1,720,997 research outputs found
Remote sensing of the distribution and quality of subtropical C3 and C4 grasses.
Full text linkThesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2013.Global climate change is expected to be accompanied by changes in the composition of plant
functional types. Such changes are predicted to follow shifts in the percentage cover and abundance
of grass species, following the C3 and C4 photosynthetic pathways. These two groups differ in a
number of physiological, structural and biochemical aspects. It is important to measure these
characteristic properties because they affect ecosystem processes, such as nutrient cycling. High
spectral and spatial resolution remote sensing systems have been proven to offer data, which can be
used to accurately detect, classify and map plant species. The major challenge, however, is that the
spectral reflectance data obtained over many narrow contiguous channels (i.e. hyperspectral data)
represent multiple classes that are often mixed for a limited training-sample size. This is commonly
referred to as the Hughes phenomenon or “the curse of dimensionality”. In the context of
hyperspectral data analysis, the Hughes phenomenon often introduces a high degree of
multicollinearity, which is caused by the use of highly-correlated spectral predictors.
Multicollinearity is a prominent problem in processing hyperspectral data for vegetation
applications, due to similarities in the spectral reflectance properties of biophysical and biochemical
attributes. This study explored an innovative method to solve the problems associated with spectral
dimensionality and the related multicollinearity, by developing a user-defined inter-band correlation
filter function to resample hyperspectral data. The proposed resampling technique convolves the
spectral dependence information between a chosen band-centre and its shorter and longer
wavelength neighbours. The utility of the new resampling technique was assessed for discriminating
C3 (Festuca costata) and C4 (Themeda triandra and Rendlia altera) grasses and for predicting their
nutrient content (nitrogen, protein, moisture, and fibre), using partial least squares and random forest
regressions. In general, results obtained showed that the user-defined inter-band correlation filter
technique can mitigate the problem of multicollinearity in both classification and regression
analyses. Wavebands in the shortwave infrared region were found to be very important in regression
and classification analyses, using field spectra-only datasets. Next, the analyses were up-scaled from
field spectra to the new generation multispectral satellite, WorldView-2 imagery, which was
acquired for the Cathedral Peak region of the Drakensberg Mountains. The results obtained, showed
that the WV2 image data contain useful information for classifying the C3 and C4 grasses and for
predicting variability in their nitrogen and fibre concentrations. This study makes a contribution by
developing a user-defined inter-band correlation filter to resample hyperspectral data, and thereby
mitigating the high dimensionality and multicollinearity problems, in remote sensing applications
involving C3 and C4 grass species or communities
Intergrating environmental variables with worldview-2 data to model the probability of occurence of invasive chromolena odata in forest canopy gaps : Dukuduku forest in KwaZulu-Natal, South Africa.
No full textThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.Several alien plants are invading subtropical forest ecosystems through canopy gaps,
resulting in the loss of native species biodiversity. The loss of native species in such habitats
may result in reduced ecosystem functioning. The control and eradication of these invaders
requires accurate mapping of the levels of invasion in canopy gaps. Our study tested (i) the
utility of WorldView-2 imagery to map forest canopy gaps, and (ii) an integration of
WorldView-2 data with environmental data to model the probability of occurrence of
invasive Chromolaena odorata (triffid weed) in Dukuduku forest canopy gaps of KwaZulu-
Natal, South Africa. Both pixel-based classification and object-based classification were
explored for the delineation of forest canopy gaps. The overall classification accuracies
increased by ± 12% from a spectrally resampled 4 band image similar to Landsat (74.64%) to
an 8 band WorldView-2 imagery (86.90%). This indicates that the new bands of WorldView
such as the red edge band can improve on the capability of common red, blue, green and
near-infrared bands in delineating forest canopy gaps. The maximum likelihood classifier
(MLC) in pixel-based classification yielded the overall classification accuracy of 86.90% on
an 8 band WorldView-2 image, while the modified plant senescence reflectance index
(mPSRI) in object-based classification yielded 93.69%. The McNemar’s test indicated that
there was a statistical difference between the MLC and the mPSRI. The mPSRI is a
vegetation index that incorporates the use of the red edge band, which solves a saturation
problem common in sensors such as Landsat and SPOT.
An integrated model (with both WorldView-2 data and environmental data) used to predict
the occurrence of Chromolaena odorata in forest gaps yielded a deviance of about 42% (D2 =
0.42), compared to the model derived from environmental data only (D2 = 0.12) and
WorldView-2 data only (D2 = 0.20). A D2 of 0.42 means that a model can explain about 42%
of the variability of the presence/absence of Chromolaena odorata in forest gaps. The
Distance to Stream and Aspect were the significant environmental variables (ρ < 0.05) which
were positively correlated with presence/absence of Chromolaena in forest gaps.
WorldView-2 bands such as the coastal band (λ425 nm) yellow band (λ605 nm) and the nearinfrared-
1 (λ833 nm) are positively and significantly related to the presence/absence of
invasive species (ρ < 0.05). On the other hand, a significant negative correlation (ρ < 0.05) of
near-infrared-2 band (λ950 nm) and the red edge normalized difference vegetation index
(NDVI725) suggests that the probability of occurrence of invasive Chromolaena increases forest gaps with low vegetation density. This study highlights the importance of WorldView-
2 imagery and its application in subtropical indigenous coastal forest monitoring
Remote sensing of wetland tree species in the iSimangaliso Wetland Park, KwaZulu-Natal, South Africa.
Full text linkDoctor of Science in Geography.The impact of global change is expected to result in changes in the distribution and composition of species. Coastal swamp and mangrove forests are some of the most threatened forest types in the world. Remote sensing is a suitable tool for monitoring species distribution and varying condition because of its spatial extent and repeatability. The ability of remote sensing to separate between species can be attributed primarily to its capability to quantify the absorption features in the electromagnetic spectrum which relate to plant biochemical and biophysical properties such as pigments, nutrients (proteins and starch), leaf water content, leaf angle distribution, leaf area index and foliage biomass. For some species, these phenological variations are extreme, as in the case of deciduous tree species, thus enhancing the ability to differentiate between species, whereas others are less pronounced, such as with evergreen tree species, making spectral distinction between species much more challenging.
Few studies have assessed the pigment and nutrient phenology of evergreen tree species in subtropical forested wetlands, let alone their spectral differences. This study assesses whether multi-season data across a number of phenological phases of evergreen wetland tree species will improve their classification accuracy when compared to a single season and single phenological event. The objectives were to (i) assess whether tree species had unique seasonal profiles of foliar biochemicals; (ii) ascertain the spectral bands of plant properties which remain important across phenological phases for species classification; (iii) determine whether leaf reflectance spectra from multiple seasons would improve species classification when compared to a single season; and (iv) whether multi-season imagery would improve species discrimination when compared to a single season. Thus, the study made use of leaf level and canopy level spectra collected using a handheld spectrometer and spaceborne RapidEye imagery, respectively.
Six dominant evergreen tree species from forested wetlands in the subtropical region of KwaZulu-Natal, South Africa, were sampled across four seasons (winter, spring, summer and autumn). Differences in foliar biochemical concentration were assessed for two pigments, including carotenoids and chlorophylls, as well as two nutrients, nitrogen and phosphorous. The results showed that the majority of species had no significant changes in foliar pigments across the four seasons. Foliar nitrogen showed a significantly higher variability in the spring, summer and autumn seasons compared to the winter, whereas foliar phosphorus also varied across the seasons but to a lesser degree. The highest percentage of species pairs was separable using foliar nitrogen, compared to the pigments and phosphorus, emphasizing the importance of nutrients such as leaf proteins for species discrimination.
The study found a changing relationship between leaf spectra and foliar nutrient concentration across the four seasons for the six evergreen tree species. Twenty-two spectral bands which are related to known absorption features of plant properties were
identified across the four seasons as important for tree species discrimination. The relationship between leaf spectra and foliar nitrogen was highest during the spring, summer and autumn seasons for narrow bands associated with absorption features of proteins compared to the red-edge region. The spectra band combination 2130 nm and 2240 nm yielded the highest coefficient of determination between leaf spectra and foliar nitrogen across three of the four seasons. Season-specific prediction models were found to be more accurate in predicting foliar nitrogen than prediction models from across all seasons. The twenty-two bands were effective for the data reduction of the hyperspectral data and yielded a similar overall accuracy compared to 421 bands.
Multi-seasonal data improved tree species classification for multispectral sensors with a few bands. The classification, in which multi-season leaf spectra or canopy data from RapidEye imagery was used, resulted in higher overall and user’s accuracies when compared to the single-season classifications. In contrast, the use of multi-season data for the classification of leaf spectra with 22 narrow bands, showed no statistical significance of differences compared to the classification results of the single season in which the highest overall accuracy of all single seasons had been obtained. The value of an increased classification accuracy should however be measured against the increase of cost when using images from multiple seasons. The study concludes that although seasonal profiles of foliar biochemicals overlap, multi-season information do improve species discrimination at foliar biochemical, leaf-spectra and canopy-spectra levels
Remote sensing of leaf area index in Savannah grass using inversion of radiative transfer model on Landsat 8 imagery : case study Mpumalanga, South Africa
Full text linkSavannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources.
In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources.M. Sc. (Environmental Science)Environmental Science
Assessment of vegetation productivity in the Umfolozi catchment using Leaf Area Index (LAI) derived from SPOT 6 image.
Full text linkMaster of Science Environmental sciences.Around the world, rural areas rely on the natural resources for their sustenance. These
include grazing lands for livestock production and fuel wood harvesting for heating
and cooking, as well as for medicinal purposes. These natural resources are barely
managed in rural areas which exacerbate the challenge of land degradation due to unsustainable
overgrazing and fuel wood collection. Land degradation has been identified
as one of the key global problems are the root cause of poverty, food insecurity
and malnutrition. In South Africa, the uMfolozi catchment is very vulnerable to disturbance
due to slow ecological recovery, growing human populations and episodic
droughts. Leaf Area Index (LAI), defined as one half the total green leaves per unit
ground surface area, is an inventory of the plant green leaves that defines the actual
size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data
could serve as an indicator of vegetation productivity. The main aim of the study is
to estimate LAI as an indicator of vegetation productivity using remotely sensed data.
First, field collected LAI were used to assess LAI models derived from various vegetation
indices and bands. Secondly, multivariate statistics were used to combine bands
and indices in estimating LAI. Combining reflectance at various bands and vegetation
indices yielded higher estimation accuracy of LAI (Bootstrapped: R² = 0.71, RMSE =
0.92) as compared to using individual bands or indices. Furthermore the study found
that environmental variables such as slope, Digital Elevation Model (DEM) and annual
mean temperature significantly influenced the spatial distribution of LAI. There is a
scope to estimate LAI empirically using bands and vegetation indices which are more
site and data specific, but the study further recommends the use of physically-based
models which are known to be robust. In conclusion, estimation of LAI is possible using
remote sensing derived variables combined with multivariate statistical techniques,
which is critical for assessing vegetation productivity
Exploring the relationship between spectral reflectance and tree species diversity in the savannah woodlands.
Full text linkDoctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Pietermaritzburg, 2018.Abstract available in PDF file
Assessing multi-temporal remote sensing imagery for discriminating savannah tree species.
Full text linkM. Sc. University of KwaZulu-Natal, Pietermaritzburg 2014.The advent of new multispectral sensors such as Worldview-2 with very high spatial resolution (VHR) has presented new opportunities for mapping vegetation at species-level. However the use of VHR data for tree species mapping is often confronted with issues of within-canopy spectral variability. The prevailing intraspecies variability in southern African savannah limits our ability to accurately map the distribution of tree species. These challenges necessitate the development of new methods for tree species mapping. This study investigated i) the utility of object-based image analysis (OBIA) for tree species mapping in the savannah environment using Worldview-2 image, ii) the spectral capability of WV-2 for species mapping and iii) the ability of multi-temporal data to enhance spectral separability between tree species in southern African savannah. Using Random Forest (RF), the study could not establish any statistically significant difference between OBIA and pixel-based approach towards savannah tree species classification (zobt < zcrit). However OBIA successfully improved classification accuracy of Sclerocharya birrea and Acacia nigrescens which makes it an appropriate alternative for classifying big trees in the savannah environment using WV-2 image.
Moreover, the spectral configuration of WV-2 with the inclusion of yellow and red-edge bands enhanced the discriminatory power of WV-2 sensor. The WV-2 image achieved higher classification accuracy (74.5% with object-based and 76.4% with pixel-based) than simulated IKONOS image (58.6% with object-based and 67.9% with pixel-based). The difference was statistically significant (zobt > zcrit). The use of multi-temporal data enhanced spectral variability between species and achieved the highest classification accuracy (80.4%) than March and April dates (72.9% and 76.4%, respectively). Multi-temporal data mitigated the spectral confusion between Sclerocharya birrea and Dichrostachys cinerea and achieved producer’s and user’s accuracy of above 60% for these tree species. The results highlight the opportunities available to biodiversity managers due to advances in remote sensing technology. The ability to accurately map tree species is the key element in the management of savannah biodiversity
Land acquisition for agribusiness development in South Comoé region, Côte d’Ivoire
Full text linkThesis (PhD (Environmental Management))--University of Pretoria, 2021.Agribusiness which contributes significantly to most economies in Africa is under threat from socio-economic and political factors which affect their productivity. This study set out to investigate the factors influencing the growth of agribusiness (agri-value chain) in sub-Saharan African countries (SSA) using South Comoé region of Côte d'Ivoire as a case study. The research is built on the argument that there is a lack of viable policy frameworks to guide effective negotiations for land acquisition and benefits sharing in the process of establishing agribusiness. The research aim was achieved through an empirical study based on focus group discussions (FGDs) and key informants’ interviews. The data were thematically analysed and discussed through the perspectives of rural community members, agribusiness investors and local government authorities (key participants identified) of the districts of Bonoua, Adiaké and Aboisso, South Comoé region. The results of this study revealed how legal pluralism (customary and modern tenure systems) posed a challenge for land acquisition negotiation for agribusiness development. The lack of integration of customary laws in the colonial and post-colonial eras created inequality in the land acquisition process. Inequalities in the negotiation of land for the establishment and grow of agribusiness has resulted to conflicts experienced in a number of countries in SSA. In addition, this study shows that the benefit sharing mechanism practiced in South Comoé, Côte d’Ivoire is inconsistent with universal standards such as that defined by the Nagoya Protocol designed to ensure fair benefit distribution in biodiversity conservation. As such the rural communities suffer marginalisation in the negotiation of benefits for the sacrifice of communal land for the establishment of Agribusiness Corporation. Moreover, the inequalities and marginalisation of grassroots communities during the negotiation of land and benefits sharing was found in this study to stem from the lack of viable land reform policy to secure the tenure rights of such rural community members. The results of the study therefore suggest a dire need for the government of Côte d’Ivoire to design effective land reform policy which takes into consideration rural community rights for the establishment of agribusiness. The land reform policy should ensure adequate negotiation of communal land and benefits thus creating an enabling environment for the growth of agribusiness. The study thus contributes to knowledge on the key role of land reform policy in ensuring the growth of agribusiness in sub-Saharan Africa.Geography, Geoinformatics and MeteorologyPhD (Environmental Management)Unrestricte
Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework
Full text linkPlease read abstract in the article.http://www.tandfonline.com/loi/tres20hj2020Plant Production and Soil Scienc
Influence of Miombo woodlands management, drivers on land use/cover and forest change, woody composition/diversity, population structure in Malawi
No full textThesis (PhD (Forest Science))--University of Pretoria, 2020.The Miombo woodland vegetation of southern Africa has been subjected to anthropogenic pressures in recent years, resulting in a change in its cover and floristic-structural composition, and the population status of important tree species. The recent land use land cover changes (LULCC) is of concern due to the negative impacts on the Miombo woodland ecological functions. Understanding the overall dynamics of the vegetation that include LULCC, forest cover change, tree species composition, and diversity, population structure (PS) in relation to forest degradation, loss of endangered/rare species, is pivotal in influencing policy and sustainable woodland management. The Malawi Government instituted policies that allowed for improved forest management. However, the impact of forest management regimes on forest cover, tree species diversity, and structure is not well established. The study aimed to determine LULCC and forest cover changes and the associated drivers and how co-management (CM) and government-management (GM) impact on the florist-structural composition, diversity and the population structure of the important tree species in Malawian Miombo landscape.
Firstly, the study analysed LULCC and the comparative impact of CM and GM on the forest cover in Malawi, for the period 1999-2018. CM involves a contractual agreement between communities (with a forest management plan who have been empowered to manage forest resources) and the Forest Department (representing the Government) in managing forest reserves. GM is the protection of forest resources by the government through the Forest Department. Since the introduction of participatory forest management (PFM), such as CM, its impact has not been established. Google Earth images (Landsat mosaics) for 1999 and 2018 for Malawi were acquired, registered, and pre-processed in Environment for Visualizing Images (ENVI 4.7) Harris Geospatial Solutions. LULCC estimation using the Inter-Governmental Panel on Climate Change (IPCC) classes was determined using the differences in error-adjusted areas between 1999 and 2018. Overall accuracies were >90%. Woodland net losses of 8.4% were to Plantation, Grassland, and Agriculture transition intensities. Agriculture net gains of 9.6% were from Grassland, Settlement, and Woodland transitions for Malawi. Forest cover within CM and GM indicated losses. Participatory land use plans and monitoring for diversified management in Malawian Miombo woodlands are required to mitigate anticipated irreversible impacts in the landscape.
The second study investigated the factors that influence changes in CM and GM forest reserves between 1999 and 2018. CM and GM regimes in Miombo woodlands are possible interventions to mitigate forest degradation and deforestation in southern Africa. However, few studies have investigated the direct and indirect drivers of LULCC using socioeconomic characteristics and Remote sensing data in CM and GM regimes. The drivers of LULCC in forest reserves, and management challenges were identified using participatory assessments in both management regimes. The changes in woodland were observed with varying extent. Communities' perceptions in the factors contributing to changes in CM and GM forest reserves were similar and mostly due to the conversion of woodlands to agriculture while extraction of woody products led to forest degradation. In both management strategies, population pressure, youthful age, poverty, and poor education were associated with forest-based livelihood activities and therefore the woodland changes. The overall woodland cover loss to grassland is attributed to its importance as a source of energy. There is thus a need to harmonize policies for sustainable use and management of woodlands in order to address local, national, and regional ecosystem services. Future studies will need to link Remote sensing and socioeconomic data as part of a monitoring tool that could assist to sustainably adapt to changes in the woodlands and surrounding communities.
The third study compared the Miombo Tree species composition and diversity between CM and GM regimes in Malawi. Tree species composition and diversity information is limited between CM and GM regimes. Two CM and two GM forest reserves were purposively selected to act as representatives of management regimes in the northern and southern regions of Malawi. Forest inventory data from 80 randomly selected nested circular plots were used. Two plot sizes: a large plot (0.16 ha; radius 22.6 m) to record stems ≥30 cm DBH, and the main plot (0.04 ha; radius 11.28 m) to record stems 5.0-29.9 cm DBH and species names. In total, 109 tree species belonging to 38 families, 87 species in GM FRs (Kaning’ina 58, Thambani 52), and 69 in CM FRs (Perekezi 45, Liwonde 43) were recorded. The largest families (number of species between brackets) were Fabaceae (34, with 3 subfamilies, Caesalpinioideae (17), Papilionoideae (12), and Mimosoideae (5), an indication of their adaptive potential in the area. Other important families were Combretaceae (7), Rubiaceae (7), and Clusiaceae (4). Species similarity between management regimes was low and was attributed to site factors, species characteristics and intensity of disturbances. TWINSPAN classification results were related to differences in site conditions and disturbances caused by historical and current resource use in management regimes. The eigenvalues ≥0.3 across CM and GM sub-communities indicated high stability. Brachystegia and Julbernardia species were highly important in CM and GM sub-communities. Uapaca species were highly important in agriculture and settlement abandoned areas in GM forest reserve. The study recommends selective harvesting to allow for dominant (Brachystegia and Julbernardia) and associated Miombo species to regenerate. Species richness and evenness (diversity) was high in more disturbed CM and GM sub-communities compared to intact areas. The high diversity was related to tree species high abundances of smaller stems with few scattered big trees. These results call for a Forest policy review to allow planned harvesting in GM forest reserves. Law enforcement is also required in both management regimes to mitigate unsustainable harvesting in sensitive areas. Future studies should include zonation to improve differentiation between site factors and wood extraction in stand development stages in management regimes.
The fourth study compared the Miombo population structure (PS) between CM and GM regimes. Such information is limited in CM and GM regimes since the introduction of PFM in Malawi. The size class distribution (SCD) of sub-canopy/canopy species showed a reversed J-shaped profile in CM and GM forest reserves (South), when compared to CM and GM, in northern Malawi. These findings may reflect differences in the historical woodland utilization in the two regions. The bell-shaped SCDs in CM sub-communities with high stem density of Brachystegia and Julbernardia species suggest strong demand for light for successful recruitment from regeneration to adult trees and could be related to wood utilization. The reversed J-shape SCDs in northern GM sub-communities with high regeneration stem density of Pittosporum viridiflorum suggest an increase in shade-tolerant evergreen tree species under a low-level disturbance. Timber species showed interrupted SCDs with few to no stems, indicating challenges in regeneration. Pioneer species were associated with disturbances under CM indicating woodland recovery. The patterns in SCD showed similarities and differences between CM and GM sub-communities between the two management strategies. In Community 2, there were significant differences (p = 0.002) between management strategies with low canopy densities in CM, which could be attributed to unsustainable harvesting. Furthermore, saplings showed significant differences with a higher stems ha-1 in CM compared to GM. Trees and regeneration SCDs suggest a thorough analysis of the PS of varied species associations to guide sustainable resource use. An adaptive management approach that uses silvicultural systems to promote sustainable forest management is recommended. Additionally, selective harvesting in recovery stages would reduce intense competition in the dense, even-aged stands. However, there is need for instituting enabling policies and to monitor changes in both management regimes to promote biodiversity conservation, resource use, and diverse ecosystem services at all levels. Malawi Government Scholarship Program and the African Forest ForumPlant Production and Soil SciencePhD (Forest Science)Unrestricte
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