UAVs for Vegetation Monitoring

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

Download Full-text

Monitoring Vegetation Dynamics at a Tidal Marsh Restoration Site: Integrating Field Methods, Remote Sensing and Modeling

Estuaries and Coasts ◽

10.1007/s12237-021-00977-4 ◽

2021 ◽

Author(s):

Alexandra S. Thomsen ◽

Johannes Krause ◽

Monica Appiano ◽

Karen E. Tanner ◽

Charlie Endris ◽

...

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Soil Analysis ◽

High Elevation ◽

Vegetation Monitoring ◽

Field Methods ◽

Bare Area ◽

Marsh Restoration ◽

Restoration Success ◽

Restoration Strategies

AbstractSea level rise threatens coastal wetlands worldwide, and restoration projects are implementing strategies that decrease vulnerability to this threat. Vegetation monitoring at sites employing new restoration strategies and determination of appropriate monitoring techniques improve understanding of factors leading to restoration success. In Central California, soil addition raised a degraded marsh plain to a high elevation expected to be resilient to sea level rise over the next century. We monitored plant survival and recruitment using area searches, transect surveys, and unoccupied aircraft systems (UAS) imagery. We used random forest modeling to examine the influence of nine environmental variables on vegetation colonization and conducted targeted soil sampling to examine additional factors contributing to vegetation patterns. Limited pre-construction vegetation survived soil addition, likely due to the sediment thickness (mean = 69 cm) and placement method. After 1 year, about 10% of the initially bare area saw vegetation reestablishment. Elevation and inundation frequency were particularly critical to understanding restoration success, with greatest vegetation cover in high-elevation areas tidally inundated < 0.85% of the time. Soil analysis suggested greater salinity stress and ammonium levels in poorly-vegetated compared to well-vegetated areas at the same elevation. We found that both transect and UAS methods were suitable for monitoring vegetation colonization. Field transects may provide the best approach for tracking early vegetation colonization at moderate-sized sites under resource limitations, but UAS provide a complementary landscape perspective. Beyond elucidating patterns and drivers of marsh dynamics at a newly restored site, our investigation informs monitoring of marsh restoration projects globally.

Download Full-text

QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽

10.3390/agronomy11050952 ◽

2021 ◽

Vol 11 (5) ◽

pp. 952

Author(s):

Lia Duarte ◽

Ana Cláudia Teodoro ◽

Joaquim J. Sousa ◽

Luís Pádua

Keyword(s):

Open Source ◽

Precision Agriculture ◽

Vegetation Indices ◽

Data Interpretation ◽

Vegetation Monitoring ◽

Distribution Maps ◽

Monitoring Process ◽

Multi Temporal ◽

The Creation ◽

The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

Download Full-text

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽

10.3390/s21010320 ◽

2021 ◽

Vol 21 (1) ◽

pp. 320

Author(s):

Emilio Guirado ◽

Javier Blanco-Sacristán ◽

Emilio Rodríguez-Caballero ◽

Siham Tabik ◽

Domingo Alcaraz-Segura ◽

...

Keyword(s):

High Resolution ◽

Optical Sensors ◽

Biotic Interactions ◽

Relevant Information ◽

Vegetation Monitoring ◽

Object Based Image Analysis ◽

Object Based ◽

Segmentation Methods ◽

High Resolution Images ◽

Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

Download Full-text