Distinguishing Brush and Weeds on Rangelands Using Video Remote Sensing

1992 ◽  
Vol 6 (4) ◽  
pp. 913-921 ◽  
Author(s):  
James H. Everitt ◽  
David E. Escobar ◽  
Mario A. Alaniz ◽  
Ricardo Villarreal ◽  
Michael R. Davis
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Quantitative Data ◽  
Near Infrared ◽  
Video System ◽  
Video Imagery ◽  
Video Images ◽  
Computer Analyses ◽  
Video Remote Sensing ◽  
Color Imagery

This paper describes the application of a relatively new remote sensing tool, airborne video imagery, for distinguishing weed and brush species on rangelands. Plant species studied were false broomweed, spiny aster, and Chinese tamarisk. A multispectral video system that acquired color-infrared (CIR) composite imagery and its simultaneously synchronized three-band [near-infrared (NIR), red, and yellow-green] narrowband images was used for the false broomweed and spiny aster experiments. A conventional color camcorder video system was used to study Chinese tamarisk. False broomweed and spiny aster could be detected on CIR composite and NIR narrowband imagery, while Chinese tamarisk could be distinguished on conventional color imagery. Quantitative data obtained from digitized video images of the three species showed that their digital values were statistically different (P = 0.05) from those of associated vegetation and soil. Computer analyses of video images showed that populations of the three species could be quantified from associated vegetation. This technique permits area estimates of false broomweed, spiny aster, and Chinese tamarisk populations on rangeland and wildland areas.

Light Reflectance Characteristics and Remote Sensing of Big Bend Loco (Astragalus mollissimusvar.earlei) and Wooton Loco (Astragalus wootonii)

Weed Science ◽  
1994 ◽  
Vol 42 (1) ◽  
pp. 115-122 ◽  
Author(s):  
James H. Everitt ◽  
James V. Richerson ◽  
Mario A. Alaniz ◽  
David E. Escobar ◽  
Ricardo Villarreal ◽  
...  
Keyword(s):  
Near Infrared ◽  
Geographical Information ◽  
Aerial Photographs ◽  
Near Infrared Reflectance ◽  
Black And White ◽  
Video Imagery ◽  
Big Bend ◽  
Light Reflectance ◽  
Global Positioning ◽  
Computer Analyses

The high near-infrared reflectance (0.76 to 0.90 μm) of Big Bend loco and Wooton loco contributed significantly to their orange-red and red image tonal responses, respectively, on color-infrared aerial photographs making them distinguishable from associated vegetation and soil. Big Bend loco could also be distinguished on color-infrared and near-infrared black-and-white video imagery where it had distinct red and whitish tonal responses, respectively. Computer analyses of photographic and videographic images showed that Big Bend loco and Wooton loco populations could be quantified from other landscape features. A global positioning system was integrated with the video imagery that permitted latitude-longitude coordinates to appear on each image. The latitude-longitude data were integrated with a geographical information system to map Big Bend loco populations.

Using Remote Sensing to Distinguish Common (Isocoma coronopifolia) and Drummond Goldenweed (Isocoma drummondii)

Weed Science ◽  
1992 ◽  
Vol 40 (4) ◽  
pp. 621-628 ◽  
Author(s):  
James H. Everitt ◽  
Mario A. Alaniz ◽  
David E. Escobar ◽  
Michael R. Davis
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Bare Soil ◽  
Aerial Photographs ◽  
Canopy Reflectance ◽  
Plant Canopy ◽  
Color Film ◽  
Golden Yellow ◽  
Computer Analyses ◽  
Reflectance Measurements

Common and Drummond goldenweed are troublesome subshrubs that often invade rangelands in southern Texas. Both species produce a profusion of conspicuous golden-yellow flowers in the fall. Common goldenweed flowers from late September to mid-October, whereas Drummond goldenweed flowers from mid-November to early December. Plant canopy reflectance measurements made on both species showed that they had higher visible (0.63- to 0.69-μm waveband) reflectance than did associated plant species and bare soil during flowering. Flowering common and Drummond goldenweed plants had a yellow image on conventional color (0.40- to 0.70-μm) aerial photographs that made them distinguishable from associated plants and soil. Computer analyses of the conventional color film transparencies showed that common and Drummond goldenweed infestations could be quantified from associated vegetation and soil. Flowering common goldenweed plants could also be detected on conventional color aerial video imagery.

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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.

Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Weed Science ◽  
2004 ◽  
Vol 52 (4) ◽  
pp. 492-497 ◽  
Author(s):  
E. Raymond Hunt ◽  
James E. McMurtrey ◽  
Amy E. Parker Williams ◽  
Lawrence A. Corp
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Leafy Spurge ◽  
Hyperspectral Remote Sensing ◽  
Total Carotenoids ◽  
Physiological Basis ◽  
Infrared Wavelengths ◽  
Pigment Concentrations

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

scholarly journals Snow dunes and glazed surfaces in Antarctica: new field and remote-sensing data

2002 ◽  
Vol 34 ◽  
pp. 81-88 ◽  
Author(s):  
Massimo Frezzotti ◽  
Stefano Gandolfi ◽  
Floriana La Marca ◽  
Stefano Urbini
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accumulation Rate ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Remotely Sensed Data ◽  
Surface Mass ◽  
Glazed Surface ◽  
Transverse Dunes

AbstractAs part of the International Trans-Antarctic Scientific Expedition project, the Italian Antarctic Programme undertook two traverses from the Terra Nova station to Talos Dome and to Dome C. Along the traverses, the party carried out several tasks (drilling, glaciological and geophysical exploration). The difference in spectral response between glazed surfaces and snow makes it simple to identify these areas on visible/near-infrared satellite images. Integration of field observation and remotely sensed data allows the description of different mega-morphologic features: wide glazed surfaces, sastrugi glazed surface fields, transverse dunes and megadunes. Topography global positioning system, ground penetrating radar and detailed snow-surface surveys have been carried out, providing new information about the formation and evolution of mega-morphologic features. The extensive presence, (up to 30%) of glazed surface caused by a long hiatus in accumulation, with an accumulation rate of nil or slightly negative, has a significant impact on the surface mass balance of a wide area of the interior part of East Antarctica. The aeolian processes creating these features have important implications for the selection of optimum sites for ice coring, because orographic variations of even a few metres per kilometre have a significant impact on the snow-accumulation process. Remote-sensing surveys of aeolian macro-morphology provide a proven, high-quality method for detailed mapping of the interior of the ice sheet’s prevalent wind direction and could provide a relative indication of wind intensity.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals A Real Time Mosaic Method for Remote Sensing Video Images from UAV

2013 ◽  
Vol 04 (03) ◽  
pp. 168-172 ◽  
Author(s):  
Yang Yang ◽  
Guangmin Sun ◽  
Dequn Zhao ◽  
Bo Peng
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Video Images
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