Effects of climate change and land use intensification on regional biological soil crust cover and composition in southern Africa

Biological soil crusts (biocrusts) form a regular and relevant feature in drylands, as they stabilize the soil, fix nutrients, and influence water cycling. However, biocrust forming organisms have been shown to be dramatically vulnerable to climate and land use change occurring in these regions. In this study, we used Normalized Difference Vegetation Index (NDVI) data of biocrust-dominated pixels (NDVIbiocrust) obtained from hyperspectral and LANDSAT-7 data to analyse biocrust development over time and to forecast future NDVIbiocrust development under different climate change and livestock density scenarios in southern Africa. We validated these results by analysing the occurrence and composition of biocrusts along a mesoclimatic gradient within the study region. Our results show that NDVIbiocrust, which reached maximum values of 0.2 and 0.4 in drier and wetter years, respectively, mainly depended on water availability. A predicted decrease in rainfall events according to all future climate scenarios combined with increased temperatures suggested a pronounced decrease in NDVIbiocrust by the end of the 21st century caused by reduced biocrust coverage. Livestock trampling had similar effects and exacerbated the negative impacts of climate change on biocrust coverage and composition. Data assessed in the field concurred with these results, as reduced biocrust cover and a shift from well-developed to early stages of biocrust development were observed along a gradient of decreasing precipitation and increasing temperatures and livestock density. Our study demonstrates the suitability of multi-temporal series of historical satellite images combined with high-resolution mapping data and Earth system models to identify climate change patterns and their effects on biocrust and vegetation patterns at regional scales.ERC was supported by a Nobel Laureate Paul Crutzen fellowship; the REBIOARID (2018-101921-B-I00) project, funded by the FEDER/Science and Innovation Ministry-National Research Agency through the Spanish National Plan for Research and the European Union Funds for Regional Development; Consejería de Economía, Conocimiento, Empresas y Universidad from the Junta de Andalucía (GlobCRUST project EMERGIA20_0033), the Biodiversity Foundation of the Ministry for the Ecological Transition (BIOCOST project) and the RH2OARID (P18-RT-5130) funded by Consejería de Economía, Conocimiento, Empresas y Universidad from the Junta de Andalucía and the European Union Funds for Regional Development. BW was supported by the Max Planck Society (Nobel Laureate Fellowship) and the German Research Foundation (projects WE2393/2-1 and WE2393/2-2). EG is supported by the European Research Council grant agreement n° 647038 (BIODESERT). The research of US was supported by the German Federal Ministry of Education and Research (BMBF, promotion number 01LG1201N)

Quantification and spatial distribution of land-cover types in semiarid environments with hyperspectral imaging, a case study in Cabo de Gata-Níjar Natural Park (Almería)

En ecosistemas semiáridos mediterráneos la alta variabilidad espacial y la baja actividad de la vegetación hacen que las técnicas de teledetección multiespectrales presenten severas limitaciones en el estudio de variables como la cobertura vegetal. La cobertura vegetal está relacionada con la productividad del ecosistema y su cuantificación resulta muy útil para comprender y detectar cambios en su funcionamiento. En este trabajo hemos explorado la posibilidad de cuantificar los tipos de cubiertas (suelo, vegetación fotosintética y no fotosintéticamente activa) en el Parque Natural Cabo de Gata-Níjar (Almería) mediante la aplicación de modelos de mezclas en imágenes hiperespectrales. Nuestro objetivo es generar información cuantitativa que pueda ser utilizada en programas de seguimiento. A pesar de la alta variabilidad espectral encontrada ha sido posible cuantificar los distintos tipos de cubierta del área, previamente estratificada mediante criterios ecosistémicos. Los resultados han ayudado a establecer los rangos de variabilidad espectral en los ecosistemas estudiados. Al mismo tiempo, han permitido la identificación y análisis de los patrones de distribución espacial de las cubiertas en cada ecosistema, relacionados con procesos específicos de cada uno de ellos. Estos resultados contribuyen de forma significativa al desarrollo de metodologías de detección de cambio basadas en técnicas hiperespectrales.In semiarid environments the high spatial variability along with the low photosynthetic activity of vegetation results in difficulties of multispectral sensors in retrieving vegetation cover. Vegetation cover is a key variable in ecosystem processes and directly related with primary productivity. We have assessed the use of hyperspectral sensors and spectral mixing analysis to quantify land-cover types (soil, photosynthetic and non-photosynthetic vegetation) in Cabo de Gata-Níjar Natural Park. The analysis was performed within homogeneous ecosystems which allowed breaking down the high spatial variability of the area making possible the quantification of land-cover types. The aim of this work is to generate quantitative information for monitoring programs. The results allowed establishing the range of variability within each ecosystem type while identifying and analysing land-cover types link with specific ecosystem processes. Besides, it was possible to identify the distinct spatial patterns of land-cover types in each ecosystem studied. These results contribute significantly as the basis for the development of change detection methodologies based on hyperspectral data