Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections.

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EchidnaCSI – improving monitoring of a cryptic species at continental scale using Citizen Science

Global Ecology and Conservation ◽

10.1016/j.gecco.2021.e01626 ◽

2021 ◽

pp. e01626

Author(s):

Alan Stenhouse ◽

Tahlia Perry ◽

Frank Grützner ◽

Megan Lewis ◽

Lian Pin Koh

Keyword(s):

Cryptic Species ◽

Citizen Science ◽

Continental Scale

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Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography

Biogeosciences ◽

10.5194/bg-13-5085-2016 ◽

2016 ◽

Vol 13 (17) ◽

pp. 5085-5102 ◽

Cited By ~ 39

Author(s):

Caitlin E. Moore ◽

Tim Brown ◽

Trevor F. Keenan ◽

Remko A. Duursma ◽

Albert I. J. M. van Dijk ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Tropical Rainforests ◽

Vegetation Phenology ◽

Repeat Photography ◽

Tropical Savannas ◽

Diverse Range ◽

Canopy Development ◽

Continental Scale ◽

Digital Repeat Photography

Abstract. Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

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A Review of Unmanned Aerial Vehicles, Citizen Science, and Interferometry Remote Sensing in Landslide Hazards

Remote Sensing of Hydrometeorological Hazards ◽

10.1201/9781315154947-23 ◽

2017 ◽

pp. 469-491 ◽

Cited By ~ 1

Author(s):

Panagiotis Partsinevelos ◽

Zacharias Agioutantis ◽

Achilleas Tripolitsiotis ◽

Nathaniel Schaefer

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicles ◽

Citizen Science ◽

Aerial Vehicles ◽

Landslide Hazards

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Satellite remote sensing and natural disturbances

Satellite Remote Sensing and the Management of Natural Resources ◽

10.1093/oso/9780198717263.003.0003 ◽

2019 ◽

pp. 37-50

Author(s):

Nathalie Pettorelli

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Accurate Information ◽

Ecological Knowledge ◽

Natural Disturbances ◽

Ecosystem Impacts ◽

Spatial And Temporal Scales ◽

Continental Scale ◽

Management Actions ◽

Wild Fire

This chapter provides an overview of how satellite remote sensing can help map the occurrence, and risk of occurrence, of several environmental disturbances; assess the extent of the associated damages; and monitor the recovery of the areas impacted by these disturbances. It particularly focuses on floods, wild fires, droughts, frost, extreme winter warming events, infestations and blooms, and bleaching events, as these are all well-known natural disturbances likely to change in frequency of occurrence and intensity over the coming decades. Through the use of examples, this chapter demonstrates how the utility of satellite remote sensing resides in the ability it provides to separate and characterise (i.e. through form, intensity, and trajectory) disturbances and responses at various spatial and temporal scales, thereby facilitating ecological knowledge expansion and the identification of relevant management actions. In particular, this contribution shows how satellites offer multiple opportunities to gain accurate information on the location, spatial extent, and duration of disturbances at the continental scale, which is needed to evaluate the ecosystem impacts of land cover changes due to, for example, wild fire, insect epidemics, and flooding, thereby reducing uncertainties in our ability to model global carbon budgets.

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The Effect of Topographic Correction on Forest Tree Species Classification Accuracy

Remote Sensing ◽

10.3390/rs12050787 ◽

2020 ◽

Vol 12 (5) ◽

pp. 787

Author(s):

Chao Dong ◽

Gengxing Zhao ◽

Yan Meng ◽

Baihong Li ◽

Bo Peng

Keyword(s):

Remote Sensing ◽

Tree Species ◽

Forest Tree ◽

Species Classification ◽

Illumination Condition ◽

Topographic Correction ◽

Forest Tree Species ◽

Tree Species Classification ◽

Inconsistency Rate ◽

Rotation Model

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.

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