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1. Walking the Line: Conducting Transect Walks in Burkina Faso

   https://doi.org/10.17730/0888-4552.43.1.18  

   Rojas, Alfredo; Nomedji, Koffi; West, Colin Thor (January 2021, Practicing Anthropology)

   Abstract In this article we present results from transect walks and participatory mapping done in Burkina Faso. Since the Sahelian drought of the 1970s, researchers have continued to depict the Sahelian region of West Africa as an environment experiencing severe degradation; a narrative that persists over time. Recently, however, analyses of satellite imagery have identified remarkable patterns of greening across the Sahel. The causes of this greening are hotly debated. Through this project we aim to inform these debates with on-the-ground perceptions of local farmers and pastoralists. The transect walk method is a community-based process that collects information on the land-use/land-cover (LULC) features across villages. Transects help triangulate data by combining high-resolution satellite imagery, firsthand observations, and local experiences of ecological processes. We describe the methodology behind transects and discuss how they contextualize an otherwise removed process of environmental analysis. We also describe the challenges that arise throughout the fieldwork process. 

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2. Arctic greening associated with lengthening growing seasons in Northern Alaska

   https://doi.org/10.1088/1748-9326/ab5e26  

   Arndt, Kyle A.; Santos, Maria J.; Ustin, Susan; Davidson, Scott J.; Stow, Doug; Oechel, Walter C.; Tran, Thao T. P.; Graybill, Brian; Zona, Donatella (December 2019, Environmental Research Letters)

   Abstract Many studies have reported that the Arctic is greening; however, we lack an understanding of the detailed patterns and processes that are leading to this observed greening. The normalized difference vegetation index (NDVI) is used to quantify greening, which has had largely positive trends over the last few decades using low spatial resolution satellite imagery such as AVHRR or MODIS over the pan-Arctic region. However, substantial fine scale spatial heterogeneity in the Arctic makes this large-scale investigation hard to interpret in terms of vegetation and other environmental changes. Here we focus on one area of the northern Alaskan Arctic using high spatial resolution (4 m) multispectral satellite imagery from DigitalGlobe^™to analyze the greening trend near Utqiaġvik (formerly known as Barrow) over 14 years from 2002 to 2016. We found that tundra vegetation has been greening (τ = 0.65,p = 0.01, NDVI increase of 0.01 yr⁻¹) despite no overall change in vegetation community composition. The greening is most closely correlated to the number of thawing degree days (R² = 0.77,F = 21.5,p < 0.001) which increased in a similar linear trend over the 14 year study period (1.79 ± 0.50 days per year,p < 0.01,τ = −0.56). This suggests that in this Arctic ecosystem, greening is occurring due to a lengthening growing season that appears to stimulate plant productivity without any significant change in vegetation communities. We found that vegetation communities in wetter locations greened about twice as fast as those found in drier conditions supporting the hypothesis that these communities respond more strongly to warming. We suggest that in Arctic environments, vegetation productivity may continue to rise, particularly in wet areas. 

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3. Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

   https://doi.org/10.3390/drones3030060  

   Yang, Bo; Hawthorne, Timothy L.; Torres, Hannah; Feinman, Michael (September 2019, Drones)

   High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data. 

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4. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles

   https://doi.org/10.1139/as-2021-0044  

   Orndahl, Kathleen M.; Ehlers, Libby P.; Herriges, Jim D.; Pernick, Rachel E.; Hebblewhite, Mark; Goetz, Scott J. (April 2022, Arctic Science)

   Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m^-2, and per-PFT RMSE ranged from 17.14-164.3 g m^-2. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery. 

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5. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles, 2018-2019

   https://doi.org/10.18739/A2R785Q5B  

   Orndahl, Kathleen (January 2022, Arctic Data Center)

   Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data. 

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