Thirty Year Analysis of Forest and Scrub Canopy Cover on the Big Sur Coast of California using Landsat Imagery

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

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Percentage canopy cover – using Landsat imagery to delineate habitat for Myanmar's endangered Eld's deer (Cervus eldi)

Animal Conservation ◽

10.1017/s1367943005002209 ◽

2005 ◽

Vol 8 (3) ◽

pp. 289-296 ◽

Cited By ~ 26

Author(s):

Kevin Koy ◽

William J. McShea ◽

Peter Leimgruber ◽

Barry N. Haack ◽

Myint Aung

Keyword(s):

Landsat Imagery ◽

Canopy Cover

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Intensified Management of Coffee Forest in Southwest Ethiopia Detected by Landsat Imagery

Forests ◽

10.3390/f11040422 ◽

2020 ◽

Vol 11 (4) ◽

pp. 422 ◽

Cited By ~ 2

Author(s):

Byongjun Hwang ◽

Kitessa Hundera ◽

Bizuneh Mekuria ◽

Adrian Wood ◽

Andinet Asfaw

Keyword(s):

Survey Methods ◽

Landsat Imagery ◽

Canopy Cover ◽

Temporal Analysis ◽

Intensive Management ◽

Economic Policies ◽

State Control ◽

Coffee Production ◽

Southwest Ethiopia ◽

Afromontane Forests

The high forests in southwest Ethiopia, some of the last remaining Afromontane forests in the country, are home to significant forest coffee production. While considered as beneficial in maintaining forests, there have been growing concerns about the degradation caused by intensive management for coffee production in these forests. However, no suitable methods have been developed to map the coffee forests. In this study, we developed a tie-point approach to consistently estimate the degree of degradation caused by intensive management by combining use of Landsat imagery with in-situ canopy cover and tree survey data. Our results demonstrate a clear distinction between undisturbed natural forest and heavily managed coffee forest due to changes in forest structure and canopy cover caused by intensive management in the coffee forest. Temporal analysis of 32 years of Landsat imagery reveals a progressive and significant transition in the level of degradation in the coffee forest over this period. This is the first time to our knowledge, that this progressive intensification of coffee forest has been measured. There is a major intensification in the mid-1990s, which follows the introduction of new liberal economic policies by the Federal government established in 1991, rising coffee prices, and changes in state control over access to the forest. The question remains as to how these 20 years of intensive management in coffee forest have affected forest biodiversity and, more importantly, how canopy trees in this forest can be regenerated in the future. This study provides potential satellite-based mapping and ground-based photography and tree survey methods to help investigate the impacts of intensive management within coffee forest on biodiversity and regeneration.

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Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning

Remote Sensing ◽

10.3390/rs11121503 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1503 ◽

Cited By ~ 12

Author(s):

Lana L. Narine ◽

Sorin C. Popescu ◽

Lonesome Malambo

Keyword(s):

Neural Network ◽

Deep Learning ◽

Land Cover ◽

Aboveground Biomass ◽

Network Architecture ◽

Prediction Models ◽

Landsat Imagery ◽

Canopy Cover ◽

Land Cover Maps ◽

Forest Aboveground Biomass

Spatially continuous estimates of forest aboveground biomass (AGB) are essential to supporting the sustainable management of forest ecosystems and providing invaluable information for quantifying and monitoring terrestrial carbon stocks. The launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) on September 15th, 2018 offers an unparalleled opportunity to assess AGB at large scales using along-track samples that will be provided during its three-year mission. The main goal of this study was to investigate deep learning (DL) neural networks for mapping AGB with ICESat-2, using simulated photon-counting lidar (PCL)-estimated AGB for daytime, nighttime, and no noise scenarios, Landsat imagery, canopy cover, and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using a simulated PCL-estimated AGB along two years of planned ICESat-2 profiles. The primary tasks were to investigate and determine neural network architecture, examine the hyper-parameter settings, and subsequently generate wall-to-wall AGB maps. A first set of models were developed using vegetation indices calculated from single-date Landsat imagery, canopy cover, and land cover, and a second set of models were generated using metrics from one year of Landsat imagery with canopy cover and land cover maps. To compare the effectiveness of final models, comparisons with Random Forests (RF) models were made. The deep neural network (DNN) models achieved R2 values of 0.42, 0.49, and 0.50 for the daytime, nighttime, and no noise scenarios respectively. With the extended dataset containing metrics calculated from Landsat images acquired on different dates, substantial improvements in model performance for all data scenarios were noted. The R2 values increased to 0.64, 0.66, and 0.67 for the daytime, nighttime, and no noise scenarios. Comparisons with Random forest (RF) prediction models highlighted similar results, with the same R2 and root mean square error (RMSE) range (15–16 Mg/ha) for daytime and nighttime scenarios. Findings suggest that there is potential for mapping AGB using a combinatory approach with ICESat-2 and Landsat-derived products with DL.

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Spatial assemblage structure of shallow-water reef fish in Southwest Australia

Marine Ecology Progress Series ◽

10.3354/meps13445 ◽

2020 ◽

Vol 649 ◽

pp. 125-140

Author(s):

DS Goldsworthy ◽

BJ Saunders ◽

JRC Parker ◽

ES Harvey

Keyword(s):

Shallow Water ◽

Reef Fish ◽

Marine Environment ◽

Fish Assemblages ◽

Canopy Cover ◽

Reef Fishes ◽

Reef Fish Assemblage ◽

Reef Type ◽

Present Distribution ◽

Southwest Australia

Bioregional categorisation of the Australian marine environment is essential to conserve and manage entire ecosystems, including the biota and associated habitats. It is important that these regions are optimally positioned to effectively plan for the protection of distinct assemblages. Recent climatic variation and changes to the marine environment in Southwest Australia (SWA) have resulted in shifts in species ranges and changes to the composition of marine assemblages. The goal of this study was to determine if the current bioregionalisation of SWA accurately represents the present distribution of shallow-water reef fishes across 2000 km of its subtropical and temperate coastline. Data was collected in 2015 using diver-operated underwater stereo-video surveys from 7 regions between Port Gregory (north of Geraldton) to the east of Esperance. This study indicated that (1) the shallow-water reef fish of SWA formed 4 distinct assemblages along the coast: one Midwestern, one Central and 2 Southern Assemblages; (2) differences between these fish assemblages were primarily driven by sea surface temperature, Ecklonia radiata cover, non-E. radiata (canopy) cover, understorey algae cover, reef type and reef height; and (3) each of the 4 assemblages were characterised by a high number of short-range Australian and Western Australian endemic species. The findings from this study suggest that 4, rather than the existing 3 bioregions would more effectively capture the shallow-water reef fish assemblage patterns, with boundaries having shifted southwards likely associated with ocean warming.

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