Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems

Surový, Peter; Ribeiro, Nuno de Almeida; Panagiotidis, Dimitrios

doi:10.1080/01431161.2018.1434329

Cited by 74 publications

(55 citation statements)

References 36 publications

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“…Monitoring mangrove forests is also arduous because of their large extent [9], thus remote sensing data has been widely used for this purpose. The kind of remote sensing platform used depends on the scale and the goal of the research [10]. Low-and medium-resolution space-borne sensors availability is generally free of charges or cheaper than airborne sensors and they offer larger coverage area while airborne and unmanned aerial vehicles (UAV) sensors have much more spatial resolution but limited autonomy, which can result in a higher cost per hectare.…”

Section: Introductionmentioning

confidence: 99%

Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

Algeet²,

et al. 2019

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Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations.

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Section: Introductionmentioning

confidence: 99%

Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

Algeet²,

et al. 2019

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“…Previous studies also reported that large and small crown density tends to be underestimated when optical images are used to delineate individual tree crowns [26]. Besides, Surový et al [27] reported that cork oak crowns are more difficult to detect (higher omission errors), when compared to other types of trees with more regular crowns such as pine, due to their sparse canopy and lower density of leaves, especially in small trees.…”

Section: Resultsmentioning

confidence: 99%

Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

2019

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In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

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“…Field mapping has many uses from small farm improvement to large-scale governmental land surveys [4]. A few prominent studies involving field mapping in agriculture include: NDVI maps [7,[17][18][19], assessing spatial variability and structure of vineyards [20], surveying and development of land cover maps [4,15], and estimation of tree locations and sizes [21,22].…”

Section: Field Mappingmentioning

confidence: 99%

“…These maps are often constructed using a series of independent images taken from an UAS and stitched together using various photogrammetric software packages [15,23]. Each individual image in the resulting mosaic are commonly georeferenced using ground control points (GCPs), overlaying Landsat satellite data, or a combination of the two [4,7,15,[17][18][19][20][21][22]. Images will also undergo a process of data correction which can involve vignetting and orthorectification [18], among others, depending on the task.…”

Section: Field Mappingmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems

Cited by 74 publications

References 36 publications

Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

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