Remote sensing can locate and assess the changing abundance of hollow-bearing trees for wildlife in Australian native forests

2014 ◽  
Vol 41 (8) ◽  
pp. 703 ◽  
Author(s):  
Christopher J. Owers ◽  
Rodney P. Kavanagh ◽  
Eleanor Bruce
Keyword(s):  
Remote Sensing ◽  
Early Stage ◽  
Structural Parameters ◽  
Low Cost ◽  
Species Abundance ◽  
Aerial Imagery ◽  
Individual Tree ◽  
Persistent Problem ◽  
Native Forests ◽  
Remote Sensing Techniques

Context Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and diversity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest. Aims The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife. Methods The data were high-resolution, multispectral aerial imagery and light detection and ranging (Lidar). A ground-based survey of 194 trees, 96 Eucalyptus crebra and 98 E. chloroclada and E. blakelyi, were used to train and validate tree-senescence classification models. Key results We found that trees in the youngest stage of tree senescence, which had a very low probability of hollow occurrence, could be distinguished using multispectral aerial imagery from trees in the later stages of tree senescence, which had a high probability of hollow occurrence. Independently, the canopy-height model used to estimate crown foliage density demonstrated the potential of Lidar-derived structural parameters as predictors of senescence and the hollow-bearing status of individual trees. Conclusions This study demonstrated a ‘proof of concept’ that remotely sensed tree parameters are suitable predictor variables for the hollow-bearing status of an individual tree. Implications Distinguishing early stage senescence trees from later-stage senescence trees using remote sensing offers potential as an efficient, repeatable and cost-effective way to map the distribution and abundance of hollow-bearing trees across the landscape. Further development is required to automate this process across the landscape, particularly the delineation of tree crowns. Further improvements may be obtained using a combination of these remote-sensing techniques. This information has important applications in commercial forest inventory and in biodiversity monitoring programs.

scholarly journals Remote Sensing of Aerosols at Night with the CoSQM Sky Brightness Data

2021 ◽  
Author(s):  
Charles Marseille ◽  
Martin Aubé ◽  
Africa Barreto Velasco ◽  
Alexandre Simoneau
Keyword(s):  
Remote Sensing ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Low Cost ◽  
Empirical Relationship ◽  
Important Indicator ◽  
Sky Brightness ◽  
Night Sky ◽  
Remote Sensing Techniques ◽  
Night Sky Brightness

The aerosol optical depth is an important indicator of aerosol particle properties and associated radiative impacts. AOD determination is therefore very important to achieve relevant climate modeling. Most remote sensing techniques to retrieve aerosol optical depth are applicable to daytime given the high level of light available. The night represents half of the time but in such conditions only a few remote sensing techniques are available. Among these techniques, the most reliable are moon photometers and star photometers. In this paper, we attempt to fill gaps in the aerosol detection performed with the aforementioned techniques using night sky brightness measurements during moonless nights with the novel CoSQM: a portable, low cost and open-source multispectral photometer. In this paper, we present an innovative method for estimating the aerosol optical depth by using an empirical relationship between the zenith night sky brightness measured at night with the CoSQM and the aerosol optical depth retrieved at daytime from the AErosol Robotic NETwork. Such a method is especially suited to light-polluted regions with light pollution sources located within a few kilometers of the observation site. A coherent day-to-night aerosol optical depth and Ångström Exponent evolution in a set of 354 days and nights from August 2019 to February 2021 was verified at the location of Santa Cruz de Tenerife on the island of Tenerife, Spain. The preliminary uncertainty of this technique was evaluated using the variance under stable day-to-night conditions, set at 0.02 for aerosol optical depth and 0.75 for Ångström Exponent. These results indicate the set of CoSQM and the proposed methodology appear to be a promising tool to add new information on the aerosol optical properties at night, which could be of key importance to improve climate predictions.

Teaching Remote Sensing Techniques With High-Quality, Low-Cost Sensors [Education]

2019 ◽  
Vol 7 (2) ◽  
pp. 185-190
Author(s):  
Tobias Schuettler ◽  
Shimrit Maman ◽  
Raimund Girwidz
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
High Quality ◽  
Remote Sensing Techniques

scholarly journals Application of remote sensing techniques for the identification of biotic stress in plum trees caused by the Plum pox virus

2015 ◽  
Vol 27 (2) ◽  
pp. 99-106 ◽  
Author(s):  
Dora Krezhova ◽  
Antoniy Stoev ◽  
Nikolay Petrov ◽  
Svetla Maneva
Keyword(s):  
Remote Sensing ◽  
Biotic Stress ◽  
Near Infrared ◽  
Early Stage ◽  
Serological Test ◽  
Strong Relationship ◽  
Plum Pox Virus ◽  
Hyperspectral Reflectance ◽  
Fibre Optics ◽  
Remote Sensing Techniques

Abstract Two hyperspectral remote sensing techniques, spectral reflectance and chlorophyll fluorescence, were used for the identification of biotic stress (sharka disease) in plum trees at an early stage without visible symptoms on the leaves. The research was focused on cultivars that are widely spread in Bulgaria: ‘Angelina’, ‘Black Diamond’ and ‘Mirabelle’. Hyperspectral reflectance and fluorescence data were collected by means of a portable multichannel fibre-optics spectrometer in the visible and near infrared spectral ranges (400-1000 nm). Statistical and deterministic analyses were applied for assessing the significance of the differences between the spectral data of healthy (control) and infected plum leaves. Comparative analyses were performed with complementary serological test DAS-ELISA, broadly implemented in plant virology. The strong relationship that was found between the results from the two remote sensing techniques and serological analysis indicates the applicability of hyperspectral reflectance and fluorescence techniques for conducting health condition assessments of vegetation easily and without damage before the appearance of visible symptoms.

scholarly journals Modelling soil salinity in Oued El Abid watershed, Morocco

2018 ◽  
Vol 37 ◽  
pp. 04002 ◽  
Author(s):  
El Mouatassime Sabri ◽  
Ahmed Boukdir ◽  
Ismail Karaoui ◽  
Abdelkrim Arioua ◽  
Rachid Messlouhi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Soil Salinity ◽  
Early Stage ◽  
Landsat Imagery ◽  
Low Cost ◽  
Drainage System ◽  
High Accuracy ◽  
Anthropogenic Factors ◽  
Moderate Amount

Soil salinisation is a phenomenon considered to be a real threat to natural resources in semi-arid climates. The phenomenon is controlled by soil (texture, depth, slope etc.), anthropogenic factors (drainage system, irrigation, crops types, etc.), and climate factors. This study was conducted in the watershed of Oued El Abid in the region of Beni Mellal-Khenifra, aimed at localising saline soil using remote sensing and a regression model. The spectral indices were extracted from Landsat imagery (30 m resolution). A linear correlation of electrical conductivity, which was calculated based on soil samples (ECs), and the values extracted based on spectral bands showed a high accuracy with an R2 (Root square) of 0.80. This study proposes a new spectral salinity index using Landsat bands B1 and B4. This hydro-chemical and statistical study, based on a yearlong survey, showed a moderate amount of salinity, which threatens dam water quality. The results present an improved ability to use remote sensing and regression model integration to detect soil salinity with high accuracy and low cost, and permit intervention at an early stage of salinisation.

scholarly journals How Much Can We See from a UAV-Mounted Regular Camera? Remote Sensing-Based Estimation of Forest Attributes in South American Native Forests

2021 ◽  
Vol 13 (11) ◽  
pp. 2151
Author(s):  
Alejandro Miranda ◽  
Germán Catalán ◽  
Adison Altamirano ◽  
Carlos Zamorano-Elgueta ◽  
Manuel Cavieres ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mixed Models ◽  
Forest Canopy ◽  
Complex Structure ◽  
Boosted Regression Trees ◽  
Tree Level ◽  
Old Growth ◽  
Individual Tree ◽  
Native Forests ◽  
Old Growth Forests

Data collection from large areas of native forests poses a challenge. The present study aims at assessing the use of UAV for forest inventory on native forests in Southern Chile, and seeks to retrieve both stand and tree level attributes from forest canopy data. Data were collected from 14 plots (45 × 45 m) established at four locations representing unmanaged Chilean temperate forests: seven plots on secondary forests and seven plots on old-growth forests, including a total of 17 different native species. The imagery was captured using a fixed-wing airframe equipped with a regular RGB camera. We used the structure from motion and digital aerial photogrammetry techniques for data processing and combined machine learning methods based on boosted regression trees and mixed models. In total, 2136 trees were measured on the ground, from which 858 trees were visualized from the UAV imagery of the canopy, ranging from 26% to 88% of the measured trees in the field (mean = 45.7%, SD = 17.3), which represented between 70.6% and 96% of the total basal area of the plots (mean = 80.28%, SD = 7.7). Individual-tree diameter models based on remote sensing data were constructed with R2 = 0.85 and R2 = 0.66 based on BRT and mixed models, respectively. We found a strong relationship between canopy and ground data; however, we suggest that the best alternative was combining the use of both field-based and remotely sensed methods to achieve high accuracy estimations, particularly in complex structure forests (e.g., old-growth forests). Field inventories and UAV surveys provide accurate information at local scales and allow validation of large-scale applications of satellite imagery. Finally, in the future, increasing the accuracy of aerial surveys and monitoring is necessary to advance the development of local and regional allometric crown and DBH equations at the species level.

scholarly journals Remote Sensing for Agricultural Crops Based on a Low Cost Quadcopter

2015 ◽  
Vol 13 (34) ◽  
pp. 49-63 ◽  
Author(s):  
Liseth Viviana Campo Arcos ◽  
Juan Carlos Corrales Muñoz ◽  
Agapito Ledezma Espino
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Early Stage ◽  
Low Cost ◽  
Aerial Photographs ◽  
Second Stage ◽  
Stage Of Development ◽  
Shortest Route ◽  
Shortest Path Algorithms ◽  
Ar Drone

This paper presents a proposal for information gathering from crops by means of a low-cost quadcopter known as the AR Drone 2.0. To achieve this, we designed a system for remote sensing that addresses challenges identified in the present research, such as acquisition of aerial photographs of an entire crop and AR Drone navigation on non-planar areas arises. The project is currently at an early stage of development. The first stage describes platform and hardware/software tools used to build the proposed prototype. Second stage characterizes performance experiments of sensors stability and altitude in AR Drone, in order to design an altitude strategy control over non-flat crops. In addition, path planning algorithms based on shortest route by graphs (Dijkstra, A* and wavefront propagation) are evaluated with simulated quadcopter. The implementation of the shortest path algorithms is the beginning to full coverage of a crop. Observations of quadcopter behavior in Gazebo simulator and real tests demonstrate viability to execute the project by using AR Drone like platform of a remote sensing system to precision agriculture.

scholarly journals Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 694 ◽  
Author(s):  
Selina Ganz ◽  
Yannek Käber ◽  
Petra Adler
Keyword(s):  
Remote Sensing ◽  
Tree Height ◽  
Field Measurements ◽  
Test Site ◽  
Aerial Images ◽  
Lidar Data ◽  
Individual Tree ◽  
Indirect Measurements ◽  
Remote Sensing Techniques ◽  
Remote Sensing Methods

We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

scholarly journals A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ben G Weinstein ◽  
Sergio Marconi ◽  
Stephanie A Bohlman ◽  
Alina Zare ◽  
Aditya Singh ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Spatial Location ◽  
The United States ◽  
Sensor Data ◽  
Data Set ◽  
Individual Tree ◽  
Individual Level ◽  
Remote Sensing Techniques ◽  
Individual Trees

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network’s Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.

scholarly journals Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

2021 ◽  
pp. 030913332110236
Author(s):  
Liam S Taylor ◽  
Duncan J Quincey ◽  
Mark W Smith ◽  
Celia A Baumhoer ◽  
Malcolm McMillan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Aerial Imagery ◽  
Commercial Sector ◽  
Mountain Glacier ◽  
Mountain Glaciers ◽  
Lake Evolution ◽  
Exponential Increase ◽  
Commercial Applications ◽  
Warming World

Remote sensing technologies are integral to monitoring the mountain cryosphere in a warming world. Satellite missions and field-based platforms have transformed understanding of the processes driving changes in mountain glacier dynamics, snow cover, lake evolution, and the associated emergence of hazards (e.g. avalanches, floods, landslides). Sensors and platforms are becoming more bespoke, with innovation being driven by the commercial sector, and image repositories are more frequently open access, leading to the democratisation of data analysis and interpretation. Cloud computing, artificial intelligence, and machine learning are rapidly transforming our ability to handle this exponential increase in data. This review therefore provides a timely opportunity to synthesise current capabilities in remote sensing of the mountain cryosphere. Scientific and commercial applications were critically examined, recognising the technologies that have most advanced the discipline. Low-cost sensors can also be deployed in the field, using microprocessors and telecommunications equipment to connect mountain glaciers to stakeholders for real-time monitoring. The potential for novel automated pipelines that can process vast volumes of data is also discussed, from reimagining historical aerial imagery to produce elevation models, to automatically delineating glacier boundaries. Finally, the applications of these emerging techniques that will benefit scientific research avenues and real-world societal programmes are discussed.

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