Characterization of Alpine Vegetation Cover Using Satellite Remote Sensing in the Front Ranges, St. Elias Mountains, Yukon Territory

1992 ◽  
Vol 2 (3) ◽  
pp. 90 ◽  
Author(s):  
Bradley A. Wilson ◽  
Steven E. Franklin
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Remote Sensing ◽  
Yukon Territory ◽  
Alpine Vegetation

scholarly journals Eco-Acoustic Indices to Evaluate Soundscape Degradation Due to Human Intrusion

2020 ◽  
Vol 12 (24) ◽  
pp. 10455
Author(s):  
Roberto Benocci ◽  
Giovanni Brambilla ◽  
Alessandro Bisceglie ◽  
Giovanni Zambon
Keyword(s):  
Remote Sensing ◽  
Environmental Degradation ◽  
Satellite Remote Sensing ◽  
Species Abundance ◽  
Urban Parks ◽  
Habitat Destruction ◽  
Natural Environments ◽  
Important Goal ◽  
Audio Recordings

The characterization of environmental quality and the detection of the first sign of environmental stress, with reference to human intrusion, is currently a very important goal to prevent further environmental degradation, and consequently habitat destruction, in order to take appropriate preservation measures. Besides the traditional field observation and satellite remote sensing, geophonic and/or biophonic sounds have been proposed as potential indicators of terrestrial and aquatic settings’ status. In this work, we analyze a series of short audio-recordings taken in urban parks and bushes characterized by the presence of different human-generated-noise and species abundance. This study aims to propose a tool devoted to the investigation of urban and natural environments in a context with different soundscape qualities, such as, for example, those that can be found in urban parks. The analysis shows the ways in which it is possible to distinguish among different habitats by the use of a combination of different acoustic and sound ecology indices.

scholarly journals Evaluation of the Accuracy of the Field Quadrat Survey of Alpine Grassland Fractional Vegetation Cover Based on the Satellite Remote Sensing Pixel Scale

2019 ◽  
Vol 8 (11) ◽  
pp. 497 ◽  
Author(s):  
Chen ◽  
Zhao ◽  
Zhang ◽  
Qin ◽  
Yi
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Remote Sensing ◽  
Alpine Grassland ◽  
Tibet Plateau ◽  
Sample Plot ◽  
Fractional Vegetation Cover ◽  
Quadrat Survey ◽  
Pixel Scale ◽  
Degree Of Fragmentation

The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation of the accuracy of the field quadrat survey of FVC based on the satellite remote sensing pixel scale is inadequate, especially in the alpine grassland of the Qinghai-Tibet Plateau. In this paper, five different alpine grasslands were examined, the accuracy of the FVC obtained by the photography method was analyzed, and the influence of the number of samples on the field survey results was studied. First, the results show that the threshold method could accurately extract the vegetation information in the photos and obtain the FVC with high accuracy and little subjective interference. Second, the number of samples measured on the ground was logarithmically related to the accuracy of the FVC of the sample plot (p < 0.001). When the number of samples was larger, the accuracy of the FVC of the sample plot was higher and closer to the real value, and the stability of data also increased with the increase of the number of samples. Third, the average FVC of the measured quadrats on the ground was able to represent the FVC of the sample plot, but on the basis that there were enough measured quadrats. Finally, the results revealed that the degree of fragmentation reflecting the state of ground vegetation affects the acquisition accuracy of FVC. When the degree of fragmentation of the sample plot is higher, the number of samples needed to achieve the accuracy index is higher. Our results suggest that when obtaining the FVC on the satellite remote sensing pixel scale, the number of samples measured on the ground is an important factor affecting the accuracy, which cannot be ignored.

scholarly journals Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

2021 ◽  
Author(s):  
Shawn D Taylor ◽  
Dawn M Browning ◽  
Ruben A Baca ◽  
Feng Gao
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Scale Effects ◽  
Simulated Data ◽  
Area Index ◽  
Land Surface Phenology

Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.

scholarly journals Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?

2022 ◽  
Vol 14 (2) ◽  
pp. 380
Author(s):  
Birgitta Putzenlechner ◽  
Philip Marzahn ◽  
Philipp Koal ◽  
Arturo Sánchez-Azofeifa
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Vegetation Cover ◽  
Satellite Remote Sensing ◽  
Coniferous Forest ◽  
Spatiotemporal Variability ◽  
Fractional Vegetation Cover ◽  
Spatially Distributed ◽  
Sentinel 2

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.

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