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Remote Sensing of Savannas and Woodlands

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 72696

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Special Issue Editor

Dr. Michael J. Hill

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Guest Editor

1. Professor Emeritus, Department of Earth System Science and Policy, University of North Dakota, Grand Forks, ND 58202, USA
2. Current address: Visiting Scientist, CSIRO Land and Water, Synergy Building, Black Mountain, Canberra, ACT 2601, Australia
Interests: savannas and grasslands; ecosystem function; remote sensing; ecological modelling; land surface and vegetation dynamics

Special Issue Information

Dear Colleagues,

Savannas and woodlands are one of the most challenging targets for remote sensing. However, they are receiving increased attention, especially in Africa and South America since they are prime candidates for agricultural conversion, important resources for livestock production and subsistence of indigenous communities, and could play a significant role in signalling vegetation shifts driven by the interaction of climate change and rising atmospheric CO₂ concentrations. The sub-Saharan region of Africa played a key role in exemplifying the value of early low resolution polar orbiting satellites and introducing the public to large scale regional dynamics of climate driven vegetation growth via NDVI time series. Fast forwarding to 2018, and a revolution in remote sensing is well underway with both public and private initiatives rapidly addressing the historical trade-offs associated with spatial resolution, temporal frequency and spectral resolution that includes the active LiDAR and Radar domains. These trade-offs are particularly important in savanna and woodland systems where over-story, mid-story and under-story vegetation strata have equal importance in structure, function and dynamics. This Special Issue seeks to provide an overview of the application of the latest sensors and sensor combinations to retrieval of quantitative properties of savannas and woodlands. The contributions should aim to illustrate improvements in retrievals of attributes of cover components and component dynamics and relate these attributes to the wide diversity of issues faced by savanna and woodland systems globally. Since the savanna/woodland complex is subject to many and varied definitions, contributions would be welcome for any ecologically defined vegetation type containing woody plants and a significant grassy understory – for example the continuum from the pinyon juniper systems of the Western USA to the Miombo woodland of Central Africa, and including systems where a patchwork of woody plants and grassland is determined by edaphic factors (e.g., Colombian llanos or Congolian savanna). The understory/grassland component of savannas and woodlands has received much less attention than the woody component and contributions that address this are encouraged. The Special Issue seeks to provide a good coverage of: Different sensors (passive and active, optical, thermal, LiDAR, Radar, high resolution small-Sats, such as the Planet array or the Worldview suite, airborne systems, and combinations thereof); integration of spatial, temporal and spectral resolution across scales to address issues; and a wide range of applications ranging from retrieval of vegetation properties and detection of disturbance and vegetation shifts to understanding of ecosystem processes and land-atmosphere interactions. Potential topics for research and review articles on applications of remote sensing in savannas and woodlands include:

Retrieval of properties and attributes for individual strata
Detection of disturbance and trends in composition and cover
Identification and mapping of invasive species and woody encroachment
Specific measurement of understory properties including litter and bare soil
Landscape scale process affecting vegetation persistence, composition and water/nutrient dynamics
Spatial interrelations and processes among plant functional types and among vegetation patches
Interactions between vegetation dynamics, fire, climate and herbivory at local and regional scale
Biochemistry of foliage and detection of species phenomics and traits
Detection of clearing, harvesting and resource use
Biomass estimation including woody and non-woody components
Assessment of fuel quantity and properties
Assessment of wildlife habitat and biodiversity status
Applications to ecosystem service assessment in multi-use savanna/woodland

Dr. Michael J. Hill
Guest Editor

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Published Papers (13 papers)

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Editorial

Jump to: Research, Review

6 pages, 746 KiB

Open AccessEditorial

Remote Sensing of Savannas and Woodlands: Editorial

by Michael J. Hill

Remote Sens. 2021, 13(8), 1490; https://doi.org/10.3390/rs13081490 - 13 Apr 2021

Cited by 3 | Viewed by 2003

Abstract

Savannas and woodlands represent one of the most challenging targets for remote sensing [...] Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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Research

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19 pages, 39618 KiB

Open AccessFeature PaperArticle

Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

by Shaun R. Levick, Tim Whiteside, David A. Loewensteiner, Mitchel Rudge and Renee Bartolo

Remote Sens. 2021, 13(2), 257; https://doi.org/10.3390/rs13020257 - 13 Jan 2021

Cited by 29 | Viewed by 4757

Abstract

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R

^{2}

= 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha

^{- 1}

vs 41.08 Mg ha

^{- 1}

). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R

^{2}

= 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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18 pages, 3968 KiB

Open AccessArticle

Environmental Drivers of Water Use for Caatinga Woody Plant Species: Combining Remote Sensing Phenology and Sap Flow Measurements

by Rennan A. Paloschi, Desirée Marques Ramos, Dione J. Ventura, Rodolfo Souza, Eduardo Souza, Leonor Patrícia Cerdeira Morellato, Rodolfo L. B. Nóbrega, Ítalo Antônio Cotta Coutinho, Anne Verhoef, Thales Sehn Körting and Laura De Simone Borma

Remote Sens. 2021, 13(1), 75; https://doi.org/10.3390/rs13010075 - 28 Dec 2020

Cited by 18 | Viewed by 4001

Abstract

We investigated the water use of Caatinga vegetation, the largest seasonally dry forest in South America. We identified and analysed the environmental phenological drivers in woody species and their relationship with transpiration. To monitor the phenological evolution, we used remote sensing indices at different spatial and temporal scales: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and green chromatic coordinate (GCC). To represent the phenology, we used the GCC extracted from in-situ automated digital camera images; indices calculated based on sensors included NDVI, SAVI and GCC from Sentinel-2A and B satellites images, and NDVI products MYD13Q1 and MOD13Q1 from a moderate-resolution imaging spectroradiometer (MODIS). Environmental drivers included continuously monitored rainfall, air temperature, soil moisture, net radiation, and vapour pressure deficit. To monitor soil water status and vegetation water use, we installed soil moisture sensors along three soil profiles and sap flow sensors for five plant species. Our study demonstrated that the near-surface GCC data played an important role in permitting individual monitoring of species, whereas the species’ sap flow data correlated better with NDVI, SAVI, and GCC than with species’ near-surface GCC. The wood density appeared to affect the transpiration cessation times in the dry season, given that species with the lowest wood density reach negligible values of transpiration earlier in the season than those with high woody density. Our results show that soil water availability was the main limiting factor for transpiration during more than 80% of the year, and that both the phenological response and water use are directly related to water availability when relative saturation of the soil profile fell below 0.25. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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16 pages, 10379 KiB

Open AccessArticle

Exploring the Variability of Tropical Savanna Tree Structural Allometry with Terrestrial Laser Scanning

by Linda Luck, Lindsay B. Hutley, Kim Calders and Shaun R. Levick

Remote Sens. 2020, 12(23), 3893; https://doi.org/10.3390/rs12233893 - 27 Nov 2020

Cited by 17 | Viewed by 3779

Abstract

Individual tree carbon stock estimates typically rely on allometric scaling relationships established between field-measured stem diameter (DBH) and destructively harvested biomass. The use of DBH-based allometric equations to estimate the carbon stored over larger areas therefore, assumes that tree architecture, including branching and crown structures, are consistent for a given DBH, and that minor variations cancel out at the plot scale. We aimed to explore the degree of structural variation present at the individual tree level across a range of size-classes. We used terrestrial laser scanning (TLS) to measure the 3D structure of each tree in a 1 ha savanna plot, with coincident field-inventory. We found that stem reconstructions from TLS captured both the spatial distribution pattern and the DBH of individual trees with high confidence when compared with manual measurements (R

^{2}

= 0.98, RMSE = 0.0102 m). Our exploration of the relationship between DBH, crown size and tree height revealed significant variability in savanna tree crown structure (measured as crown area). These findings question the reliability of DBH-based allometric equations for adequately representing diversity in tree architecture, and therefore carbon storage, in tropical savannas. However, adoption of TLS outside environmental research has been slow due to considerable capital cost and monitoring programs often continue to rely on sub-plot monitoring and traditional allometric equations. A central aspect of our study explores the utility of a lower-cost TLS system not generally used for vegetation surveys. We discuss the potential benefits of alternative TLS-based approaches, such as explicit modelling of tree structure or voxel-based analyses, to capture the diverse 3D structures of savanna trees. Our research highlights structural heterogeneity as a source of uncertainty in savanna tree carbon estimates and demonstrates the potential for greater inclusion of cost-effective TLS technology in national monitoring programs. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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23 pages, 7710 KiB

Open AccessArticle

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

by Polyanna da Conceição Bispo, Pedro Rodríguez-Veiga, Barbara Zimbres, Sabrina do Couto de Miranda, Cassio Henrique Giusti Cezare, Sam Fleming, Francesca Baldacchino, Valentin Louis, Dominik Rains, Mariano Garcia, Fernando Del Bon Espírito-Santo, Iris Roitman, Ana María Pacheco-Pascagaza, Yaqing Gou, John Roberts, Kirsten Barrett, Laerte Guimaraes Ferreira, Julia Zanin Shimbo, Ane Alencar, Mercedes Bustamante, Iain Hector Woodhouse, Edson Eyji Sano, Jean Pierre Ometto, Kevin Tansey and Heiko Balzter Show full author list Hide full author list

Remote Sens. 2020, 12(17), 2685; https://doi.org/10.3390/rs12172685 - 19 Aug 2020

Cited by 34 | Viewed by 8203

Abstract

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R² = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha⁻¹ and a bias of 0.43 Mg ha⁻¹. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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23 pages, 9520 KiB

Open AccessEditor’s ChoiceArticle

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

by Ane Alencar, Julia Z. Shimbo, Felipe Lenti, Camila Balzani Marques, Bárbara Zimbres, Marcos Rosa, Vera Arruda, Isabel Castro, João Paulo Fernandes Márcico Ribeiro, Victória Varela, Isa Alencar, Valderli Piontekowski, Vivian Ribeiro, Mercedes M. C. Bustamante, Edson Eyji Sano and Mario Barroso

Remote Sens. 2020, 12(6), 924; https://doi.org/10.3390/rs12060924 - 13 Mar 2020

Cited by 125 | Viewed by 12674

Abstract

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr⁻¹), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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23 pages, 5534 KiB

Open AccessArticle

Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography

by Pedro J. Gómez-Giráldez, María J. Pérez-Palazón, María J. Polo and María P. González-Dugo

Remote Sens. 2020, 12(4), 600; https://doi.org/10.3390/rs12040600 - 11 Feb 2020

Cited by 21 | Viewed by 4600

Abstract

Annual grasslands are an essential component of oak savanna ecosystems as the primary source of fodder for livestock and wildlife. Drought resistance adaptation has led them to complete their life cycle before serious soil and plant water deficits develop, resulting in a close link between grass phenology and soil water dynamics. In this work, these links were explored using a combination of terrestrial photography, satellite imagery and hydrological ground measurements. We obtained key phenological parameters of the grass cycle from terrestrial camera data using the Green Chromatic Coordinate (GCCc) index. These parameters were compared with those provided by time-series of vegetation indices (VI) obtained from Sentinel-2 (S2) satellites and time-series of abiotic variables, which defined the hydrology of the system. The results showed that the phenological parameters estimated by the S2 Normalized Difference Vegetation Index (NDVI) (r = 0.83, p < 0.001) and soil moisture (SM) (r = 0.75, p < 0.001) presented the best agreement with ground-derived observations compared to those provided by other vegetation indices and abiotic variables. The study of NDVI and SM dynamics, that was extended over four growing seasons (July 2015–May 2019), showed that the seasonality of both variables was highly synchronized, with the best agreements at the beginning and at the end of the dry seasons. However, stage changes were estimated first by SM, followed by NDVI, with a delay of between 3 and 10 days. These results support the use of a multi-approach method to monitor the phenology and the influence of the soil moisture dynamic under the study conditions. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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23 pages, 9249 KiB

Open AccessArticle

A Healthy Park Needs Healthy Vegetation: The Story of Gorongosa National Park in the 21st Century

by Hannah Herrero, Peter Waylen, Jane Southworth, Reza Khatami, Di Yang and Brian Child

Remote Sens. 2020, 12(3), 476; https://doi.org/10.3390/rs12030476 - 03 Feb 2020

Cited by 13 | Viewed by 6018

Abstract

Understanding trends or changes in biomass and biodiversity around conservation areas in Africa is important and has economic and societal impacts on the surrounding communities. Gorongosa National Park, Mozambique was established under unique conditions due to its complex history. In this study, we used a time-series of Normalized Difference Vegetation Index (NDVI) to explore seasonal trends in biomass between 2000 and 2016. In addition, vegetation directional persistence was created. This product is derived from the seasonal NDVI time series-based analysis and represents the accumulation of directional change in NDVI relative to a fixed benchmark (2000–2004). Trends in precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was explored from 2000–2016. Different vegetation covers are also considered across various landscapes, including a comparison between the Lower Gorongosa (savanna), Mount Gorongosa (rainforest), and surrounding buffer zones. Important findings include a decline in precipitation over the time of study, which most likely drives the observed decrease in NDVI. In terms of vegetation persistence, Lower Gorongosa had stronger positive trends than the buffer zone, and Mount Gorongosa had higher negative persistence overall. Directional persistence also varied by vegetation type. These are valuable findings for park managers and conservationists across the world. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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30 pages, 7645 KiB

Open AccessArticle

The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

by Michael J. Hill and Juan P. Guerschman

Remote Sens. 2020, 12(3), 406; https://doi.org/10.3390/rs12030406 - 28 Jan 2020

Cited by 30 | Viewed by 5225

Abstract

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (F_PV), non-photosynthetic vegetation (F_NPV), and bare soil (F_BS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in F_NPV and positive trends F_BS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of F_NPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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22 pages, 10287 KiB

Open AccessFeature PaperArticle

Functional Phenology of a Texas Post Oak Savanna from a CHRIS PROBA Time Series

by Michael J. Hill, Andrew Millington, Rebecca Lemons and Cherie New

Remote Sens. 2019, 11(20), 2388; https://doi.org/10.3390/rs11202388 - 15 Oct 2019

Cited by 5 | Viewed by 3008

Abstract

Remnant midwestern oak savannas in the USA have been altered by fire suppression and the encroachment of woody evergreen trees and shrubs. The Gus Engeling Wildlife Management Area (GEWMA) near Palestine, Texas represents a relatively intact southern example of thickening and evergreen encroachment in oak savannas. In this study, 18 images from the CHRIS/PROBA (Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy) sensor were acquired between June 2009 and October 2010 and used to explore variation in canopy dynamics among deciduous and evergreen trees and shrubs, and savanna grassland in seasonal leaf-on and leaf-off conditions. Nadir CHRIS images from the 11 useable dates were processed to surface reflectance and a selection of vegetation indices (VIs) sensitive to pigments, photosynthetic efficiency, and canopy water content were calculated. An analysis of temporal VI phenology was undertaken using a fishnet polygon at 90 m resolution incorporating tree densities from a classified aerial photo and soil type polygons. The results showed that the major differences in spectral phenology were associated with deciduous tree density, the density of evergreen trees and shrubs—especially during deciduous leaf-off periods—broad vegetation types, and soil type interactions with elevation. The VIs were sensitive to high densities of evergreens during the leaf-off period and indicative of a photosynthetic advantage over deciduous trees. The largest differences in VI profiles were associated with high and low tree density, and soil types with the lowest and highest available soil water. The study showed how time series of hyperspectral data could be used to monitor the relative abundance and vigor of desirable and less desirable species in conservation lands. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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25 pages, 10596 KiB

Open AccessArticle

Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso

by Flávia de Souza Mendes, Daniel Baron, Gerhard Gerold, Veraldo Liesenberg and Stefan Erasmi

Remote Sens. 2019, 11(10), 1161; https://doi.org/10.3390/rs11101161 - 15 May 2019

Cited by 27 | Viewed by 6877

Abstract

Mapping vegetation types through remote sensing images has proved to be effective, especially in large biomes, such as the Brazilian Cerrado, which plays an important role in the context of management and conservation at the agricultural frontier of the Amazon. We tested several combinations of optical and radar images to identify the four dominant vegetation types that are prevalent in the Cerrado area (i.e., cerrado denso, cerradão, gallery forest, and secondary forest). We extracted features from both sources of data such as intensity, grey level co-occurrence matrix, coherence, and polarimetric decompositions using Sentinel 2A, Sentinel 1A, ALOS-PALSAR 2 dual/full polarimetric, and TanDEM-X images during the dry and rainy season of 2017. In order to normalize the analysis of these features, we used principal component analysis and subsequently applied the Random Forest algorithm to evaluate the classification of vegetation types. During the dry season, the overall accuracy ranged from 48 to 83%, and during the dry and rainy seasons it ranged from 41 up to 82%. The classification using Sentinel 2A images during the dry season resulted in the highest overall accuracy and kappa values, followed by the classification that used images from all sensors during the dry and rainy season. Optical images during the dry season were sufficient to map the different types of vegetation in our study area. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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14 pages, 2397 KiB

Open AccessArticle

Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data

by Sanath Sathyachandran Kumar, Niall P. Hanan, Lara Prihodko, Julius Anchang, C. Wade Ross, Wenjie Ji and Brianna M Lind

Remote Sens. 2019, 11(7), 815; https://doi.org/10.3390/rs11070815 - 04 Apr 2019

Cited by 8 | Viewed by 3802

Abstract

Globally, the spatial distribution of vegetation is governed primarily by climatological factors (rainfall and temperature, seasonality, and inter-annual variability). The local distribution of vegetation, however, depends on local edaphic conditions (soils and topography) and disturbances (fire, herbivory, and anthropogenic activities). Abrupt spatial or temporal changes in vegetation distribution can occur if there are positive (i.e., amplifying) feedbacks favoring certain vegetation states under otherwise similar climatic and edaphic conditions. Previous studies in the tropical savannas of Africa and other continents using the MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF) satellite data product have focused on discontinuities in the distribution of tree cover at different rainfall levels, with bimodal distributions (e.g., concentrations of high and low tree cover) interpreted as alternative vegetation states. Such observed bimodalities over large spatial extents may not be evidence for alternate states, as they may include regions that have different edaphic conditions and disturbance histories. In this study, we conduct a systematic multi-scale analysis of diverse MODIS data streams to quantify the presence and spatial consistency of alternative vegetation states in Sub-Saharan Africa. The analysis is based on the premise that major discontinuities in vegetation structure should also manifest as consistent spatial patterns in a range of remote sensing data streams, including, for example, albedo and land surface temperature (LST). Our results confirm previous observations of bimodal and multimodal distributions of estimated tree cover in the MODIS VCF. However, strong disagreements in the location of multimodality between VCF and other data streams were observed at 1 km scale. Results suggest that the observed distribution of VCF over vast spatial extents are multimodal, not because of local-scale feedbacks and emergent bifurcations (the definition of alternative states), but likely because of other factors including regional scale differences in woody dynamics associated with edaphic, disturbance, and/or anthropogenic processes. These results suggest the need for more in-depth consideration of bifurcation mechanisms and thus the likely spatial and temporal scales at which alternative states driven by different positive feedback processes should manifest. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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Review

Jump to: Editorial, Research

28 pages, 6342 KiB

Open AccessReview

Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

by Tasiyiwa Priscilla Muumbe, Jussi Baade, Jenia Singh, Christiane Schmullius and Christian Thau

Remote Sens. 2021, 13(3), 507; https://doi.org/10.3390/rs13030507 - 31 Jan 2021

Cited by 17 | Viewed by 5933

Abstract

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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