jesse Casana
Keyword(s):  
Remote Sensing ◽  
New Hampshire ◽  
Cultural Landscape ◽  
Near Infrared ◽  
Ground Surface ◽  
Sensor Technology ◽  
Aerial Imaging ◽  
Wide Range ◽  
Multi Temporal ◽  
Remote Sensing Methods

While archaeologists have long understood that thermal and multi-spectral imagery can potentially reveal a wide range of ancient cultural landscape features, only recently have advances in drone and sensor technology enabled us to collect these data at sufficiently high spatial and temporal resolution for archaeological field settings. This paper presents results of a study at the Enfield Shaker Village, New Hampshire (USA), in which we collect a time-series of multi-spectral visible light, near-infrared (NIR), and thermal imagery in order to better understand the optimal contexts and environmental conditions for various sensors. We present new methods to remove noise from imagery and to combine multiple raster datasets in order to improve archaeological feature visibility. Analysis compares results of aerial imaging with ground-penetrating radar and magnetic gradiometry surveys, illustrating the complementary nature of these distinct remote sensing methods. Results demonstrate the value of high-resolution thermal and NIR imagery, as well as of multi-temporal image analysis, for the detection of archaeological features on and below the ground surface, offering an improved set of methods for the integration of these emerging technologies into archaeological field investigations.

scholarly journals Lidar measurement of snow depth: a review

2013 ◽  
Vol 59 (215) ◽  
pp. 467-479 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Thomas H. Painter ◽  
David C. Finnegan
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Near Infrared ◽  
Ground Surface ◽  
Lidar Measurement ◽  
Snow Hydrology ◽  
Sensing Technology ◽  
Transfer Error ◽  
Scale Range ◽  
Visible Wavelengths

AbstractLaser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1 m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1 m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.

scholarly journals Application of Remote Sensing Methods to Monitor Coastal Zones

2020 ◽  
Vol 8 (6) ◽  
pp. 391 ◽  
Author(s):  
Luis Pedro Almeida ◽  
Rafael Almar
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Original Research ◽  
Coastal Zones ◽  
Special Issue ◽  
Research Papers ◽  
Sensing Applications ◽  
Wide Range ◽  
Remote Sensing Applications ◽  
Remote Sensing Methods

In this Special Issue “Application of Remote Sensing Methods to Monitor Coastal Zones” nine original research papers were published, with topics covering a wide range of ranging of remote sensing applications including coastal topography, bathymetry, land cover, and nearshore hydrodynamics [...]

scholarly journals Spektroskopi Reflektansi Sampel Tanah dan Batuan yang Mengandung Mineral Pembawa Unsur Tanah Jarang dan Radioaktif

EKSPLORIUM ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 89
Author(s):  
Arie Naftali Hawu Hede ◽  
Muhammad Anugrah Firdaus ◽  
Yogi La Ode Prianata ◽  
Mohamad Nur Heriawan ◽  
Syafrizal Syafrizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Optical Sensors ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Infrared Range ◽  
Basic Principles ◽  
Spectral Signatures ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

ABSTRAKSpektroskopi reflektansi merupakan salah satu metode nondestruktif untuk identifikasi mineral dan sebagai dasar dalam analisis pengindraan jauh (indraja) sensor optik. Penelitian ini bertujuan melakukan kajian penerapan spektroskopi reflektansi pada panjang gelombang 350–2.500 nm untuk sampel tanah dan batuan pembawa unsur tanah jarang (rare earth element-REE) dan radioaktif. Sampel diambil dari beberapa lokasi di Bangka Selatan dan Mamuju yang sebelumnya telah diidentifikasi memiliki potensi REE dan unsur radioaktif. Kurva reflektansi hasil analisis sampel dari Bangka Selatan menunjukan adanya kenampakan absorpsi yang menjadi karakteristik untuk kehadiran REE, dalam bentuk mineral monasit, zirkon, dan xenotime khususnya pada sampel yang berasal dari material tailing dan konsentrat bijih timah. Panjang gelombang yang menjadi kunci khususnya berada pada rentang visible-near infrared (VNIR; 400–1.300 nm). Sedangkan untuk sampel yang berasal dari Mamuju, yang merupakan daerah prospeksi mineral radioaktif, karakteristik spektral memperlihatkan beberapa panjang gelombang kunci terutama pada rentang shortwave infrared (1.300–2.500 nm). Hasil interpretasi menunjukkan mineral mayor berupa mineral lempung, sulfat, spesies NH4, dan mineral yang mengandung Al-OH lainnya, sedangkan untuk beberapa sampel pada panjang gelombang VNIR diidentifikasi mengandung mineral besi oksida/hidroksida. Hasil penelitian ini diharapkan dapat berguna untuk pemetaan eksplorasi REE dan radioaktif dengan menggunakan metode indraja.ABSTRACTReflectance spectroscopy is one of the nondestructive methods of mineral identification and is one of the basic principles in the remote sensing analysis using optical sensors. This research aimed at applying reflectance spectroscopy at 350–2,500 nm wavelength range for samples containing rare earth elements (REE) and radioactive minerals. Samples were taken from several locations in South Bangka and Mamuju that had previously been identified as potential location of REE and radioactive-bearing minerals. Reflectance data shows that there are absorption characteristics for REE-bearing minerals; monazite, zircon, and xenotime minerals especially from tailings and tin ore concentrate for the samples from South Bangka. The key wavelengths are specifically in the visible-near infrared range (VNIR; 400–1300 nm). For the samples from Mamuju, which is known as radioactive mineral prospecting areas, spectral characteristics provide information that there are spectral signatures in the shortwave infrared range (1,300–2,500 nm). The results of major mineral interpretations include clay minerals, sulfates, NH4 species, and other minerals containing Al-OH. However, some samples at the VNIR wavelength identified as iron oxide/hydroxide minerals. It is hoped that these results can be useful for REE and radioactive exploration mapping using remote sensing methods.

scholarly journals Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 28 ◽  
Author(s):  
Ibrahim Wahab ◽  
Ola Hall ◽  
Magnus Jirström
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Field Methods ◽  
Smallholder Farms ◽  
Sub Saharan ◽  
Remote Sensing Methods ◽  
Spad Readings

The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a better indicator of crop vigor and a better estimator of yields—r = 0.372 and r = 0.393 for mean and maximum GNDVI respectively at about five weeks after planting compared to in-field methods like SPAD readings at the same stage (r = 0.259). Our study therefore demonstrates that GNDVI derived from UAV imagery is a reliable and timeous predictor of crop vigor and yields and that this is applicable even in complex smallholder farms in SSA.

scholarly journals Monitoring the Severity of Pantana phyllostachysae Chao on Bamboo Using Leaf Hyperspectral Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4146
Author(s):  
Xuying Huang ◽  
Zhanghua Xu ◽  
Xu Yang ◽  
Jingming Shi ◽  
Xinyu Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Gradient Boosting ◽  
Leaf Water Content ◽  
Leaf Nitrogen Content ◽  
Light Gradient ◽  
Bamboo Leaves ◽  
Remote Sensing Methods ◽  
The Impact

Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge on whether the severity of this pest could be effectively monitored by using remote sensing methods is very limited. To fill this gap, this study aimed to identify the PPC damage of moso bamboo leaves using hyperspectral data. Specifically, we investigated differences in relative chlorophyll content (RCC), leaf water content (LWC), leaf nitrogen content (LNC), and hyperspectral spectrum among healthy, damaged (mildly damage, moderately damage, severely damage), and off-year bamboo leaves. Then, the hyperspectral indices sensitive to pest damage were selected by recursive feature elimination (RFE). The PPC damage identification model was constructed using the light gradient boosting machine (LightGBM) algorithm. We designed two different scenarios, without (A) and with (B) off-year samples, to evaluate the impact of off-year leaves on identification results. The RCC, the LWC, and the LNC of damaged leaves generally showed clear declined trends with the deterioration of damaged severity. The RCC and the LNC of off-year leaves were significantly lower than those of healthy and damaged leaves, whereas the LWC of off-leaves was significantly different from that of damaged leaves. The pest infestation caused noticeable distortion of leaf spectrum, increases in red and shortwave infrared bands, and decreases in green and near-infrared bands. The magnitude of reflectance change increased with the pest severity. The reflectance of off-year leaves in visible and near-infrared regions was distinguishably higher than that of healthy and damaged leaves. The overall accuracy (OA) of the constructed model for the identification of leaves with different degrees of damage severity reached 81.51%. When off-year, healthy, and damaged leaves were lumped together, the OA of the constructed model decreased by 5%. About half of the off-year leaf samples were misclassified into the damaged group. The identification of off-year leaves is a challenge for monitoring PPC damage using hyperspectral data. These results can provide practical guidance for monitoring PPC using remote sensing methods.

scholarly journals Low cost infrared and near infrared sensors for UAVs

2014 ◽  
Vol XL-1 ◽  
pp. 1-7 ◽  
Author(s):  
S. T. Aden ◽  
J. P. Bialas ◽  
Z. Champion ◽  
E. Levin ◽  
J. L. McCarty
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Low Cost ◽  
Cost Effective ◽  
Infrared Sensors ◽  
Management Options ◽  
Time Saving ◽  
Wide Range ◽  
Infrared Cameras ◽  
Wildland Firefighting

Thermal remote sensing has a wide range of applications, though the extent of its use is inhibited by cost. Robotic and computer components are now widely available to consumers on a scale that makes thermal data a readily accessible resource. In this project, thermal imagery collected via a lightweight remote sensing Unmanned Aerial Vehicle (UAV) was used to create a surface temperature map for the purpose of providing wildland firefighting crews with a cost-effective and time-saving resource. The UAV system proved to be flexible, allowing for customized sensor packages to be designed that could include visible or infrared cameras, GPS, temperature sensors, and rangefinders, in addition to many data management options. Altogether, such a UAV system could be used to rapidly collect thermal and aerial data, with a geographic accuracy of less than one meter.

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

scholarly journals Normalized Dryness Built-up Index (NDBI) to Detect Settlement Change In Buleleng Sub-District

2021 ◽  
Vol 20 (1) ◽  
pp. 46
Author(s):  
Muhammad Rahman ◽  
A Sediyo Adi Nugraha
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Settlement Development ◽  
Landsat 7 ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

This research aims to find out the development of settlements that occur over the next 20 years. Monitoring the development of settlements is carried out by remote sensing methods using Landsat 7 ETM+ imagery and Landsat 8 OLI imagery. Landsat 7 ETM+ used in 2000, and Landsat 8 OLI used in 2019. The algorithm is used to identify settlement development using the Normalized Dryness Built-up Index (NDBI). This algorithm uses two bands, such as Near-infrared and shortwave infrared, to calculate. The results showed that the growth of settlements occurred very significant because, in 2000, the number of settlements amounted to 628.2 hectares and in 2019 amounted to 1891.8 hectares. The increase in settlements occurred throughout the region in the Buleleng sub-district. Therefore, it can be concluded that NDBI can be used to monitor the development of settlements and the increase in settlements occurring as much as 28 % over 20 years.

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