Drought events and their effects on vegetation productivity in China

Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982–2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)

Trends in vegetation productivity and seasonality for Namaqualand, South Africa between 1986 and 2011: an approach combining remote sensing and repeat photography

This thesis presents an assessment of vegetation change and its drivers across a subset of Namaqualand, South Africa. Namaqualand forms part of the Succulent Karoo biome, which is characterised by exceptionally high species biodiversity but which has undergone severe transformation since the arrival of pastoral colonists. Vegetation productivity in Namaqualand is of great importance since there is a high dependence on natural resources, livestock and agriculture for both subsistence and income. However, there is considerable debate on the relative contribution of land-use change and climate change to vegetation change and land degradation in Namaqualand. Early studies based on bioclimatic envelop models suggest that an increase in temperature and more arid conditions could result in the vegetation cover of the Succulent Karoo being significantly reduced. On the other hand, more recent studies show that less extreme changes in rainfall could result in the vegetation of the biome remaining fairly stable with possible increases in the spatial extent by 2050. Furthermore, field observations and repeat photography, suggest that the change in vegetation in the region over the course of the 20th century generally portrays an increase in cover largely as a result of changes in land-use. By combining repeat photography and satellite data from NOAA-AVHRR and TERRA-MODIS sensors as well as baseline climatology data from the CRU TS 3.2 data set this study aimed to: (1) Determine the critical pathways of inter-annual and intra-seasonal vegetation change in the Namaqualand; (2) Investigate the role of land-use and climate variability as key drivers of vegetation change in Namaqualand

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

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

Multi-scale targeting of land degradation in northern Uzbekistan using satellite remote sensing

Advancing land degradation (LD) in the irrigated agro-ecosystems of Uzbekistan hinders sustainable development of this predominantly agricultural country. Until now, only sparse and out-of-date information on current land conditions of the irrigated cropland has been available. An improved understanding of this phenomenon as well as operational tools for LD monitoring is therefore a pre-requisite for multi-scale targeting of land rehabilitation practices and sustainable land management. This research aimed to enhance spatial knowledge on the cropland degradation in the irrigated agro-ecosystems in northern Uzbekistan to support policy interventions on land rehabilitation measures. At the regional level, the study combines linear trend analysis, spatial relational analysis, and logistic regression modeling to expose the LD trend and to analyze the causes. Time series of 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), summed over the growing seasons of 2000-2010, were used to determine areas with an apparent negative vegetation trend; this was interpreted as an indicator of LD. The assessment revealed a significant decline in cropland productivity across 23% (94,835 ha) of the arable area. The results of the logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table, land-use intensity, low soil quality, slope, and salinity of the groundwater. To quantify the extent of the cropland degradation at the local level, this research combines object-based change detection and spectral mixture analysis for vegetation cover decline mapping based on multitemporal Landsat TM images from 1998 and 2009. Spatial distribution of fields with decreased vegetation cover is mainly associated with abandoned cropland and land with inherently low-fertility soils located on the outreaches of the irrigation system and bordering natural sandy deserts. The comparison of the Landsat-based map with the LD trend map yielded an overall agreement of 93%. The proposed methodological approach is a useful supplement to the commonly applied trend analysis for detecting LD in cases when plot-specific data are needed but satellite time series of high spatial resolution are not available. To contribute to land rehabilitation options, a GIS-based multi-criteria decision-making approach is elaborated for assessing suitability of degraded irrigated cropland for establishing Elaeagnus angustifolia L. plantations while considering the specific environmental setting of the irrigated agro-ecosystems. The approach utilizes expert knowledge, fuzzy logic, and weighted linear combination to produce a suitability map for the degraded irrigated land. The results reveal that degraded cropland has higher than average suitability potential for afforestation with E. angustifolia. The assessment allows improved understanding of the spatial variability of suitability of degraded irrigated cropland for E. angustifolia and, subsequently, for better-informed spatial planning decisions on land restoration. The results of this research can serve as decision-making support for agricultural planners and policy makers, and can also be used for operational monitoring of cropland degradation in irrigated lowlands in northern Uzbekistan. The elaborated approach can also serve as a basis for LD assessments in similar irrigated agro-ecosystems in Central Asia and elsewhere.Multisclare Bewertung der Landdegradation in Nord-Uzbekistan unter der Verwendung von Satellitenfernerkundung Die zunehmende Landdegradation (LD) in den bewässerten Agrarökosystemen in Usbekistan behindert die nachhaltige Entwicklung dieses vorwiegend landwirtschaftlich geprägten Landes. Bis heute sind nur wenige und veraltete Informationen über die aktuellen Bodenbedingungen der bewässerten Anbauflächen verfügbar. Ein besseres Verständnis dieses Phänomens sowie operationelle Werkzeuge für LD-Monitoring sind daher Voraussetzung für ein nachhaltiges Landmanagement sowie für Landrehabilitationsmaßnahmen. Ziel dieser Studie war es, das räumliche Verständnis der Degradierung von Anbaugebieten in den bewässerten Agrarökosystemsn des nördlichen Usbekistans zu verbessern, um staatliche Interventionen in Bezug auf Landrehabilitationsmaßnahmen zu unterstützen Auf der regionalen Ebene kombiniert die Studie lineare Trendanalyse, räumliche relationale Analyse sowie logistischer Regressionsmodellierung, um den LD-Trend darzustellen und Gründe zu analysieren. Zeitreihen von 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) Bildern wurden für den Zeitraum der Anbauperioden zwischen 2000-2010 untersucht, um Bereiche mit einem offensichtlich negativen Vegetationstrend zu ermitteln. Dieser negative Trend kann als Indikator für LD interpretiert werden. Die Untersuchung ergab eine signifikante Abnahme der Bodenproduktivität auf 23% (94,835 ha) der Anbaufläche. Zudem deuten die Ergebnisse der logistischen Modellierung darauf hin, dass das räumliche Muster des beobachteten Trends überwiegend mit der Höhe des Grundwasserspiegels, der Landnutzungsintensität, der geringen Bodenqualität, der Hangneigung sowie der Grundwasserversalzung zusammenhängt. Um das Ausmaß der Degradation der Anbauflächen auf der lokalen Ebene zu quantifizieren, kombiniert diese Studie objektbasierte Erkennung von Veränderungen und spektrale Mischungsanalyse für die Abnahme der Vegetationsbedeckung auf der Grundlage von multitemporalen Landsat-TM-Bildern im Zeitraum von 1998 bis 2009. Die räumliche Verteilung der Felder mit abnehmender Vegetationsbedeckung hängt überwiegend mit verlassenen Anbauflächen sowie mit nährstoffarmen Böden in den Randbereichen des Bewässerungssystems und an den Grenzen zu natürlichen Sandwüsten zusammen. Ein Vergleich mit der Karte des LD-Trends ergab insgesamt eine Übereinstimmung von 93%. Der vorgeschlagene Ansatz ist eine nützliche Ergänzung zu der häufig angewendeten Trendanalyse für die Ermittlung von LD in Regionen, für die keine Satellitenbildzeitreihen mit hoher Auflösung verfügbar sind. Als Beitrag zu Landrehabilitationsmöglichkeiten, wird ein GIS-basierter Multi-Kriterien-Ansatz zur Einschätzung der Eignung von degradierten bewässerten Anbauflächen für Elaeagnus angustifolia L. Plantagen beschrieben, der gleichzeitig die spezifischen Umweltbedingungen der bewässerten Agrarökosysteme berücksichtigt. Dieser Ansatz beinhaltet Expertenwissen, Fuzzy-Logik und gewichtete lineare Kombination, um eine Eignungskarte für die bewässerten degradierten Anbauflächen herzustellen. Die Ergebnisse zeigen, dass diese Flächen ein überdurchschnittliches Eignungspotenzial für die Aufforstung mit E. angustifolia aufweisen. Diese Studie trägt zu einem verbesserten Verständnis der räumlichen Variabilität der Eignung von solchen Flächen für E. angustifolia bei. Die Ergebnisse dieser Studie können als Entscheidungshilfe für landwirtschaftliche Planer und politische Entscheidungsträger sowie für verbesserte Landrehabilitationsmaßnahmen und operationelles Monitoring der Degradation von Anbauflächen im nördlichen Usbekistan eingesetzt werden. Zudem kann der beschriebene Ansatz als Grundlage für LD-Untersuchungen in ähnlichen bewässerten Agrarökosystemen in Zentralasien und anderswo dienen

Climate, land use and vegetation trends: Implication of land use change and climate change on northwestern drylands of Ethiopia

Land use / land cover (LULC) change assessment is getting more consideration by global environmental change studies as land use change is exposing dryland environments for transitions and higher rates of resource depletion. The semiarid regions of northwestern Ethiopia are not different as land use transition is the major problem of the region. However, there is no satisfactory study to quantify the change process of the region up to now. Hence, spatiotemporal change analysis is vital for understanding and identification of major threats and solicit solutions for sustainable management of the ecosystem. LULC change studies focus on understanding the patterns, processes and dynamics of land use transitions and driving forces of change. The change processes in dryland ecosystems can be either seasonal, gradual or abrupt changes of random or systematic change processes that result in a pattern or permanent transition in land use. Identification of these processes of change and their type supports adoption of monitoring options and indicate possible measures to be taken to safeguard this dynamic ecosystem. This study examines the spatiotemporal patterns of LULC change, temporal trends in climate variables and the insights of the communities on change patterns of ecosystems. Landsat imagery, MODIS NDVI, CRU temperature, TAMSAT rainfall and socio-ecological field data were used in order to identify change processes. LULC transformation was monitored using support vector machine (SVM) algorithm. A cross-tabulation matrix assessment was implemented in order to assess the total change of land use categories based on net change and swap change. In addition, the pattern of change was identified based on expected gain and loss under a random process of gain and loss, respectively. Breaks For Additive Seasonal and Trend (BFAST) analysis was employed for determining the time, direction and magnitude of seasonal, abrupt and trend changes within the time series datasets. In addition, Man Kendall test statistic and Sen’s slope estimator were used for assessing long term trends on detrended time series data components. Distributed lag (DL) model was also adopted in order to determine the time lag response of vegetation to the current and past rainfall distribution. Over the study period of 1972- 2014, there is a significant change in LULC as evidenced by a significant increase in size of cropland of about 53% and a net loss of over 61% of woodland area. The period 2000-2014 has shown a sharp increase of cropland and a sharp decline of woodland areas. Proximate causes include agricultural expansion and excessive wood harvesting; and underlying causes of demographic factor, economic factors and policy contributed the most to an overuse of existing natural resources. In both the observed and expected proportion of random process of change and of systematic changes, woodland has shown the highest loss compared to other land use types. The observed transition and expected transition under random process of gain of woodland to cropland is 1.7%, implies that cropland systematically gains to replace woodland. The comparison of the difference between observed and expected loss under random process of loss also showed that when woodland loses cropland systematically replaces it. The assessment of magnitude and time of breakpoints on climate data and NDVI showed different results. Accordingly, NDVI analysis demonstrated the existence of breakpoints that are statistically significant on the seasonal and long term trends. There is a positive trend, but no breakpoints on the long term precipitation data during the study period. The maximum temperature also showed a positive trend with two breakpoints which are not statistically significant. On the other hand, there is no seasonal and trend breakpoints in minimum temperature, though there is an overall positive trend along the study period. The Man-Kendall test statistic for long term average Tmin and Tmax showed significant variation where as there is no significant trend within the long term rainfall distribution. The lag regression between NDVI and precipitation indicated a lag of up to forty days. This proves that the vegetation growth in this area is not primarily determined by the current precipitation rather with the previous forty days rainfall. The combined analysis showed declining vegetation productivity and a loss of vegetation cover that contributed for an easy movement of dust clouds during the dry period of the year. This affects the land condition of the region, resulting in long term degradation of the environmen

Satellite Remote Sensing of Woody and Herbaceous Leaf Area for Improved Understanding of Forage Resources and Fire in Africa

In sub-Saharan Africa (SSA) tree-grass systems commonly referred to as savannas dominating drylands, play a critical role in social, cultural, economic and environmental systems. These coupled natural-human systems support millions of people through pastoralism, are important global biodiversity hotspots and play a critical role in global biogeochemical cycles. Despite the importance of SSA savannas, they have been marginalized for years as most governments neglect dryland resources in favor of agricultural research and development assistance. Hence, lack of spatially and temporally accurate information on the status and trends in savanna resources has led to poor planning and management. This scenario calls for research to derive information that can be used to guide development, management and conservation of savannas for enhanced human wellbeing, livestock productivity and wildlife management. The above considerations motivated a more detailed study of the composition, temporal and spatial variability of savannas, comprising of three components. Remote sensing data was combined with field and literature data to: partition Moderate Resolution Imaging Spectroradiometer (MODIS) total leaf area index (LAIA) time series into its woody (LAIW) and herbaceous (LAIH) constituents for SSA; and application of the partitioned LAI to determine how changes in herbaceous and woody LAI, affect fire regimes and livestock herbivory in SSA. The results of this analysis include presentation of algorithm for partitioning of MODIS LAIA from 2003-2015. Biome phenologies, seasonality and distribution of woody and herbaceous LAI are presented and the long-term average 8-day phenologies availed for evaluation and research application. In determining how changes in herbaceous and woody LAI affect fire regimes in SSA, we found that herbaceous fuelload (indexed as LAIH) correlated more closely with fire, than with LAIW, providing more explanatory power than overall biomass in fire activity. We observed an asymptotic relationship between herbaceous fuel-load and fire with trees promoting fires in dry ecosystems but suppressing fires in wetter regions. In the livestock herbivory analysis we found that the more refined forage indices (LAIH and LAIW) explained more of the variability in livestock distribution than the aggregate biomass, with livestock favoring moderate to nutrient rich forage resources dependent on animal body size

Variability of African Farming Systems from Phenological Analysis of NDVI Time Series

Food security exists when people have access to sufficient, safe and nutritious food at all times to meet their dietary needs. The natural resource base is one of the many factors affecting food security. Its variability and decline creates problems for local food production. In this study we characterize for sub-Saharan Africa vegetation phenology and assess variability and trends of phenological indicators based on NDVI time series from 1982 to 2006. We focus on cumulated NDVI over the season (cumNDVI) which is a proxy for net primary productivity. Results are aggregated at the level of major farming systems, while determining also spatial variability within farming systems. High temporal variability of cumNDVI occurs in semiarid and subhumid regions. The results show a large area of positive cumNDVI trends between Senegal and South Sudan. These correspond to positive CRU rainfall trends found and relate to recovery after the 1980's droughts. We find significant negative cumNDVI trends near the south-coast of West Africa (Guinea coast) and in Tanzania. For each farming system, causes of change and variability are discussed based on available literature (Appendix A). Although food security comprises more than the local natural resource base, our results can perform an input for food security analysis by identifying zones of high variability or downward trends. Farming systems are found to be a useful level of analysis. Diversity and trends found within farming system boundaries underline that farming systems are dynamic

Precipitation and Greenness in Pastoral Lands of East Turkana, Kenya

Pastoralism has long supported livelihoods and provided essential ecosystem services in landscapes of East Africa. Vegetation productivity is central to the functioning of pastoral systems but may be affected by changes in climate and landuse. Vegetation monitoring is important for understanding the effects of global change in pastoral lands; however, it can be time and resource intensive. Remote sensing provides opportunities for efficient multi-scale monitoring of vegetation and climatic drivers. In this thesis, I explore the utility of satellite and UAV remote sensing for monitoring vegetation and precipitation trends and relationships in the East of Lake Turkana Region of northern Kenya. In Chapter 1, I examine regional greenness and precipitation time series at monthly, seasonal, and annual temporal resolutions, as well as relationships between greenness and precipitation from 2000 to 2022. I found evidence of long-term precipitation–greenness coupling at monthly and annual temporal resolutions. There were no trends in monthly or annual regional precipitation, while NDVI significantly increased at monthly temporal resolution but did not exhibit a significant trend at annual temporal resolution. Traditional pastoral practices, such as use of livestock corrals (bomas), also influence local vegetation composition and abundance. In chapter two, I use satellite and unmanned aerial vehicle (UAV) remote sensing data to monitor greenness in and around abandoned boma settlements at seasonal and annual temporal resolutions. Results showed that mean NDVI from UAV and Sentinel-2 data varied based on seasons (dry or wet) and from boma to boma. NDVI significantly differed between bomas and non-boma sites and there was significant positive correlation between NDVI with precipitation across all bomas, with an optimum temporal lag response of one month. Collectively, my results add to the body of literature demonstrating the utility of satellite and UAV-based remote sensing data for monitoring vegetation in pastoral systems. Advisor: Daniel R. Ude