Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)- vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001–2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982–2010) data found that for MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas

Transformación del bosque tropical seco en la región del alto magdalena (Tolima- Colombia): valor predictivo de variables ambientales.

The knowledge of the transformation of biodiversity from remote sensing and the estimation and analysis of indices spectral can become a practical way to evaluate the territory and its resources, in addition to being a technique that can provide information base to guide decision making in the identification of priority areas for conservation. In this research I determined the predictive value of environmental variables (topographical, hydrological, anthropogenic and biomass) through statistical procedures with the purpose to analyse and establish if possible the transformation of space-time for subsequent years of the coverage of Tropical Dry Forest in the region of alto Magdalena (Colombia). Is processed by a series composed of 112 images of the sensor Landsat 4-5 TM, Landsat 7 ETM+ and Landsat 8, corresponding to the periods of dry season and wet in the temporalities 1987, 2000 and 2014; next to a series subsequent to the years 1990, 1995 and 2010 are used as years of control for the values of the variables of Biomass (Indices NDVI and NDII). To improve the level of interpretation of the changes that had the coverage I performed a correction of the values by means of the TVI (Vegetation Index Transformed) and the ranges established by Kalacska et al. (2004) for NDVI in BTs. For the variables of anthropogenic disturbance and watersheds, was applied and modified the methodology suggested by Quijas (2011) where they evaluated a series of distances euclidean from sampling sites with respect to hedges closest and with the greatest impact on the plant communities, which in this case were Grasses, Crops and bare ground and degraded. Considering, however, as the spatial scale can affect the ability of different predictor variables of biomass, was calculated from the values of the indices of plant biomass (NDVI, NDII) three spatial scales: 50, 150 and 300 m. These data were added as the fifth group of predictor variable and is called “Donuts”. Finished the processing, we obtained a total of 28 predictive variables, which were grouped and processed according to its attribute by means of the statistical programmes SPSS and JMP to obtain the 15 best models of testing for each year, giving as result a mathematical algorithm of prediction with best variables to set area.The data obtained are presented below.El conocimiento de la transformación de la biodiversidad a partir de la teledetección y la estimación y análisis de índices espectrales pueden convertirse en una manera práctica de evaluar el territorio y sus recursos, además de ser una técnica que puede proveer información base para guiar la toma de decisiones en la identificación de áreas prioritarias de conservación. En esta investigación se determinó el valor predictivo de variables ambientales (topográficas, hidrológicas antrópicas y de Biomasa) por medio de procedimientos estadísticos con el propósito de analizar y establecer de ser posible la transformación espacio-temporal para los años subsecuentes de la cobertura de Bosque Seco Tropical en la región del alto Magdalena (Colombia). Se procesó una serie compuesta de 112 imágenes de los sensores Landsat 4-5 TM, Landsat 7 ETM+ y Landsat 8, correspondientes a los periodos de época seca y húmeda en las temporalidades 1987, 2000 y 2014; junto a una serie subsecuente para los años 1990, 1995 y 2010 utilizados como años de control para los valores de las variables de Biomasa (Índices NDVI y NDII). Para mejorar el nivel de interpretación de los cambios que presentaba la cobertura se realizó una corrección de los valores por medio del TVI (Índice de Vegetación Transformado) y los rangos establecidos por Kalacska et al. (2004) para NDVI en BTs. Para las variables de perturbación antropogénica e hidrológicas, se aplicó y modifico la metodología sugerida por Quijas (2011) en donde se evaluaron una serie de distancias euclidianas a partir de sitios de muestreo respecto a las coberturas más cercanas y con mayor impacto en las comunidades vegetales, que para este caso fueron Pastos, Cultivos y tierra desnuda y degradada. No obstante, considerando como la escala espacial puede llegar a afectar la capacidad de distintas variables predictoras de biomasa, se calculó a partir de los valores de los índices de biomasa vegetal (NDVI, NDII) tres escalas espaciales: 50, 150 y 300 m. Estos datos se agregaron como el quinto grupo de variable predictora y se denominaron “Donas”. Finalizado el procesamiento, se obtuvo un total de 28 variables predictivas, que fueron agrupadas y procesadas según su atributo por medio de los programas estadísticos SPSS y JMP para obtener los 15 mejores modelos de prueba para cada año, dando como resultado un algoritmo matemático de predicción con las mejores variables para establecer área. Los datos obtenidos se presentan a continuación.PregradoIngeniero(a) Geógrafo y Ambienta

The response of southern African vegetation to droughts in past and future climates

Drought and climate change pose a threat to southern African vegetation. This study examines the response of southern African vegetation to drought in both past and future climates. Multiyear and multi-simulation datasets from three dynamic global vegetation models (DGVMs), namely, Community Land Model version 4 (CLM4), Community Land Model version 4 with Variable Infiltration Capacity hydrology (CLM4VIC), and Organising Carbon and Hydrology in Dynamic Ecosystems designed by Laboratoire des Sciences du Climat et de l’Environnement (ORCHIDEE-LSCE). These three DGVMs and the Community Earth System Model (CESM) were analyzed for the study. The DGVM simulations were forced with the reanalysis climate dataset from the National Centers for Environmental Prediction (NCEP) and the Climatic Research Unit - NCEP (CRUNCEP). The simulated climate results were evaluated with observation datasets from the Climatic Research Unit (CRU), while the simulated vegetation index (i.e. Normalized Difference Vegetation Index, NDVI) were evaluated with NDVI data from the Global Inventory Modelling and Mapping Studies (GIMMS). Meteorological droughts were analyzed at different timescales (1- to 18-month timescales), using two drought indexes: the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI). The responses of vegetation to drought were quantified by means of Pearson Correlation Analysis. The DGVMs were applied to study the sensitivity of vegetation to fire, while the CESM was used to project impact of climate change on the characteristics of southern African vegetation in the future (up to the year 2100) under the 8.5 Representative Concentration Pathway (RCP8.5) scenario, focusing on impacts at 1.5oC and 2.0oC global warming levels (GWLs). Analysis of the observed data shows that the spatial distribution of vegetation across southern Africa is more influenced by the rainfall distribution than by the temperature distribution. The observed correlation between drought index and vegetation index is higher than 0.8 over southeastern part of the region at 3-month drought timescale, and there is no difference between the spatial distribution of the correlation between the SPEI and the vegetation index, and between the SPI and the vegetation index. The three DGVMs failed to capture the response of vegetation to drought; however, the CLM4 shows the best performance while ORCHIDEELSCE fared the worst of the three. The CLM4 simulation show that fire strongly influences growth of vegetation over the summer rainfall region but it has weak influence over vegetation in the western arid zone. The CESM strongly captures the spatial patterns of precipitation and the vegetation index across southern Africa, but it overestimates the magnitudes of the vegetation index across the region, except in Namibia and Angola. The CESM also underestimates the correlation between drought indexes with vegetation, and the timescales at which the vegetation respond to droughts. The CESM projects an increase in the drought intensity as a result of an increased temperature across southern African biomes. However the increase in drought intensity is more pronounced with the SPEI than with the SPI. CESM also projects a future decrease in the vegetation index (i.e. NDVI) in the region except in the dry savanna biome. The impacts of 1.5oC GWLs on the vegetation fluxes vary throughout southern Africa, and the magnitudes of changes in the vegetation fluxes are affected by a further increase in global warming over the region. While there is a good agreement among the CESM simulations on the projected changes in vegetation fluxes across the biomes, the uncertainty in the projections is higher with 1.5oC than with 2.0oC GWL. The results of the study can be applied to mitigate the impacts of climate variability and change on southern African vegetation. Specific mitigation efforts that could be applied to reduce the impacts of droughts and climate change are watershed management, improved vegetation management, impact monitoring, environmental awareness, and remote sensing tools

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