Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola

Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes. We compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources. View Full-Tex

An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa

The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019

Using time series analysis to monitor deforestation dynamics in Miombo woodlands in Southern Highlands of Tanzania

Deforestation and forest fragmentation are threatening the Miombo woodlands in Southern Highlands of Tanzania. Miombo ecoregion is considered one of the world’s most valuable wilderness areas, providing livelihood for over 150 million people throughout the region, who are directly or indirectly depending on these ecosystem services. Monitoring deforestation process using satellite images enables the identification of ongoing changes and pressures facing the region, which is crucial for the sustainable management of the area. In this thesis the deforestation dynamics are analysed using LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) -time series algorithm. The algorithm uses temporal segmentation of spectral trajectories to extract change information from pixel time series derived from satellite images. The study is focused in miombo woodlands around a rural village of Mantadi, which is located in Tanzanian Southern Highlands. The capacity of LandTrendr algorithm to detect changes in Miombo woodland region is evaluated through the appliance of three spectral indices. The results are combined to examine the magnitude and spatial distribution of deforestation in the study area. The results show that to detect areas under any kind of disturbance, LandTrendr performs considerably well with all three indices. In more profound change magnitude detection, clear differences between the spectral indices can be noticed especially in finding subtler, low magnitude changes. The Normalized Burn Ratio (NBR) was found to be most stable index to detect changes in miombo woodlands. Combining the results from spectral indices increased the mapping accuracy by 10 %. The results indicate that 26,5 % of the whole study area has been under very high or high magnitude disturbance and 29,5 % under low or moderate magnitude disturbance between 1987 and 2018. This study proves that the LandTrendr algorithm is suitable for tracking long-term deforestation dynamics in Miombo woodland environments.Metsäkato ja metsien pirstoutuminen uhkaavat Tansanian eteläisten ylänköalueiden miombo-savanneja. Miombo-savannit muodostavat yhden maailman tärkeimmistä erämaa-alueista, tarjoten toimeentulon yli 150 miljoonalle ihmiselle, jotka ovat tavalla tai toisella riippuvaisia alueen ekosysteemipalveluista. Metsäkatoprosessien seuranta satelliittikuvien avulla mahdollistaa tapahtuvien muutosten ja paineiden tunnistamisen, mikä on elintärkeää alueen kestävälle hallinnalle. Tässä opinnäytetyössä metsien häviämisen dynamiikkaa analysoidaan LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) aikasarja-algoritmin avulla. Algoritmi hyödyntää spektraalisen kulkuradan ajallista segmentointia erottaakseen muutostiedot satelliittikuvien pikselikohtaisista aikasarjoista. Tutkimus kohdistuu miombo-metsäalueisiin Tansanian eteläisillä ylänköalueilla sijaitsevan Mantadi-kylän ympärillä. LandTrendr-algoritmin kykyä havaita muutoksia miombo-savanneilla arvioidaan kolmen spektraalisen indeksin avulla. Lopulta indeksien tulokset yhdistetään, jotta metsäkadon laajuutta ja alueellista jakautumista tutkimusalueella voitaisiin tutkia entistä tarkemmin. Tulokset osoittavat, että LandTrendr havaitsee metsissä tapahtuneet muutokset merkittävän hyvin kaikilla kolmella indeksillä. Perusteellisemmassa muutoksen voimakkuuden tarkastelussa havaitaan selviä eroja eri indeksien välillä, etenkin hienovaraisempien muutosten tunnistamisessa. Yksittäisistä indekseistä NBR (The Normalized Burn Ratio) osoittautui kaikkein vakaimmaksi miombo-savanneilla tapahtuvien muutosten tunnistamisessa. Kolmen spektraalisen indeksin tulosten yhdistäminen lisäsi kartoitustarkkuutta 10 %. Tulokset osoittavat, että 26,5 % tutkimusalueen metsistä on hävinnyt tai heikentynyt voimakkaasti ja 29,5 % on kokenut pieniä tai kohtalaisia häiriöitä vuosien 1987-2018 välillä. Tutkielma osoittaa, että LandTrendr algoritmin avulla metsäkatoa voidaan kartoittaa tehokkaasti Miombo-metsäympäristöissä pitkällä aikavälillä

Spatial and temporal trends of burnt area in Angola: implications for natural vegetation and protected area management

Fire is a key driver of natural ecosystems in Africa. However, human activity and climate change have altered fire frequency and severity, with negative consequences for biodiversity conservation. Angola ranks among the countries with the highest fire activity in sub-Saharan Africa. In this study, we investigated the spatial and temporal trends of the annual burnt area in Angola, from 2001 to 2019, and their association with terrestrial ecoregions, land cover, and protected areas. Based on satellite imagery, we analyzed the presence of significant trends in burnt area, applying the contextual Mann–Kendall test and the Theil–Sen slope estimator. Data on burnt areas were obtained from the moderate-resolution imaging spectroradiometer (MODIS) burnt area product and the analyses were processed in TerrSet. Our results showed that ca. 30% of the country’s area burned every year. The highest percentage of annual burnt area was found in northeast and southeast Angola, which showed large clusters of decreasing trends of burnt area. The clusters of increasing trends were found mainly in central Angola, associated with savannas and grasslands of Angolan Miombo woodlands. The protected areas of Cameia, Luengue-Luiana, and Mavinga exhibited large areas of decreasing trends of burnt area. Conversely, 23% of the Bicuar National Park was included in clusters of increasing trends. Distinct patterns of land cover were found in areas of significant trends, where the clusters of increasing trends showed a higher fraction of forest cover (80%) than the clusters of decreasing trends (55%). The documentation of burnt area trends was very important in tropical regions, since it helped define conservation priorities and management strategies, allowing more effective management of forests and fires in countries with few human and financial resourcesinfo:eu-repo/semantics/publishedVersio

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

Agents of Forest Disturbance in the Argentine Dry Chaco

Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km2 Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km2 (±407 km2), followed by fire (4562 ± 388 km2) and logging (3891 ± 341 km2). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed.Peer Reviewe

Mapping decadal land cover changes in the woodlands of north eastern Namibia from 1975 to 2014 using the Landsat satellite archived data

Woodlands and savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to subsistence and intensive agriculture or urbanized. This study investigates changes in land cover over four administrative regions of North Eastern Namibia within the Kalahari woodland savannah biome, covering a total of 107,994 km2. Land cover is mapped using multi-sensor Landsat imagery at decadal intervals from 1975 to 2014, with a post-classification change detection method. The dominant change observed was a reduction in the area of woodland savannah due to the expansion of agriculture, primarily in the form of small-scale cereal and pastoral production. More specifically, woodland savannah area decreased from 90% of the study area in 1975 to 83% in 2004, and then increased to 86% in 2014, while agricultural land increased from 6% to 12% between 1975 and 2014. We assess land cover changes in relation to towns, villages, rivers and roads and find most changes occurred in proximity to these. In addition, we find that most land cover changes occur within land designated as communally held, followed by state protected land. With widespread changes occurring across the African continent, this study provides important data for understanding drivers of change in the region and their impacts on the distribution of woodland savannahs

Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns

Monitoring ecosystem dynamics in semi-arid environments using multi-sensor Earth-observation

Climate change and a growing human population are instigating major changes on the Earth’s surface. Monitoring and understanding these changes as they unfold is critical for society and the environment. Satellite remote sensing provides the only means of achieving this over large spatial and temporal scales, and major progress in the application of Earth-observation imagery has been made since the beginning of the space age in the mid-20th century. However, savannahs - dynamic systems comprised of shrubs, trees, and grass species - have proved challenging for EO-based monitoring. Yet, these ecosystems cover almost 25% of the Earth’s surface and are home to some of the poorest people on the planet. This thesis investigates the use of EO for monitoring ecosystem dynamics in African savannahs, focusing specially on woody cover and biomass provision. One of the most common Earth-observation (EO) based tools for monitoring vegetation is the Normalised Difference Vegetation Index (NDVI). A detailed review of the application of NDVI for monitoring land degradation was undertaken. This covered the historical context and ongoing debates around NDVI analyses, and highlighted key research gaps. NDVI was then used to map grass biomass for the Kruger National Park in South Africa, by combining in situ data with a downscaled NDVI dataset in a machine-learning framework. These predictions highlighted that the NDVI-biomass relationship is vulnerable to overfi�tting in space and time, due to spatial autocorrelation and a variable species composition, respectively. The NDVI was further explored at the continental scale using multiple time-series analyses. These revealed that a majority of African savannahs have only experienced vegetation greening in the 1982-2016 period. Areas of declining vegetation, or changes in the trend direction, were associated with phenological changes (i.e. a shrinking growth season), woodland degradation, or population increases. Finally, fractional woody vegetation cover was mapped for the Limpopo province of South Africa using Landsat spectral metrics and ALOS PALSAR radar imagery and a series of Random Forest regression models. The most accurate models combined multi-seasonal Landsat data and the radar layers. However, this was only marginally more accurate than just using dry and wet season metrics alone. When using a single season of imagery, the dry season preformed best. These results were reaffirmed for categorical savannah land-cover classifications, highlighting the importance of multi-sensor and multi-temporal data. The thesis contributes new insights for monitoring savannahs using EO imagery. By combining EO data with modern statistics and machine-learning methods novel insights to ecological and environmental issues can be gained. In the coming years, the increasing number of operational sensors and the volume of data collected will be of great benefit for environmental monitoring, especially in savannahs

Linking agents, patterns and outcomes of forest disturbances to understand pathways of degradation in the Argentine Dry Chaco

Tropische Trockenwälder sind von großer Bedeutung für das Klima, die biologische Vielfalt und den Lebensunterhalt von Millionen von Menschen. Die Walddegradation bedroht die tropischen Trockenwälder, aber es fehlt an Wissen über ihre Muster, ihr Ausmaß und ihre Ursachen. Ziel dieser Arbeit war es, das derzeitige Verständnis der Walddegradation im argentinischen Dry Chaco mit Hilfe der Fernerkundung zu verbessern. Mithilfe des Landsat-Archivs habe ich die Störungsgeschichte des verbleibenden Waldes charakterisiert, die räumlichen und zeitlichen Muster der Störungsfaktoren bewertet und die langfristigen Auswirkungen der verschiedenen Faktoren auf die Waldstruktur untersucht. Die Ergebnisse zeigen, dass über 30 Jahre hinweg große Gebiete des argentinischen Dry Chaco (etwa 8 %) von Störungen betroffen waren. Meine Ergebnisse zeigen einen anthropogenen Zusammenhang mit den meisten Störungsarten, deuten aber auch auf einen komplexen indirekten Einfluss von Niederschlagsmustern hin, wobei Waldstörungen in Dürrejahren besonders verbreitet sind. Die Analyse der zeitlichen Muster der verschiedenen Einwirkungen zeigt Trends in der Landnutzung im Laufe der Zeit, wobei neue Landnutzungsformen wie silvopastorale Systeme entstehen und alte Praktiken wie die Abholzung jedes Jahr einen relativ stabilen Anteil der Flächen betreffen. Die Ergebnisse zu den langfristigen Auswirkungen von Störungen zeigen, dass sich die Waldstruktur bei den am weitesten verbreiteten Störungen über drei Jahrzehnte kaum oder gar nicht erholt, was auf eine großflächige Walddegradation schließen lässt. Diese Arbeit zeigt das Potenzial von Satellitenzeitreihen für eine robuste Charakterisierung der Walddynamik im Zusammenhang mit der Degradation auch in tropischen Trockenwäldern. Die aus dieser Arbeit resultierenden Karten, Ansätze und Erkenntnisse tragen zu einem besseren Verständnis der Walddegradation im Dry Chaco bei und können zu einem wirksameren Schutz der tropischen Trockenwälder beitragen.Tropical dry forests are of great importance for climate regulation, harbour biodiversity and sustain the livelihood of millions of people. Deforestation and degradation threaten tropical dry forests but whereas our understanding of tropical deforestation has increased tremendously over the last decades, knowledge of the patterns, extent and drivers of forest degradation is lacking. This thesis aimed to advance the current understanding of forest degradation in the Dry Chaco by means of remote sensing. Using the Landsat archive, I characterized the disturbance history of the remaining Argentine Dry Chaco forest, assessed spatial and temporal patterns of disturbance agents, and investigated the long-term effect of different agents on forest structure. Results show that over 30 years large areas of the Argentine Dry Chaco (about 8%) were affected by disturbances. My findings reveal an anthropogenic link to most types of disturbances, while also suggesting complex indirect influence of precipitation patterns, with forest disturbances being particularly widespread during drought years. The analyses of temporal patterns of different agents reveals trends in land-use practices over time, with new land uses emerging, such as silvopastoral systems, and old practices such as logging, affecting a fairly stable share of areas every year. Findings on the long-term impact of disturbances indicate that for the most widespread disturbances, forest structure shows little or no recovery over three decades, which suggests forest degradation affecting large areas. This thesis demonstrates the potential of satellite time series for robust characterization of forest dynamics related to degradation also in tropical dry forests, despite the complex conditions these systems represent. The maps, approaches and knowledge resulting from this thesis contribute to a better understanding of forest degradation in the Dry Chaco and can inform more effective conservation of tropical dry forests