122 research outputs found
Recommended from our members
An Automatic Mosaicking Algorithm for the Generation of a Large-Scale Forest Height Map Using Spaceborne Repeat-Pass InSAR Correlation Magnitude
This paper describes an automatic mosaicking algorithm for creating large-scale mosaic maps of forest height. In contrast to existing mosaicking approaches through using SAR backscatter power and/or InSAR phase, this paper utilizes the forest height estimates that are inverted from spaceborne repeat-pass cross-pol InSAR correlation magnitude. By using repeat-pass InSAR correlation measurements that are dominated by temporal decorrelation, it has been shown that a simplified inversion approach can be utilized to create a height-sensitive measure over the whole interferometric scene, where two scene-wide fitting parameters are able to characterize the mean behavior of the random motion and dielectric changes of the volume scatterers within the scene. In order to combine these single-scene results into a mosaic, a matrix formulation is used with nonlinear least squares and observations in adjacent-scene overlap areas to create a self-consistent estimate of forest height over the larger region. This automated mosaicking method has the benefit of suppressing the global fitting error and, thus, mitigating the âwallpaperingâ problem in the manual mosaicking process. The algorithm is validated over the U.S. state of Maine by using InSAR correlation magnitude data from ALOS/PALSAR and comparing the inverted forest height with Laser Vegetation Imaging Sensor (LVIS) height and National Biomass and Carbon Dataset (NBCD) basal area weighted (BAW) height. This paper serves as a companion work to previously demonstrated results, the combination of which is meant to be an observational prototype for NASAâs DESDynI-R (now called NISAR) and JAXAâs ALOS-2 satellite missions
Remote Sensing of Savannas and Woodlands
Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome
Using satellite remote sensing to quantify woody cover and biomass across Africa
The goal of quantifying the woody cover and biomass of tropical savannas, woodlands
and forests using satellite data is becoming increasingly important, but limitations in
current scientific understanding reduce the utility of the considerable quantity of satellite
data currently being collected. The work contained in this thesis reduces this knowledgegap,
using new field data and analysis methods to quantify changes using optical, radar
and LiDAR data.
The first paper shows that high-resolution optical data (Landsat & ASTER) can be used
to track changes in woody vegetation in the Mbam Djerem National Park in Cameroon.
The method correlates a satellite-derived vegetation index with field-measured canopy
cover, and the paper concludes that forest encroached rapidly into savanna in the region
from 1986-2006. Using the same study area, but with radar remote sensing data from
1996 and 2007 (ALOS PALSAR & JERS-1), the second paper shows that radar
backscatter correlates well with field-measured aboveground biomass (AGB). This
dataset confirms the woody encroachment within the park; however, in a larger area
around the park, deforestation dominates.
The AGB-radar relationships described above are expanded in the next paper to include
field plots from Budongo Forest (Uganda), the Niassa Reserve (north Mozambique), and
the Nhambita Community Project (central Mozambique). A consistent AGB-radar
relationship is found in the combined dataset, with the RMSE for predicted AGB values
for a site increasing by <30 %, compared with a site-specific equation, when using an
AGB-radar equation derived from the three other sites. The study of the Nhambita site is
extended in the following paper to assess the ability of radar to detect change over short
time periods in this environment, as will be needed for REDD (Reducing Emissions
from Deforestation and Degradation). Using radar mosaics from 2007 and 2009, areas
known (from detailed ground data) to have been degraded decreased in AGB in the radar
change detection, whereas areas of agroforestry and forest protection showed small
increases
Carbon losses from deforestation and widespread degradation offset by extensive growth in African woodlands
Degradationâthe loss of carbon stored in intact woodlandâis very difficult to measure over large areas. Here, the authors show that carbon emissions from degradation in African woodlands greatly exceed those from deforestation, but are happening alongside widespread increases in biomass in remote areas
Remote sensing environmental change in southern African savannahs : a case study of Namibia
Savannah biomes cover a fifth of Earthâs surface, harbour many of the worldâs most iconic
species and most of its livestock and rangeland, while sustaining the livelihoods of an
important proportion of its human population. They provide essential ecosystem services and
functions, ranging from forest, grazing and water resources, to global climate regulation and
carbon sequestration. However, savannahs are highly sensitive to human activities and climate
change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing
anthropogenic disturbances in the form of agricultural intensification and urbanization, have
resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened
ecosystems are some of the least studied or protected, and hence should be given high
conservation priority. Importantly, the scale of land degradation has not been fully explored,
thereby comprising an important knowledge gap in our understanding of ecosystem services
and processes, and effectively impeding conservation and management of these biodiversity
hotspots.
The primary drivers of land degradation include deforestation, triggered by the increasing
need for urban and arable land, and concurrently, shrub encroachment, a process in which the
herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These
processes have significant repercussions on ecosystem service provision, both locally and
globally, although the extents, drivers and impacts of either remain poorly quantified and
understood. Additionally, regional aridification anticipated under climate change, will lead to
important shifts in vegetation composition, amplified warming and reduced carbon
sequestration. Together with a growing human population, these processes are expected to
compound the risk of land degradation, thus further impacting key ecosystem services.
Namibia is undergoing significant environmental and socio-economic changes. The most
pervasive change processes affecting its savannahs are deforestation, degradation and shrub
encroachment. Yet, the extent and drivers of such change processes are not comprehensively
quantified, nor are the implications for rural livelihoods, sustainable land management, the
carbon cycle, climate and conservation fully explored. This is partly due to the complexities
of mapping vegetation changes with satellite data in savannahs. They are naturally spatially
and temporally variable owing to erratic rainfall, divergent plant functional type phenologies
and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to
(i) quantify distinct vegetation change processes across Namibia, and (ii) develop
methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite
data spanning a range of spatial, temporal and spectral resolutions are integrated with field
datasets to achieve these aims, which are addressed in four journal articles.
Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in
land cover classes over five decades throughout the Namibian Kalahari woodlands. The
approach addresses issues implicit in change detection of savannahs by capturing the distinct
phenological phases of woody vegetation and integrating multi-sensor, multi-source data.
Vegetation extent was found to have decreased due to urbanization and small-scale arable
farming. An assessment of the limitations leads to Chapter 4, which elaborates on the
previous chapter by quantifying aboveground biomass changes associated with deforestation
and shrub encroachment. The approach centres on fusing multiple satellite datasets, each
acting as a proxy for distinct vegetation properties, with calibration/validation data consisting
of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating
the contribution of land management to ecosystem carbon changes.
To identify whether biomass is declining across the country, Chapter 5 focuses on regional,
moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS
data are used to model observed fractional woody vegetation cover, a proxy for biomass.
Trends in modelled fractional woody cover are then evaluated in relation to the predominant
land-uses and precipitation. Negative trends slightly outweighed positive trends, with
decreases arising largely in protected, urban and communal areas. Since precipitation is a
fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing
to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as
a function of precipitation, with residuals assumed to describe the fraction of NDVI not
explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend,
although extensive âgreeningâ is unrelated to rainfall. NDVI amplitude, used as a proxy for
vegetation density, indicates a widespread shift to a denser condition.
In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and
temporal limitations characteristic of the previous approaches. Results, together with those of
the previous chapters, are synthesized and a synopsis of the main findings is presented.
Vegetation loss is predominantly caused by demand for urban and arable land. Greening
trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry
and rangeland management, with precipitation presenting little influence. Despite
prevalent greening, degradation processes associated with shrub encroachment, including soil
erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment
occurs regionally. This thesis successfully integrates multi-source data to map, measure and
monitor distinct change processes across scales
Protected Area Performance in the Dry Forests and Savannahs of West Africa: a Study using L-band Synthetic Aperture Radar
Tropical ecosystems harbour the highest concentrations of biodiversity on Earth and play a pivotal role in the global carbon cycle, yet deforestation and degradation continue unabated in many regions, with net forest loss at 5.5 million ha yr-1 between 2010 and 2015. Protected areas offer a partial solution to this problem, with a growing body of evidence demonstrating their effectiveness for habitat conservation in the dense forests of Amazonia, Central Africa and Southeast Asia. Despite containing over a quarter of global biodiversity hotspots and being low density but significant carbon stores, tropical drylands have received far less attention in conservation terms, and research into protected areas in these ecosystems is far more limited. The overall effectiveness of protected areas in different dryland regions, and the factors influencing performance, are less understood. By measuring protected area performance as a function of aboveground biomass change, this study investigated the effectiveness of protected areas in the savannah belt of Nigeria, a country with a long history of environmental degradation. L-band Synthetic Aperture Radar (SAR), a form of remote sensing that penetrates the vegetation canopy, provided a means of consistently monitoring aboveground biomass change over time. Twenty-one areas, ranging in size from 117,000 ha to 608,410 ha, and offering varying levels of protection according to IUCN designations, were selected, with aboveground biomass changes between 2007 and 2017 determined by subjecting L-band SAR data to a novel approach called âBiomass Matchingâ. The combination of SAR and Biomass Matching allowed aboveground biomass changes within these protected areas to be detected and estimated without the need for supplementary field data, which is usually required to calibrate such remote sensing data. All but four protected areas experienced increases in aboveground biomass over the study period, with mean change being +1.22 Mg ha-1, compared to +0.26 Mg ha-1 for a set of twelve similar unprotected areas. Furthermore, their performance was affected by an array of factors, though accessibility and management efficacy were deemed the most influential. These results suggest that, with appropriate monitoring and resourcing, protected areas in Nigerian dry forests and savannahs can provide effective habitat conservation, though more inaccessible areas will inherently perform better
Land cover and forest mapping in boreal zone using polarimetric and interferometric SAR data
Remote sensing offers a wide range of instruments suitable to meet the growing need for consistent, timely and cost-effective monitoring of land cover and forested areas. One of the most important instruments is synthetic aperture radar (SAR) technology, where transfer of advanced SAR imaging techniques from mostly experimental small test-area studies to satellites enables improvements in remote assessment of land cover on a global scale. Globally, forests are very suitable for remote sensing applications due to their large dimensions and relatively poor accessibility in distant areas.
In this thesis, several methods were developed utilizing Earth observation data collected using such advanced SAR techniques, as well as their application potential was assessed. The focus was on use of SAR polarimetry and SAR interferometry to improve performance and robustness in assessment of land cover and forest properties in the boreal zone. Particular advances were achieved in land cover classification and estimating several key forest variables, such as forest stem volume and forest tree height.
Important results reported in this thesis include: improved polarimetric SAR model-based decomposition approach suitable for use in boreal forest at L-band; development and demonstration of normalization method for fully polarimetric SAR mosaics, resulting in improved classification performance and suitable for wide-area mapping purposes; establishing new inversion procedure for robust forest stem volume retrieval from SAR data; developing semi-empirical method and demonstrating potential for soil type separation (mineral soil, peatland) under forested areas with L-band polarimetric SAR; developing and demonstrating methodology for simultaneous retrieval of forest tree height and radiowave attenuation in forest layer from inter-ferometric SAR data, resulting in improved accuracy and more stable estimation of forest tree height
- âŠ