Special Issue "Remote Sensing of Savannas and Woodlands"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Dr. Michael J. Hill
Website
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

Manuscript Submission Information

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Published Papers (8 papers)

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Research

Open AccessArticle
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach
Remote Sens. 2020, 12(17), 2685; https://doi.org/10.3390/rs12172685 - 19 Aug 2020
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 [...] Read more.
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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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
Remote Sens. 2020, 12(6), 924; https://doi.org/10.3390/rs12060924 - 13 Mar 2020
Cited by 3
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 [...] Read more.
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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Open AccessArticle
Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography
Remote Sens. 2020, 12(4), 600; https://doi.org/10.3390/rs12040600 - 11 Feb 2020
Cited by 2
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 [...] Read more.
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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Open AccessArticle
A Healthy Park Needs Healthy Vegetation: The Story of Gorongosa National Park in the 21st Century
Remote Sens. 2020, 12(3), 476; https://doi.org/10.3390/rs12030476 - 03 Feb 2020
Cited by 1
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 [...] Read more.
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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Open AccessArticle
The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands
Remote Sens. 2020, 12(3), 406; https://doi.org/10.3390/rs12030406 - 28 Jan 2020
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 [...] Read more.
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 (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) 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 FNPV and positive trends FBS. 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 FNPV 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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Open AccessFeature PaperArticle
Functional Phenology of a Texas Post Oak Savanna from a CHRIS PROBA Time Series
Remote Sens. 2019, 11(20), 2388; https://doi.org/10.3390/rs11202388 - 15 Oct 2019
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 [...] Read more.
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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Open AccessArticle
Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso
Remote Sens. 2019, 11(10), 1161; https://doi.org/10.3390/rs11101161 - 15 May 2019
Cited by 3
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 [...] Read more.
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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Open AccessArticle
Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data
Remote Sens. 2019, 11(7), 815; https://doi.org/10.3390/rs11070815 - 04 Apr 2019
Cited by 2
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 [...] Read more.
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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