Characterizing degradation gradients through land cover change analysis in rural Eastern Cape, South Africa

CITATION: Munch, Z., et al. 2017. Characterizing degradation gradients through land cover change analysis in rural Eastern Cape, South Africa. Geosciences, 7(1):7, doi:10.3390/geosciences7010007.The original publication is available at http://www.mdpi.comLand cover change analysis was performed for three catchments in the rural Eastern Cape, South Africa, for two time steps (2000 and 2014), to characterize landscape conversion trajectories for sustained landscape health. Land cover maps were derived: (1) from existing data (2000); and (2) through object-based image analysis (2014) of Landsat 8 imagery. Land cover change analysis was facilitated using land cover labels developed to identify landscape change trajectories. Land cover labels assigned to each intersection of the land cover maps at the two time steps provide a thematic representation of the spatial distribution of change. While land use patterns are characterized by high persistence (77%), the expansion of urban areas and agriculture has occurred predominantly at the expense of grassland. The persistence and intensification of natural or invaded wooded areas were identified as a degradation gradient within the landscape, which amounted to almost 10% of the study area. The challenge remains to determine significant signals in the landscape that are not artefacts of error in the underlying input data or scale of analysis. Systematic change analysis and accurate uncertainty reporting can potentially address these issues to produce authentic output for further modelling.http://www.mdpi.com/2076-3263/7/1/7Publisher's versio

Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s 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 “greening” 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

Disaggregating Tree And Grass Phenology In Tropical Savannas

Savannas are mixed tree-grass systems and as one of the world’s largest biomes represent an important component of the Earth system affecting water and energy balances, carbon sequestration and biodiversity as well as supporting large human populations. Savanna vegetation structure and its distribution, however, may change because of major anthropogenic disturbances from climate change, wildfire, agriculture, and livestock production. The overstory and understory may have different water use strategies, different nutrient requirements and have different responses to fire and climate variation. The accurate measurement of the spatial distribution and structure of the overstory and understory are essential for understanding the savanna ecosystem. This project developed a workflow for separating the dynamics of the overstory and understory fractional cover in savannas at the continental scale (Australia, South America, and Africa). Previous studies have successfully separated the phenology of Australian savanna vegetation into persistent and seasonal greenness using time series decomposition, and into fractions of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS) using linear unmixing. This study combined these methods to separate the understory and overstory signal in both the green and senescent phenological stages using remotely sensed imagery from the MODIS (MODerate resolution Imaging Spectroradiometer) sensor. The methods and parameters were adjusted based on the vegetation variation. The workflow was first tested at the Australian site. Here the PV estimates for overstory and understory showed best performance, however NPV estimates exhibited spatial variation in validation relationships. At the South American site (Cerrado), an additional method based on frequency unmixing was developed to separate green vegetation components with similar phenology. When the decomposition and frequency methods were compared, the frequency method was better for extracting the green tree phenology, but the original decomposition method was better for retrieval of understory grass phenology. Both methods, however, were less accurate than in the Cerrado than in Australia due to intermingling and intergrading of grass and small woody components. Since African savanna trees are predominantly deciduous, the frequency method was combined with the linear unmixing of fractional cover to attempt to separate the relatively similar phenology of deciduous trees and seasonal grasses. The results for Africa revealed limitations associated with both methods. There was spatial and seasonal variation in the spectral indices used to unmix fractional cover resulting in poor validation for NPV in particular. The frequency analysis revealed significant phase variation indicative of different phenology, but these could not be clearly ascribed to separate grass and tree components. Overall findings indicate that site-specific variation and vegetation structure and composition, along with MODIS pixel resolution, and the simple vegetation index approach used was not robust across the different savanna biomes. The approach showed generally better performance for estimating PV fraction, and separating green phenology, but there were major inconsistencies, errors and biases in estimation of NPV and BS outside of the Australian savanna environment

Evaluation of the impact of climate and human induced changes on the Nigerian forest using remote sensing

The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.Tertiary Education Trust Fund, Nigeri

Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia

Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa

While cropland expansion and demand for woodfuel exert increasing pressure on woody vegetation in East Africa, climate change is inducing woody cover gain. It is however unclear if these contrasting patterns have led to net fractional woody cover loss or gain. Here we used non-parametric fractional woody cover (WC) predictions and breakpoint detection algorithms driven by satellite observations (Landsat and MODIS) and airborne laser scanning to unveil the net fractional WC change during 2001-2019 over Ethiopia and Kenya. Our results show that total WC loss was 4-times higher than total gain, leading to net loss. The contribution of abrupt WC loss (59%) was higher than gradual losses (41%). We estimated an annual WC loss rate of up to 5% locally, with cropland expansion contributing to 57% of the total loss in the region. Major hotspots of WC loss and degradation corridors were identified inside as well as surrounding protected areas, in agricultural lands located close to agropastoral and pastoral livelihood zones, and near highly populated areas. As the dominant vegetation type in the region, Acacia-Commiphora bushlands and thickets ecosystem was the most threatened, accounting 69% of the total WC loss, followed by montane forest (12%). Although highly outweighed by loss, relatively more gain was observed in woody savanna than in other ecosystems. These results reveal the marked impact of human activities on woody vegetation and highlight the importance of protecting endangered ecosystems from increased human activities for mitigating impacts on climate and supporting sustainable ecosystem service provision in East Africa.Peer reviewe

Development and analysis of global long-term burned area based on avhrr-ltdr data

La tesis doctoral titulada “Development and analysis of global Long-Term Burned Area based on AVHRR-LTDR data” propone la extensión temporal de la información global de área quemada obtenida a partir de imágenes de satélite. Un nuevo y consistente producto global de área quemada fue desarrollado ofreciendo datos por casi cuarenta años (1982-2018). El producto fue generado, analizado y validado, además de aplicado en el estudio global de tendencias espaciales y temporales. El trabajo fue financiado y desarrollado bajo el proyecto Fire Disturbance (Fire_cci) perteneciente al programa Climate Change initiative (CCI) de la Agencia Espacial Europea (ESA). Un producto global en una escala temporal larga contribuye a rellenar un vacío en la información necesaria para los modelos del clima y el estudio del cambio climático. Para llevar a cabo este objetivo fue preciso utilizar la base de datos de imágenes globales de satélite más extensa disponible, los datos pertenecientes al sensor Advanced Very High Resolution Radiometer (AVHRR) y de los satélites National Oceanic and Atmospheric Administration (NOAA). Por ello, se hizo uso de un algoritmo novedoso que introdujo una visión renovada para afrontar estas limitaciones y detectar área quemada. Modelos mensuales de Random Forest fueron desarrollados. Un innovador índice sintético y la obtención de proporciones de área quemada por cada pixel, hizo de este algoritmo y producto, únicos. Además, una validación y un estudio espacio-temporal fue realizado por primera vez en una serie temporal larga de casi cuarenta años. Los resultados de la inter-comparación con otros productos globales de área quemada, ofreció correlaciones altas, mostrando relaciones mensuales con los productos MCD64A1 (r = 0.89, %MAE = 21%) y FireCCI51 (r = 0.95, %MAE = 10%) durante las series temporales comunes. También se obtuvieron altas correlaciones con los perímetros oficiales que se extendían a la época pre-MODIS, como (Australia: r = 0.89, %MAE = 26%; Canadá: r = 0.81, %MAE = 33%; Alaska: r = 0.96, %MAE = 42%). La degradación de los satélites no influyó a los patrones de área quemada en la serie temporal. La validación fue novedosa al realizar a una serie temporal de casi 30 años, con un buen comportamiento del producto, y el uso de proporciones fue capaz de reducir errores. Los datos del periodo AVHRR2 del producto tienen mayor incertidumbre que AVHRR3 debido a la calidad de los sensores, aunque ambos periodos son consistentes. El producto desarrollado en esta tesis reveló tendencias de disminución de área quemada en África oriental, regiones boreales, Asia central y el sur de Australia, y tendencias de aumento de área quemada en África occidental y central, Sudamérica, USA y el norte de Australia

An assessment of fire regimes on different vegetation types using MODIS burned area products.

Master of Science in Geography. University of KwaZulu-Natal, Pietermaritzburg 2017.Cost-efficient satellite data has been widely used to map the spatial and temporal distribution of fires in resource scarce regions such as southern Africa. However, the accuracy of such data is often unknown, which compromises the integrity of the mapped burnt areas. In that regard, there is need to validate the accuracy of these data products if they are to be used for drawing strategies for monitoring and managing natural resources such as grasslands. This study, therefore, evaluated the mapping accuracy of the cost-free MODIS burned area satellite data products for 2013 and 2014 burning seasons in the grasslands of KwaZulu-Natal, South Africa. To validate MODIS data, we used independent reference data derived from the new generation Landsat 8 Operational Land Imager (OLI) based on the new accuracy assessment procedure promulgated. A total of 60 ground sampling sites were used in this study in conjunction with Landsat 8 data to validate MODIS burned area products. Results of this study illustrate a high level of agreement (>80% overall accuracy) between the MODIS burned area products and the independent reference data. Meanwhile, Landsat data was also validated by ground collected points yielding an accuracy of 94%. Specifically, MODIS data was validated by ground collected points yielding an accuracy of 87%. These findings suggest that MODIS burned area products (MCD45A1) are an accurate, reliable and cheap data source for mapping burnt areas at a regional scale

Climatic impacts of vegetation dynamics in Eastern Africa

The climate system responds to changes in the structure and physiology of vegetation. These changes can be induced by seasonal growing cycles, anthropogenic land cover changes (LCCs), and precipitation extremes. The extent to which vegetation changes impact the climate depends on the type of ecosystem, the season, and the intensity of perturbations from LCCs and precipitation extremes. Under the growing impacts of climate change and human modification of natural vegetation cover, understanding and monitoring the underlying biogeophysical processes through which vegetation affects the climate are central to the development and implementation of effective land use plans and mitigation measures. In Eastern Africa (EA) the vegetation is characterized by multiple growing cycles and affected by agricultural expansion as well as recurrent and severe drought events. Nonetheless, the degrees to which vegetation changes affect the surface energy budget and land surface temperature (LST) remain uncertain. Moreover, the relative contributions of various biogeophysical mechanisms to land surface warming or cooling across biomes, seasons, and scales (regional to local) are unknown. The objective of this thesis was to analyze and quantify the climatic impacts of land changes induced by vegetation seasonal dynamics, agricultural expansion, and precipitation extremes in EA. In particular, this thesis investigated these impacts across biomes and spatio-temporal scales. To address this objective, satellite observation and meteorological data were utilized along with empirical models, observation-based metrics, and statistical methods. The results showed that rainfall–vegetation interaction had a strong impact on LST seasonality across ecoregions and rainfall modality patterns. Furthermore, seasonal LST dynamics were largely controlled by evapotranspiration (ET) changes that offset the albedo impact on the surface radiation balance. Forest loss disturbed the LST dynamics and increased local LST consistently and notably during dry seasons, whereas during the wet season its impact was limited because of strong rainfall–vegetation interaction. Moreover, drought events affected LST anomalies; however, the impact of droughts on temperature anomalies was highly regulated by vegetation greening. In addition, the conversion of forest to cropland generated the highest net warming (1.3 K) compared with other conversion types (savanna, shrubland, grassland, and cropland). Warming from the reduction of ET and surface roughness was up to ~10 times stronger than the cooling effect from albedo increases (−0.12 K). Furthermore, large scale analysis revealed a comparable warming magnitude during bushland-to-cropland conversion associated with the dominant impact of latent heat (LE) flux reduction, which outweighed the albedo effect by up to ~5 times. A similar mechanism dominated the surface feedback during precipitation extremes; where LE flux anomalies dominated the energy exchange causing the strongest LST anomaly in grassland, followed by savanna. By contrast, the impact was negligible in forest ecosystems. In conclusion, the results of this thesis clarify the mechanics and magnitude of the impacts of vegetation dynamics on LST across biomes and seasons. These results are crucial for guiding land use planning and climate change mitigation efforts in EA. The methods and results of this thesis can assist in the development of ecosystem-based mitigation strategies that are tailored to EA biomes. Moreover, they can be used for assessing the performance of climate models and observation-based global scale studies that focus on the biogeophysical impacts of LCCs. Keywords: LST seasonality; Land cover change; Bushland (Acacia-Commiphora); Biophysical effects; Precipitation extremes; Satellite observation.Ilmastojärjestelmä reagoi kasvillisuuden rakenteen ja fysiologian muutoksiin. Muutokset voivat johtua kasvukauden vaiheesta, ihmistoiminnan vaikutuksesta maanpeitteeseen ja sään ääri-ilmiöistä. Se missä määrin kasvillisuuden muutokset vaikuttavat ilmastoon riippuu ekosysteemistä ja vuodenajasta sekä maanpeitemuutosten ja sään ääri-ilmiöiden voimakkuudesta. Ilmastonmuutoksen ja maanpeitteen muokkaamisen vaikutusten voimistuessa on keskeistä ymmärtää ja seurata biogeofysikaalisia prosesseja, joiden kautta kasvillisuus vaikuttaa ilmastoon. Tällä tiedolla on keskeinen rooli tehokkaiden maankäyttösuunnitelmien kehittämisessä ja toteuttamisessa sekä ilmastonmuutoksen hillinnässä. Itä-Afrikassa kasvillisuudella on ominaisesti useita kasvukausia ja siihen vaikuttavat maatalouden laajentuminen sekä toistuvat ja vakavat kuivuusjaksot. Siitä huolimatta kasvillisuuden muutosten vaikutus energiataseeseen ja maanpinnan lämpötilaan on edelleen epävarmaa. Lisäksi eri biogeofysikaalisten mekanismien suhteellista vaikutusta maanpinnan lämpenemiseen tai jäähtymiseen eri biomien, vuodenaikojen ja mittakaavojen (alueellinen ja paikallinen) välillä ei tunneta. Tämän tutkielman tavoitteena oli analysoida ja kvantifioida kasvillisuuden vuodenaikaisvaihtelun, maatalouden laajentumisen ja sademäärän ääri-ilmiöiden aiheuttamien muutosten ilmastovaikutuksia Itä-Afrikassa. Erityisesti tutkielmassa tarkasteltiin vaikutuksia eri biomien ja mittakaavojen välillä. Tutkielmassa hyödynnettiin satelliittihavaintoja ja meteorologisia tietoja sekä empiirisiä malleja, havaintopohjaisia indeksejä ja tilastollisia menetelmiä. Tulokset osoittivat, että sademäärän ja kasvillisuuden vuorovaikutuksella oli voimakas vaikutus maanpinnan lämpötilan vuodenaikaisvaihteluun kasvillisuustyyppien ja sademoodien välillä. Maanpinnan lämpötilaa säätelivät suurelta osin evapotranspiraation muutokset, jotka kompensoivat albedon vaikutuksia pinnan säteilytasapainoon. Metsän häviäminen häiritsi maanpinnan lämpötilan dynamiikkaa ja lisäsi sitä paikallisesti, etenkin kuivina vuodenaikoina, kun taas sadekauden aikana sen vaikutus oli vähäinen sateen ja kasvillisuuden voimakkaan vuorovaikutuksen vuoksi. Lisäksi kuivuus vaikutti lämpötilan poikkeavuuksiin; kuivuuden vaikutusta sääteli kuitenkin voimakkaasti kasvillisuuden vihertyminen. Metsän muuntaminen viljelysmaaksi aiheutti suurimman nettolämmityksen (1.3 K) verrattuna muihin muutostyyppeihin (savanni, pensaikko, ruohostomaat ja viljelymaat). Evapotranspiraation vähenemisestä ja pinnan epätasaisuudesta aiheutuva lämpeneminen oli jopa noin 10 kertaa voimakkaampi kuin albedon jäähdytysvaikutus (−0.12 K). Lisäksi pensaikon muuntaminen viljelysmaaksi aiheutti vastaavan lämpenemisen. Lämpeneminen liittyi latentin lämpövuon merkityksen vähentymiseen, joka ylitti albedovaikutuksen jopa noin viisinkertaisesti. Samanlainen mekanismi hallitsi sademäärän ääripäiden aikana, jolloin latentin lämpövuon poikkeavuudet hallitsivat energianvaihtoa aiheuttaen voimakkaimman maanpinnan lämpötilan poikkeavuuden ruohostomailla ja savanneilla. Sitä vastoin metsissä vaikutus oli vähäinen. Yhteenvetona voidaan todeta, että tutkielman tulokset selventävät kasvillisuuden dynamiikan vaikutusten mekanismeja ja suuruutta maanpinnan lämpötilaan biomien ja vuodenaikojen välillä. Tulokset ovat tärkeitä Itä-Afrikan maankäytön suunnittelun ja ilmastonmuutoksen hillitsemistoimien ohjaamisessa. Tutkielman menetelmät ja tulokset voivat auttaa kehittämään Itä-Afrikan biomeille räätälöityjä ekosysteemipohjaisia lieventämisstrategioita. Lisäksi niitä voidaan käyttää arvioimaan ilmastomalleja ja havaintopohjaisia globaalin mittakaavan tutkimuksia, jotka keskittyvät maanpeitemuutosten biogeofysikaalisiin vaikutuksiin. Avainsanat: Maanpinnan lämpötilan vuodenaikaisvaihtelu; Maanpeitteen muutos; Pensaikko (AcaciaCommiphora); Biofysikaaliset vaikutukset; Sademäärä; Satelliittikaukokartoitus

Mapping and monitoring of agricultural drought across different land uses and land cover in the North-Eastern KwaZulu Natal

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of Witwatersrand in fulfilment of the academic requirements for the degree of Master of Science in Environmental Sciences June 2018. Johannesburg, South Africa.Drought is complex and one of the least understood natural hazards in Southern Africa. Timely information about the extent, the intensity, duration and impacts of the agricultural drought is essential for adaptation and management. In this study, the research aims, are made to monitor and map agricultural drought across different land uses and land cover in north-eastern KwaZulu-Natal as it was declared a disaster area in 2016 (AgriSA, 2016). Droughts occurred throughout South Africa during the summer season of 2014 to 2015 and 2015 to 2016. In this study the adopted methodology was through the use of remote sensing and Geographic Information System (GIS) techniques. Remote sensing and GIS was used to map and monitor the agricultural drought in the study area. To understand the impacts of the drought across different agricultural land use and other land cover types, the land uses and land cover was classified using Landsat earth observation data and maximum likelihood algorithm in the study area, and multi-temporal Normalized Difference Vegetation Index (NDVI) (1997-2017) with a twenty year interval used to map and monitor the agricultural drought and the meteorological (rainfall) in order to validate the NDVIs. Agricultural drought was then determined from investigating changes between 2015 and 2017 which were years that experienced severe conditions. The rainfall data was interpolated using Inverse Distance Weighted (IDW) interpolation to understand the mean rainfall from the weather stations services. Thereafter, Standardized Precipitation Index (SPI) values were determined from the rainfall data in order to understand the severity of the droughts in certain parts of the study area from the weather station data. The meteorological analysis was cross compared with agricultural drought. The mean NDVI and mean rainfall interpolated shows that their relationship is inversely proportional, because where rainfall is low; NDVI is high for the years 2015 to 2017. The land use and land cover in the study is largely dominated by bush, cultivated cane crop, grassland and plantations. Looking at the overall classification in the year 2015, it is clear that bush land use and land cover was largely dominated in the study area, with other land use and land cover classes which were also part of the year 2015. During the year 2016 the other classes of land use and land cover where also dominating the study area for example grasslands and plantations. In the year 2017 we see cultivated cane crop start to emerge in the study area but land use and land cover is largely dominated by bush land use and land cover. The overall accuracy of the study was 74.2%. Keywords: Agricultural drought, Land use/land cover, Remote sensing, Landsat 8 OLI/TIRS, Normalized Difference Vegetation Index, Standardized Precipitation Index, Accuracy Assessment.LG201