A statistical approach for predicting grassland degradation in disturbance-driven landscapes

Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non -military sites in the Kansas Flint Hills. The response variable was the longterm linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non -military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the nonmilitary site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage

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

Understanding the spatial temporal vegetation dynamics in Rwanda

Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country's vegetation has improved throughout the study period, while 14.1% of the country's vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The mosaic grassland/forest or shrubland was severely degraded, followed by sparse vegetation, grassland or woody vegetation regularly flooded on water logged soil, artificial surfaces and broadleaved forest regularly flooded. The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration

Vegetation dynamics and precipitation sensitivity in three regions of northern Pantanal of Mato Grosso

The wet areas of the Pantanal provide important services such as water and carbon storage, improved water quality, and climate regulation. Analysis and monitoring of vegetated land and precipitation on a regional scale using remote sensing data can provide important information for the preservation of the landscape and biodiversity of the region. Thus, the purpose was to analyze characteristics of the green cycle of the vegetated surface and to what extent the vegetated surface responds to the variability of precipitation in the Pantanal. The areas include the regions of Cáceres (CAC), Poconé (POC), and Barão de Melgaço (BAM) in Mato Grosso. Time series of accumulated precipitation (PPT) and NDVI (Normalized Difference Vegetation Index) were used for the period from 2000 to 2016, obtained on NASA’s Giovanni platform (National Aeronautics and Space Administration). The analysis of the wavelet transform was applied for NDVI data and there was cross-correlation analysis for PPT and NDVI data. The results showed that the highest correlation between PPT and NDVI was positive with a 1-month lag, but was significant with a lag of up to 3 months. The wavelet analyses showed that the largest wavelet powers occurred at the frequency between 0.5 and 1.3 years, i.e., the NDVI series presented the main variances on the approximately annual scale, indicating that these characteristics are important aspects of local phenology variability, such as cumulative green throughout the year and generalized senescence.As áreas úmidas do Pantanal fornecem importantes serviços, como armazenamento de água e carbono, melhoria da qualidade da água e regulação do clima. A análise e o monitoramento da superfície vegetada e da precipitação em escala regional, com uso de dados de sensoriamento remoto, podem oferecer informações importantes para a preservação da paisagem e da biodiversidade da região. Assim, o objetivo deste estudo foi analisar características do ciclo do verde da superfície vegetada e em que medida a superfície vegetada responde pela variabilidade da precipitação no Pantanal. As áreas analisadas compreendem as regiões de Cáceres (CAC), Poconé (POC) e Barão de Melgaço (BAM), em Mato Grosso. Foram usadas séries temporais de precipitação acumulada (PPT) e índice de vegetação Normalized Difference Vegetation Index (NDVI) para o período de 2000 a 2016, obtidos na plataforma Giovanni da National Aeronautics and Space Administration (NASA). Foram aplicadas a análise da transformada wavelet para os dados de NDVI e a análise de correlação cruzada para os dados de PPT e NDVI. Os resultados mostraram que a maior correlação entre a PPT e o NDVI foi positiva com defasagem de um mês, mas foi significativa em até uma defasagem de três meses. As análises wavelet mostraram que as maiores potências ocorreram na periodicidade entre 0,5 e 1,3 anos, isto é, as séries de NDVI apresentaram as principais variâncias na escala aproximadamente anual, indicando que essas características são aspectos importantes da variabilidade da fenologia local, como o verde cumulativo ao longo do ano e a senescência generalizada

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

Refining historical burned area data from satellite observations

• The burned area reported by global satellite products is largely biased. •We aimed to correct burned area biases before Sentinel-2 era. •A solution is to combine coarse resolution burned area with environmental data. •Validations in independent sites and years demonstrate that our tool is operational. Sentinel-2 imagery has revealed a substantial underestimation of burned area (BA) compared with earlier satellite products with coarser spatial resolution. In this context, we investigate the predictability of biases between the reference Sentinel-2 BA product developed for Sub-Saharan Africa (FireCCISFD) in 2019 and commonly used global coarse resolution BA products (MCD64, Fire CCI and C3S), providing tools to refine historical annual BA data before the Sentinel-2 era. To do so, we built a comprehensive dataset of environmental predictors of BA biases, with variables or proxies of (I) the annual BA estimated from the coarse-resolution product, (II) BA sizes, (III) the persistence and strength of BA signals, (IV) the maximum potential BA, and (V) the obstruction of land surface observation from satellites. Full and parsimonious random forest models were performed and validated through out-of-bag (OOB) estimations, and reconstructed BAs were validated with external data over space, and over time. The explained variance in BA biases was ≥78.58% (OOB) for all full and parsimonious models. The reconstructed BA data showed a high correspondence with the reference BA in the validation sites over space (≥91.15% var. explained) and time (≥90.37% var. explained), notably reducing biases of coarse resolution products. As an example of the model applicability, the spatial patterns of Madagascar’s BA were reconstructed for 2005, 2010, 2015 and 2020, revealing a burned extent between two and four times higher than previous estimations. The proposed models are operational solutions to obtain regional and global virtually unbiased BA estimates since 2000

Estimation of herbaceous fuel moisture content using vegetation indices and land surface temperature from MODIS data

The monitoring of herbaceous fuel moisture content is a crucial activity in order to assess savanna fire risks. Faced with the difficulty of managing wide areas of vegetated surfaces, remote sensing appears an attractive alternative for terrestrial measurements because of its advantages related to temporal resolution and spatial coverage. Earth observation (EO)-based vegetation indices (VIs) and the ratio between Normalized Difference Vegetation Index (NDVI) and surface temperature (ST) were used for assessment of herbaceous fuel moisture content estimates and validated against herbaceous data collected in 2010 at three open savanna sites located in Senegal, West Africa. EO-based estimates of water content were more consistent with the use of VI as compared to the ratio NDVI/ST. Different VIs based on near-infrared (NIR) and shortwave infrared (SWIR) reflectance were tested and a consistent relationship was found between field measurements of leaf equivalent water thickness (EWT) from all test sites and Normalized Difference Infrared Index (NDII), Global Vegetation Moisture Index (GVMI) and Moisture Stress Index (MSI). Also, strong relationships were found between fuel moisture content (FMC) and VIs for the sites separately; however, they were weaker for the pooled data. The correlations between EWT/FMC and VIs were found to decrease progressively as the woody cover increased. Although these results suggest that NIR and SWIR reflectance can be used for the estimation of herbaceous water content, additional validation from an increased number of study sites is necessary to study the robustness of such indices for a larger variety of savanna vegetation types

The Impact of Sustained Drought on Vegetation Ecosystem in Southwest China Based on Remote Sensing

AbstractSouthwest China is an important ecological shelter and ecologically vulnerable area. Since last winter and this spring, southwest china have suffered from sustained drought that rarely happened in the same season of past years, severely threatening the health of vegetation ecosystem. Annually contemporaneous difference of NDVI is used as an evaluation indicator in this analysis, in which vegetations are monitored and analyzed in a macro-scale. The results indicate that from August 2009 through March 2010: 1) vegetation in southwest China was remarkably impacted by sustained drought, leading to the ascendant trend of threatening degree. 2) the area of vegetation ecosystem that suffered from this disaster in Yunnan, Guangxi and Guizhou accounts for more than 80% of the total area of the vegetation ecosystem in these three administrative regions. 3) farmland vegetation was seriously damaged, resulting in large areas of crops dying off and failing and reservoirs and ponds drying up; 4) The effect on natural vegetation was obvious and the growth was apparently suppressed. Large areas of vegetations in dry-hot valley and Karst area degenerated, threatening the local biodiversity. Verification showed that study result is consistent with the result of practical monitoring, indicating that annually contemporaneous difference of NDVI responds strongly to the spatial and temporal sustained drought, which could precisely represent the occurrence and progress of drought and detailed spatial distribution

Dynamique de la végétation des savanes en lien avec l’usage des feux à Madagascar. Analyse par série temporelle d’images de télédétection

Bien que le feu soit reconnu comme un facteur d’influence dans la dynamique de végétation des savanes, son rôle n’est pas clairement défini. Cette thèse aborde le problème de l’étude de la relation entre l’usage des feux et la dynamique de végétation. L’approche choisie repose sur l’analyse de séries temporelles d’images de télédétection à moyenne résolution spatiale. Les savanes étudiées sont situées sur le bassin versant de Marovoay au nord-ouest de Madagascar. Dans la mesure où il n’existe pas de consensus quant aux méthodes à utiliser, les savanes de Madagascar offrent un contexte particulier, en raison de la dégradation très prononcée du couvert végétal et des changements recherchés, pour tester les méthodes existantes et en proposer des nouvelles. Le premier objectif de ce travail est d’identifier le régime des feux à travers le suivi des variations spatio-temporelles des surfaces brûlées en milieu de savane. Pour cela, une méthode de cartographie des surfaces brûlées a été développée : elle est basée sur le calcul d’un indicateur annuel indiquant le passage d’un feu pendant la saison sèche et d’un indicateur saisonnier traduisant la période de passage du feu. Cette méthode, appliquée au site d’étude, a permis de produire une série temporelle de données utilisées pour caractériser le régime des feux à partir de deux paramètres, la période d’occurrence et la fréquence de passage du feu. En parallèle, le deuxième objectif consiste à caractériser la dynamique de végétation par l’analyse des variations spatio-temporelles de l’activité végétale. Deux approches de détection des changements, basées sur le traitement de série temporelle de NDVI, ont été testées. La première repose sur l’analyse des variations inter annuelles d’un indicateur phénologique traduisant l’activité végétale pendant la phase de croissance des savanes. La deuxième utilise une technique de décomposition temporelle pour extraire la tendance d’une série de NDVI. Dans les deux cas, les résultats ont permis de caractériser la dynamique de végétation à travers trois classes d’évolution de l’activité végétale (séries progressive, régressive ou stable). Ces résultats ont été évalués par comparaison avec ceux issus de techniques de détection des changements basées sur l’analyse diachronique d’images à haute résolution spatiale. Enfin, dans la dernière étape du travail, nous avons étudié les relations entre les informations relatives aux régimes des feux et à la dynamique de végétation en utilisant des modèles de régression multivariée. L’objectif est d’estimer l’importance et le rôle du feu dans la dynamique de végétation. Les résultats ont amené à trois conclusions : a) Le feu est un facteur de maintien des savanes ; b) Dans les situations où la pression liée aux activités anthropiques est faible, le feu, en particulier par la fréquence de son usage, est un facteur déterminant de la dynamique de végétation ; c) Dans les autres situations, l’interprétation des résultats est complexe et difficile, très certainement en raison de l’interaction de multiples facteurs anthropiques. ABSTRACT : Fire is recognized to be an essential factor that explains savanna vegetation dynamics. But its role is not clearly defined. This work investigates the problem of studying the relation between fire usage and vegetation dynamic. This is addressed through the analysis of time series of medium spatial resolution remotely sensed images. We studied the savanna localized on the Marovoay watershed, on the northwest part of Madagascar. As no consensus exists on the adapted methods, the savanna of Madagascar offers a particular context to test existing methods or develop new ones, because of the advanced vegetation cover degradation and the nature of change to be detected. The first objective of this work is to identify fire regime by analyzing the spatio-temporal variations of burned areas in savanna areas. To this end, a burned area mapping method was developed. It is based on the definition of an annual indicator, which indicates the occurrence of a fire during the dry season, and a seasonal indicator, which gives the information on the date of the fire event. Applied to the study site, this method has lead to the production of a time series of data used for the characterization of the fire regime through two parameters, the period of occurrence and the frequency of fire. In parallel, the second objective consists on characterizing vegetation dynamic by monitoring spatiotemporal variations of vegetation activity. Two change detection approaches based on NDVI time series, were tested. The first consists on analyzing the inter annual variations of a phenological indicator related to vegetation activity during the active growth season. The second uses a temporal decomposition technique to extract the trend from an NDVI time series. In both cases, the vegetation dynamic is characterized through three classes linked to the evolution of the vegetation activity (progressive, regressive or stable series). Results were evaluated by a comparison with the results obtained from a diachronic change detection technique based of high spatial resolution images. Finally, in the last part of this work, we investigated the relation between fire regimes and vegetation activity change classes using multivariate regression models. The objective is to analyze to determine the importance and the role of fire into the vegetation dynamic. Results leaded to three conclusions: a) Fire is a factor that maintains savanna; b) In areas where pressure due to anthropogenic activities is low, fire frequency represents a determinant factor to explain vegetation dynamic; c) In others areas, the interpretation of results appears to be complex and difficult, probably because of the high level of interactions between multiple environmental factors

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