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Background This paper presents an analysis of fire regimes in the poorly studied Angolan catchment of the Okavango Delta in Botswana. We used MODIS data to examine the frequency and seasonality of fires over 20 years (from 2000 to 2020) in three dominant vegetation types (miombo woodlands, open woodlands and grasslands, and short closed to open bushlands), and in areas where people were present, and where they were absent. Results The median fire return intervals for both open woodlands and grasslands and short bushlands were relatively short (1.9 and 2.2 years respectively). In miombo woodlands, fires were less frequent (median return periods of 4.5 years). Human population density had no discernible effect on the fire return intervals, but about 14% of the miombo woodlands experienced no fires over 20 years. Ongoing shifting cultivation within miombo woodlands has led to structural changes and the introduction of fire into this vegetation type where fires were rare or absent in the past. About 12% of the miombo did not burn during the period examined where people were present, whereas close to 20% of the sites remained unburnt where people were absent. This suggests that people did not change the fire return interval in any of the vegetation types studied, but that they altered the amount of the landscape that is flammable in miombo vegetation. Fires occurred between June and September, with a peak in the late dry season (August and September). Conclusions Historical research indicates that late dry-season fires are detrimental to miombo woodlands, and our analysis suggests that degradation in parts of the catchment has led to the introduction of fire to this previously fire-free and fire-sensitive vegetation type. Deforestation of miombo woodlands, and the consequent introduction of fire, is a cause for concern with respect to the ecological stability of the Okavango Delta. Managers should therefore aim to protect the remaining closed-canopy miombo stands from further clearing and to attempt to shift the timing of burns to the early dry season to reduce their intensity., Antecedentes Este trabajo presenta un análisis de los regímenes de fuegos en la pobremente estudiada cuenca del delta del Río Okavango en Bostwana. Usamos datos de MODIS para examinar la frecuencia y estacionalidad de los incendios durante 20 años (desde 2000 hasta 2020) en tres tipos vegetales dominantes (bosques de miombo, bosques abiertos de sabanas y pastizales, y arbustales bajos cerrados y abiertos), y en áreas donde había gente presente y donde ahora está ausente. Resultados La mediana del intervalo de retorno del fuego tanto para las sabanas, los pastizales y los arbustales bajos fue relativamente baja (1,9 y 2,2 años, respectivamente). En los bosques de miombo, los incendios fueron menos frecuentes (la mediana del intervalo de fuego fue de 4,5 años). La densidad de la población humana no tuvo un efecto discernible en los intervalos de retorno del fuego, aunque alrededor del 14% de los bosques de miombo no experimentaron incendios en los últimos 20 años. Las modificaciones en los cultivos dentro de los bosques de miombo llevó a cambios estructurales, y la introducción del fuego en este tipo de vegetación estuvo ausente o fue rara en el pasado. En las áreas donde la gente estuvo presente, alrededor del 12% del miombo no se quemó durante el período examinado, mientras que cerca del 20% de los sitios permanecieron sin quemarse en los lugares donde la gente estuvo ausente. Esto sugiere que la gente no cambió el intervalo de retorno del fuego en ninguno de los tipos de vegetación estudiados, en los que ellos mismos alteraron la superficie del paisaje inflamable en la vegetación del miombo. Los incendios ocurrieron entre junio y septiembre, con un pico de ocurrencia al final de la estación seca (agosto y septiembre). Conclusiones La investigación histórica indica que los incendios al final de la estación seca son detrimentales para los bosques del miombo, y nuestro análisis sugiere que la degradación en partes de esa cuenca ha llevado a la introducción del fuego en este tipo de vegetación que era previamente libre de incendios y sensible a los efectos del fuego. La deforestación de los bosques de miombo, y la consecuente introducción del fuego, es una causa de preocupación para la estabilidad ecológica del delta del Okavago. Los gestores deberían por lo tanto ayudar a proteger los remanentes de los doseles cerrados de bosques de miombo de futuros aclareos, y hacer el intento de cambiar el período de quema hacia el inicio de la estación seca para reducir su intensidad.

This work was supported by the AGRIGEN-NORTE-01-0145-FEDER-000007; Norte Portugal Regional Operational Programme [NORTE2020]; PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundacao para a Ciencia e Tecnologia under contract CEECINST/00014/2018., Drylands range across more than half of the global terrestrial area and harbour about a quarter of continental vertebrate species, many of them endemic. However, this fauna is being increasingly threatened, in particular the one that inhabits deserts, one of the last biomes on earth. This work tracks the most relevant global change drivers acting on drylands, especially in deserts and arid regions, the conservation actions being developed, and the research needs for vertebrate conservation, following IUCN standardised classification schemes. Using the Sahara-Sahel wetlands as case study, it is provided a detailed examination of these aspects to support regional biodiversity conservation and human welfare. Deserts and arid regions are threatened by the synergistic effects of increasing development of urban areas, agriculture, energy production, mining, transportation and service corridors, resulting in pollution, invasive species, human intrusions and disturbance, biological resource overuse and in general, natural system modifications. In addition, climate change together with social underdevelopment of many desert-range countries places the mitigation of threat factors in a large and complex web of global-local societal challenges. Conservation actions targeting land/ water and species protection and management, as well as education, awareness, capacity building, and legislation measures to increase livelihood development, are being developed. Additional research efforts are need to enhance biodiversity conservation planning, monitoring biodiversity and land-degradation status (based on Essential Biodiversity Variables), and quantification of socioeconomic factors associated with sustainable use of natural resources and human development. Sahara-Sahel wetlands are important life-support systems for both humans and vertebrates, the last vulnerable to listed global threats. They offer framework scenario to revert current environmental and societal challenges in deserts. Long-term conservation of desert vertebrate biodiversity requires appropriate policy instruments to promote sustainable use of natural resources. Raising environmental alertness within local communities of uniqueness of desert biodiversity is needed to promote policy change., Norte Portugal Regional Operational Programme [NORTE2020], PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), Portuguese Foundation for Science and Technology European Commission CEECINST/00014/2018 AGRIGEN-NORTE-01-0145-FEDER-000007

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments.

We present a framework for mapping grazing management units in extensive pastoral landscapes. Mapping such units from remote sensing, particularly in the pastoral landscapes of East Africa, is usually hampered by high cost of field data collection and by the fact that the relationship between rangeland ecological characteristics and rangeland use for adaptive grazing management by mobile pastoralists is context specific and reflects complex socio-ecological interactions. We propose, therefore, a flexible framework that combines topographic descriptors with rangeland greenness and phenology indicators derived by remote sensing imagery to derive thematic classes that users can query, according to their requirements, to produce grazing management units. The framework is illustrated for an extensive pastoral region in Southern Ethiopia. The thematic classes generated were statistically compared with Plant Functional Types (PFTs) classes obtained by interpretation of GPS-tagged photos, showing a highly significant relationship (Chi-Square test, χ2 = 5971.4, p = 0.0001). Then, the same classes were further classified into grazing management units by integrating information about rangeland seasonal use and accessibility from pastoral communities. This approach could support the definition of adaptive community-based rangeland management strategies by combining ecologically meaningful maps derived by satellite indicators with local knowledge about rangeland use and accessibility.

Tropical dry forests provide globally important ecosystem services and host exceptionally high biodiversity. These biomes are currently under immense pressure, particularly for conversion to agriculture, and already experience high global deforestation rates. Miombo forests in Southern Angola are affected by deforestation, fragmentation and degradation, caused mainly by an increasing rural population who follows a traditional farming system of shifting cultivation with slash-and-burn agriculture. After the termination of the civil war in 2002, population growth and resettlements have accelerated the use of woody resources, selective logging and clearing for cultivation purposes and led to an exceedance of sustainability thresholds. Large scale projects are expected to put further pressure on the forests and increase the potential of conflicts regarding land resources and competition with local subsistence farming. We use an existing time series segmentation tool (LandTrendr) with a time series of Normalized Burn Ratio (NBR) data in combination with adapted temporal metrics to provide information about the dynamics of different cultivation patterns, to gain insight into historical developments and to assess temporal cultivation characteristics. We define cleared areas and cultivation time on a pixel-by-pixel basis providing temporal and spatial information on current and past changes from 1989 to 2013 using data from Landsat 5–8. Overall accuracy for the disturbance detection is 72%. We can follow the effect of armed conflicts on agricultural expansion with a drop in deforestation rate of more than 70% from 12,000 to 4000 ha per year (1994–1998) and subsequently tripling to 12,000 ha per year again after 2002. Deforestation patterns are in accordance with previous multi-temporal studies, although time series segmentation reveals more detailed information on deforestation and cultivation dynamics. We successfully separate areas of different historic backgrounds and agricultural dynamics, e.g. areas that were severely affected during the civil war, which transition from shifting to semi-permanent and permanent systems. We provide recommendations for the assessment of agricultural dynamics in similar areas where ground data and basic information is missing.

The need for operational monitoring of landscape processes on the national to global scale led to an increased demand for pixel-based composites using complete earth observation (EO) archives. Commonly, composites are generated without explicit consideration of temporal criteria but are rather based on optimizing band indices within a pre-defined temporal window. However, for certain applications phenology-adapted composites that represent the land surface as being in the same phenological stage are required, e.g. tree type discrimination where greening up or senescence dates are modified by terrain elevation. We developed a novel pixel-based compositing technique that dynamically adjusts the selection process to the underlying land surface phenology (LSP) of each pixel. By doing so, phenologically sound composites across large areas can be derived for regular intervals and different phenological points in time, e.g. peak, end or minimum of season. Various day-of-year (DOY) scoring functions were implemented to flexibly define the phenological target. The technique is general enough for global application and can be applied to any kind of gridded EO archive, herein demonstrated for MODIS and Landsat data. Multi-annual composites were successfully generated for Zambia for most seasons. As an exception, we found even very frequent MODIS observations to be insufficient for peak vegetation composites due to interference with the rainy season. The phenology-adaptive composites were compared to static ones, i.e. using a single target DOY. Results clearly indicated that biomass levels differ significantly between the techniques, and a phenological normalization across elevation gradients and land cover classes could be achieved. However, the implications are non-trivial and the characteristics of both methods need to be considered cautiously before deciding which approach is superior with regards to a specific thematic application. The quality of the MODIS and Landsat composites, as well as the performance of the phenology-adaptive and static compositing techniques were assured using a quantitative cross-comparison. A 12-year annual time series demonstrated the feasibility for land cover change and modification mapping. Several change processes were clearly discriminable. The resulting phenologically coherent composites are important to establish national, regional or even global landscape monitoring, reporting and verification systems.

We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by a modified Fmask code. Surface reflectance is inferred from Tanre's formulation of the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) is estimated over dark objects (DOs) that are identified in a combined database and image-based approach, where information on their temporal persistency is utilized. AOD is inferred with consideration of the actual target reflectance and background contamination effect. In case of absent DOs in bright scenes, a fallback approach with a modeled AOD climatology is used instead. Topographic normalization is performed by a modified C-correction. The data are projected into a single coordinate system and are organized in a gridded data structure for simplified pixel-based access. We based the assessment of the produced data set on an exhaustive analysis of overlapping pixels: 98.8% of the redundant overlaps are in the range of the expected ±2.5% overall radiometric algorithm accuracy. AOD is in very good agreement with Aerosol Robotic Network sunphotometer data ( $R^{2}$ : 0.72 to 0.79, low intercepts, and slopes near unity). The uncertainty in using the water vapor fallback climatology is approximately ±2.8% for the TM SWIR1 band in the wet season. The topographic correction was considered successful by an investigation of the nonrelationship between the illumination angle and the corrected radiance.

Satellite-derived land surface phenology (LSP) serves as a valuable input source for many environmental applications such as land cover classifications and global change studies. Commonly, LSP is derived from coarse-resolution (CR) sensors due to their well-suited temporal resolution. However, LSP is increasingly demanded at medium resolution (MR), but inferring LSP directly from MR imagery remains a challenging task (e.g., due to acquisition frequency). As such, we present a methodology that directly predicts MR LSP on the basis of the respective CR LSP and MR reflectance imagery. The approach considers information from the local pixel neighborhood at both resolutions by utilizing several prediction proxies, including spectral distance and multiscale heterogeneity metrics. The prediction performs well with simulated data $(R^{2} = 0.84)$ , and the approach substantially reduces noise. The size of the smallest reliably predicted object coincides with the effective CR pixel size (i.e., field-of-view). Nevertheless, even subpixel objects can be reliably predicted provided that pure CR pixels are located within the search radius. The application to real MODIS LSP and Landsat reflectance well preserves the phenological landscape composition, and the spatial refinement is especially striking in heterogeneous agricultural areas, where, for example, the circular shape of center pivot irrigation schemes is successfully restored at MR.

The repopulation of abandoned areas in Angola after 27years of civil war led to a fast and extensive expansion of agricultural fields to meet the rising food demand. Yet, the increase in crop production at the expense of natural resources carries an inherent potential for conflicts since the demand for timber and wood extraction are also supposed to rise. We use the concept of ecosystem services to evaluate the trade-off between food and woody biomass. Our study area is located in central Angola, in the highlands of the upper Okavango catchment. We used Landsat data (spatial resolution: 30×30m) with a bi-temporal and multi-seasonal change detection approach for five time steps between 1989 and 2013 to estimate the conversion area from woodland to agriculture. Overall accuracy is 95%, user's accuracy varies from 89-95% and producer's accuracy ranges between 92-99%. To quantify the trade-off between woody biomass and the amount of food, this information was combined with indicator values and we furthermore assessed biomass regrowth on fallows. Our results reveal a constant rise in agricultural expansion from 1989-2013 with the mean annual deforestation rate increasing from roughly 5300ha up to about 12,000ha. Overall, 5.6% of the forested areas were converted to agriculture, whereas the FAO states a national deforestation rate for Angola of 5% from 1990-2010 (FAO, 2010). In the last time step 961,000t per year of woodland were cleared to potentially produce 1240t per year of maize. Current global agro-economical projections forecast increasing pressure on tropical dry forests from large-scale agriculture schemes (Gasparri et al., 2015; Searchinger and Heimlich, 2015). Our study underlines the importance of considering subsistence-related change processes, which may contribute significantly to negative effects associated with deforestation and degradation of these forest ecosystems.

We developed a spatio-temporal path reflectance climatology for use in atmospheric corrections for a Landsat pre-processing framework. The climatology is intended as a fallback strategy for aerosol estimation in bright Southern African savannah ecosystems where the rarity of dark objects decreases the applicability of common image-based aerosol estimation strategies and the widespread burning prohibits the use of a fixed aerosol loading. We predicted the climatological path reflectance surface by applying a multivariate regression model to all available path reflectance retrievals on basis of the geolocation and the days of the year on which the data were acquired. The resulting predictions are able to successfully model major spatio-temporal gradients of the path reflectance distribution. The prediction error (weighted root mean squared Error at 0.483 µm) was less than 1% reflectance while the prediction itself varied by 4.6% reflectance. Thus, using the modelled climatology for atmospheric correction is...

The Okavango catchment is a hot spot of accelerating land use change. In particular, climate predictions, demographic developments and a growing utilization of ecosystem services and functions are expected to increase pressure on resources and land. Land use conflicts, the sustenance of precarious livelihoods, deforestation of woodland savannahs, upstream–downstream water issues and human–wildlife conflicts are among the processes that are characteristic of policy and management challenges in the region. In the Eastern and Western Kavango regions of Namibia and the Cuando-Cubango province of Angola, a unique cross-border situation exists that allows assessing how the combination of local traditions, regional land management and national policies determines spatial patterns of land use and land cover transformation processes. To map major land use types and change processes we used a set of multi-temporal Landsat-5 TM and Landsat-7 ETM+ data sets, support vector machine (SVM) classification and iterative spectral mixture analysis (ISMA) on images covering the period from 1990 to 2010. Integrating satellite imagery with literature reviews, interviews, census and household survey data, we assessed the contrasting development of resource utilization on both sides of the Okavango River. We investigated if and how policies and regulations at different levels drive land use decisions, and how these decisions manifest spatially. We found a strong and interconnected urban growth on both sides of the river. The area around Rundu has constantly been evolving to become Namibia's second largest city, also functioning as a hub of development and transborder commerce with opposing Calai. This trend was found to affect adjacent settlement areas and cause widespread conversion of woodland savannahs to agricultural land or their utilization for timber extraction. The conversion of woodland savannah to arable land was by far the dominant land use change process on both sides of the river, with a total conversion area of 460 km2 (Namibia) and 293 km2 (Angola) observed during the observation period. Strong spatial change gradients occurred in relation to determining factors, such as accessibility, proximity to water, urban centres, etc., while relations to settlements where less obvious. Assessing results by country illustrated the difference in land use intensity and resource consumption between Angola and Namibia, which relate directly to historical developments, with a long period of stability in Namibia standing opposed to the recent and ongoing recovery from civil war in Angola. These are added to by statutory and traditional policy frameworks, the national endowment with natural capital (e.g. oil, uranium, diamonds, zinc) and the integration into global markets, which strongly affects national economies of both countries at large. Underlying land use decisions were found to be largely driven by individualized perspectives on growth ideologies, consumerism and wealth-aspirations connected to globalization processes. However, at present the result of these perspectives is still mainly a small-structured conversion to rainfed agriculture as a component of subsistence strategies of local livelihoods, and thus stands opposed to other regions of the world, where change processes are much more driven by large companies or follow national regulations and result in more intensive uses.

Societal, economic and scientific interests in knowing where biodiversity is, how it is faring and what can be done to efficiently mitigate further biodiversity loss and the associated loss of ecosystem services are at an all-time high. So far, however, biodiversity monitoring has primarily focused on structural and compositional features of ecosystems despite growing evidence that ecosystem functions are key to elucidating the mechanisms through which biological diversity generates services to humanity. This monitoring gap can be traced to the current lack of consensus on what exactly ecosystem functions are and how to track them at scales beyond the site level. This contribution aims to advance the development of a global biodiversity monitoring strategy by proposing the adoption of a set of definitions and a typology for ecosystem functions, and reviewing current opportunities and potential limitations for satellite remote sensing technology to support the monitoring of ecosystem functions worldwide. By clearly defining ecosystem processes, functions and services and their interrelationships, we provide a framework to improve communication between ecologists, land and marine managers, remote sensing specialists and policy makers, thereby addressing a major barrier in the field.

The country of Spain is representative of land change processes in Mediterranean member states of the European Union (EU). These land change processes are often triggered by European, national and sub-national policies and include widespread land abandonment and urbanisation trends, as well as an increase in land use intensities accompanied by strong exploitation of water resources. The Mediterranean is part of the dryland ecoregion, which is particularly vulnerable to ecosystem degradation. While remote sensing data permit the characterisation of the temporal dimension of land surface processes, the syndrome-based approach aims to integrate this with information on local/regional socio-economic and physical frameworks. In this study, we incorporated two major drivers of land change, climatic boundary conditions and population density change, to understand the patterns of the assessed land cover changes. We used the Mediterranean Extended Daily One Km AVHRR Data Set (MEDOKADS), which represents the Mediterranean Basin at a resolution of 1 km2 for the period of 1989-2004 at 10-day time intervals. The long-term vegetation trend could be characterised based on a linear regression analysis of Normalised Difference Vegetation Index (NDVI) values. Further descriptors of phenology were calculated, such as the amplitude of annual cycles or the date of occurrence of the annual maximum of vegetation cover. Subsequently, trend analysis was applied to these parameters to map phenology shifts representative of changes in land use/cover. Next, a rule-based classification was employed to integrate the individual results and assign syndromes representing specific land change process patterns. For natural and semi-natural areas, locations characterised by increasing biomass are associated with areas of population loss due to migration to urban centres. By contrast, many fertile agricultural areas are associated with intensification processes, including the expansion of irrigation schemes. Furthermore, areas that are dominated by inter-annual variability in precipitation were identified by employing the results of an analysis of NDVI-rainfall relationships. The resulting data set identifies major land change syndromes for the whole of Spain and links the apparent results of change to driving factors and causes, thus serving as an excellent data source to inform policy makers of effects caused by legislative and management actions.

Drylands cover about 41% of the globe's surface and provide important ecosystem services, but land use and climate change exert considerable pressure on these ecosystems. Both of these drivers frequently result in gradual vegetation change and landscape-scale trend analysis based on yearly vegetation estimates can capture such changes. Such trend analyses based on high-resolution time series of satellite imagery have so far not widely been used and existing studies in drylands relied on different vegetation measures. Spectral mixture analysis (SMA) has been chosen due to its superiority to simpler vegetation estimates in quantifying vegetation cover in single-date studies, however SMA can be challenging to implement for large areas. Here, we quantify the trade-off involved when using simple vegetation estimates instead of SMA fractions for subsequent trend analyses. We calculated NDVI, SAVI and Tasseled Cap Greenness, as well as SMA green vegetation fractions for a time series of Landsat images from 1984–2005 for a study region in Crete. Linear trend analysis showed that trend coefficients and the spatial patterns of trends were similar across all vegetation estimates and the entire study region, especially for areas where vegetation changed gradually. On average, trends based on simple measures differed less than 5% from SMA-based trends with decreasing similarity in trend results from Tasseled Cap Greenness to SAVI and NDVI. Vegetation estimates differed markedly in their response to disturbance events such as fires. Trend analyses based on qualitative measures can easily be applied across very large areas and using multi-sensor time series based on high-resolution data. While the subtle differences between vegetation estimates may still be important for some applications, the robustness of trend analyses regarding the choice of vegetation estimate bears considerable promise to reconstruct fine-scale vegetation dynamics and land use histories and to assess climate change impacts on the world's drylands.

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice.

16 páginas, 6 figuras, 5 tablas., Diagnosis of land condition is a basic prerequisite for finding the degradation of a territory under climatic and human pressures leading to desertification. Ecosystemic approaches, such as the one presented here, address ecosystem maturity or resilience. They are low cost, not very prone to error propagation and well-suited to implementation on remotely sensed time–series data covering large areas. The purposes of this work were to develop a land condition surveillance methodology based on the amount of biomass produced per unit rainfall, and to test it on the Iberian Peninsula. In this article, we propose parallel and complementary synchronic assessment and diachronic monitoring procedures to overcome the paradox of monitoring as a sequence of assessments. This is intrinsically contradictory when dealing with complex landscape mosaics, as relative estimators commonly produced for assessment are often difficult to set in a meaningful time sequence. Our approach is built on monthly time–series of two types of data, a vegetation density estimator (Green Vegetation Fraction-GVF) derived from Global Environmental Monitoring satellite archives, and corresponding interpolated climate fields. Rain Use Efficiency (RUE) is computed on two time scales to generate assessment classes. This enables detrended comparisons across different climate zones and provides automatic detection of reference areas to obtain relative RUE. The monitoring procedure uses raw GVF change rates over time and aridity in a stepwise regression to generate subclasses of discriminated trends for those drivers. The results of assessment and monitoring are then combined to yield the land condition diagnostics through explicit rules that associate their respective categories. The approach was tested in the Iberian Peninsula for the period 1989 to 2000 using monthly GVF images derived from the 1-km MEDOKADS archive based on the NOAA-AVHRR sensors, and a corresponding archive of climate variables. The resulting land condition was validated against independent data from the Natura 2000 network of conservation reserves. In very general terms, land was found to be healthier than expected, with localised spots of ongoing degradation that were associated with current or recent intensive land use. Static or positive vegetation growth rates were detected almost everywhere, including Mediterranean areas that had undergone increased aridification during the study period. Interestingly, degrading or static trends prevailed in degraded or unusually degraded land, whereas trends to improve were most represented in land in good or unusually good condition., This research was funded by the European Commission projects LADAMER (ref. EVK2-2002-0599) and DeSurvey (Integrated Project contract No. 003950).

A novel multiannual land-cover-classification scheme for classifying hypertemporal image data is suggested, which is based on a supervised decision fusion (DF) approach. This DF approach comprises two steps: First, separate support vector machines (SVMs) are trained for normalized difference vegetation index (NDVI) time-series and mean annual temperature values of three consecutive years. In the second step, the information of the preliminary continuous SVM outputs, which represent posterior probabilities of the class assignments, is fused using a second-level SVM classifier. We tested the approach using the 10-day maximum-value NDVI composites from the ldquoMediterranean Extended Daily One-km Advanced Very High Resolution Radiometer Data Setrdquo (MEDOKADS). The approach increases the classification accuracy and robustness compared with another DF method (simple majority voting) and with a single SVM expert that is trained for the same multiannual periods. The results clearly demonstrate that DF is a reliable technique for large-area mapping using hypertemporal data sets.

In this study a link was established between anomalies in climatic and Advanced Very High Resolution Radiometer (AVHRR)/Normalized Difference Vegetation Index (NDVI) data in Spain for the period from 1989 to 1999 on a monthly and annual basis using multivariate distributed lag (DL) models and generalized least-square (GLS) parameter estimation. In most areas significant time-delayed correlation between anomalies of monthly rainfall and NDVI data was confined to an interval of 1 month. Locally higher lag orders of up to 3 months were found. By contrast, relationships between surface temperature and the NDVI were insignificant in the multivariate context at most locations. The multiple correlation coefficients of the DL models achieved 0.6 in the maximum. Regions characterized by the most significant NDVI-rainfall correlations include the southern forelands of the Pyrenees in Cataluna, rainfed agricultural areas in Extremadura, Andalusia, and the western parts of Castilla y Leon. Average ratios of rainfall to potential evapotranspiration (PET) in the sensitive areas ranged between 0.5 and 2, with annual rainfall amounts less than 700 mm. For each land-cover class a linear discriminant analysis (LDA) was carried out to assess the environmental factors that might explain the differences in the NDVI-rainfall relationships. The highest discriminant coefficients and factor loadings were recorded for those factors that recurrently trigger water deficit in the sensitive regions, such as low total annual rainfall, large seasonal rainfall variability, high average PET and surface temperature. On the annual basis the lagged correlation of the NDVI and rainfall data was confined to natural vegetation (grassland and scrubland) areas in western Spain. This region suffered from a severe drought in the early 1990s, after which biomass production lagged several years behind improved rainfall conditions. The approach presented is useful for assessing the influence of climatic variables on the pattern of temporal anomalies in the NDVI or related vegetation parameters.

Land degradation is caused by and has impacts on both the social and natural components of coupled human-environment systems. However, few studies integrate both aspects simultaneously. The main objective of this study is to test a method to evaluate land degradation based on the integration of aggregate metrics of biophysical and socio-economic "degradation". We applied a framework that integrates the biophysical and socio-economic dimensions of land degradation to test the hypothesis that macro-economic policies, and in particular agricultural subsidies, are an important driving force of land degradation in marginal regions of the Mediterranean Europe. We analysed the influence of subsidies on the profitability of each crop and livestock type found in a sample of farms in a region of northern Greece. Spatial and socioeconomic data on agricultural households were collected to link remote sensing data and land degradation maps to socio-economic conditions of these households, as measured by the standard gross margin. The results demonstrate that subsidies provide a crucial socio-economic support to maintain the profitability of agricultural activities but may also promote land-use practices with damaging ecological impacts. Different levels of biophysical and socio-economic "degradation" were associated with different land use practices. The integration of the socio-economic and biophysical dimensions of land degradation reveals associations that would not be detectable if indicators along one dimension alone would be used. (C) 2008 Elsevier B.V. All rights reserved.

In past decades, the European Mediterranean has undergone widespread land use transformations. These are largely driven by changes of socio-economic conditions, such as accession to the European Community, and had strong effects on the way the land is being used. Aiming at a systematic description of such change processes on a global level, the syndrome concept was proposed to describe archetypical, co-evolutionary patterns of human–nature interactions, and has been specifically linked to the desertification issue. In this study, we present an adaptation of the syndrome approach to the Iberian Peninsula. We suggest a data processing and interpretation framework to map the spatial extent of specific syndromes. The mapping approach is based on the time series analysis of satellite data. We have characterized vegetation dynamics using NDVI estimates from the coarse scale, hyper-temporal 1-km MEDOKADS archive, which is based on calibrated NOAA–AVHRR images. Results indicate that local patches of abrupt disturbance, mainly caused by fire, are contrasted by a widespread increase in vegetation, which is in large parts attributed to the abandonment of rural areas. Although this questions the dominance of classical desertification traits, i.e. decline of productivity after disturbance, it is concluded that the recent greening presents a different sort of environmental risk, as it may negatively impact on fire regimes and the hydrological cycle.

Aboveground net primary productivity (ANPP) is limited by water availability especially in dry and desert regions, and many studies have linked ANPP to current and previous “effective” rainfall events. In this study a distributed lag model (DLM) was used to assess the impact of current and previous 16 day rainfall anomalies on the Enhanced Vegetation Index (EVI) as a proxy for ANPP in the Okavango catchment (South Africa). The two important aspects in using DLMs are the explained total ANPP variability by the rainfall regime and the duration of that dependency. The results indicate that more than 50 % of the Okavango Basin are sensitive towards current and previous rainfall anomalies. These regions are mainly restricted to the southern semi-arid parts of the catchment, whereas in the humid and sub-humid northern areas significant correlations were observed only locally. Here, the dominant land cover classes are shrub- and grassland, thornbush savannahs and mixed woodlands. The duration of significant rainfall-EVI dependencies ranges from concurrent anomalies to a time-shift of 3.5 months. A logistic regression model was applied to discriminate among the sensitive and non-sensitive areas in the basin in terms of possible physiogeographic covariates. The model was able to correctly classify ~80 % of the available pixels. Most relevant explanatory covariates were evaporation, elevation and land cover.

Understanding the impact of land transformation processes on ecosystem services (ESS) is an essential prerequisite for drafting and implementing sustainable land management concepts. This study presents an analysis of land transformation processes in Horqin Sandy Lands, one of the dry areas in Inner Mongolia (China). It aims at demonstrating the impacts of governmental management policies on land use change and its impact on the long-term availability of important ecosystem services. Spectral mixture analysis is applied to a calibrated time series of Landsat-TM/ETM+ images which covers a period of 20 years (1987–2007); the mixture model comprises three spectral end-members (Green Vegetation, Mobile Sand, Water) which are conceived as surrogates for important ecosystem services. Changing land surface conditions are identified through linear trend analysis of end-member proportions and by mapping the spatial extension of specific surface types at subsequent dates within the observation period. For translating the derived change rates into readjustments of selected ESS-indicators a simple linear model is proposed. Fuelled by long-term satellite observations, the synoptic representation of changing ecosystem services forms the basis for addressing synergies and trade-offs between ecological and societal well-being. The case of Horqin Sandy Lands, where new land use concepts are implemented by promoting selected ecosystem services at the cost of others, provides a striking example for these mechanisms.

The aim of this study was to evaluate the potentials and limits of remote sensing time series regarding change analysis of drylands. We focussed on the assessment and monitoring of land degradation using different scales of remote sensing data. Special interest was paid on how the spatial resolutions of different sensors influence the derivation of vegetation related variables, such as trends in time and the shift of phenological cycles. Hence, a comparison was performed using high and medium resolution sensors and their suitability for monitoring land degradation will be evaluated. Long time series of Landsat TM and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a test area in the Mediterranean. At local scale additional information was delivered by a multi-seasonal land use/cover change detection (LUCC) analysis. The test site which is located in Central Macedonia (Greece) is mainly characterized by long-term, gradual processes mainly driven by grazing and the extension of irrigated arable land.

Land use conversions or changes of land management practices are primary drivers of global environmental change. 'Natural experiment' situations, where some conditions vary, but other potential land use determinants remain relatively constant, offer unique opportunities to study land use change, its drivers and feedbacks on human-environment systems. The Chalkidiki peninsula in Northern Greece is an ideal test case to study recent land use transformations and socio-economic changes (e.g. resulting from accession to the EU) against a stable reference area. Of the three peninsular legs of Cassandra, Sithonia and Athos, the latter harbours the 'Autonomous Monastic State of the Holy Mountain', a sovereign and isolated monastic state. Apart from subsistence agriculture around the monasteries, it represents a Mediterranean ecosystem in a state virtually unaffected by modern human use. We have used a time series of 22 fully corrected Landsat-TM and ETM+ data to study land use/land cover change on the peninsula, and related the results to a similar study in the adjacent County of Lagadas. A diachronic land use change analysis based on SVM classification was conducted using two three image-pairs. Where natural and semi-natural vegetation formations remained stable, trends were calculated using a pixel-wise linear trend analysis of SMA-derived vegetation cover estimates. Results were interpreted using auxiliary data and in relation to the Athos area. Changes were found to result from discontinuation of extensive land use in Cassandra and Sithonia in favour of intensified agricultural use and the expansion of tourist activities, complemented by land abandonment in less attractive areas.

In the field of hydrological modelling, there is mostly a lack of spatially distributed data that may allow a detailed analysis of simulation results. This study was to demonstrate that remote sensing can partly fill this gap, as combining reflective and thermal data allows the retrieval of estimates for evapotranspiration (ET). Two Landsat-5 TM scenes were analysed, and the results were afterwards compared to the daily output of the Precipitation Runoff Modeling System, a conceptual model based on Hydrologic Response Units and designed for meso- to macroscale applications. For the study site, the mesoscale Ruwer basin located in the low mountain range of Rhineland-Palatinate (Germany), an overall good agreement of ET estimates retrieved by both approaches was found. At one date, some mismatches indicated clear inconsistencies in the model structure and parameterisation scheme. Based on these findings, a modified soil module was implemented to allow for a more realistic specification of land use dependant parameters. After this, PRMS provided ET estimates now very similar to those from Landsat TM, and the RMSE was reduced from 1.30 to 0.86 mm. These results indicate, that the representation of the hydrological cycle by hydrological modelling may be improved by the integration of appropriate remote sensing data.

Fire spread information on a large scale is still a missing key layer for a complete description of fire regimes. We developed a novel multilevel object-based methodology that extracts valuable information about fire dynamics from Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data. Besides the large area capabilities, this approach also derives very detailed information for every single fire regarding timing and location of its ignition, as well as detailed directional multitemporal spread information. The approach is a top–down approach and a multilevel segmentation strategy is used to gradually refine the individual object membership. The multitemporal segmentation alternates between recursive seed point identification and queue-based fire tracking. The algorithm relies on only a few input parameters that control the segmentation with spatial and temporal distance thresholds. We present exemplary results that indicate the potential for further use of the derived parameters.

Results of ground based experiments on spectral reflectance of dock leaves using a ZEISS spectrometer MCS501 UV-NIR with 2.5 nm resolution clearly show the well known induction kinetics of chlorophyll fluorescence when dark adapted leaves were exposed to continuous illumination. This observed feature demonstrates the origin of the transient behavior of reflectance caused by natural or sun-induced fluorescence. The time constant of the decline of sun induced chlorophyll fluorescence is identical to the time constant obtained simultaneously using a PAM (Pulse Amplitude Modulation) fluorometer. Results of airborne measurements with the sensor ROSIS (Reflective Optics System Imaging Spectrometer) demonstrate for the first time that sun induced chlorophyll fluorescence can be derived from airborne hyperspectral reflectance data. The new method presented here uses the spectral absorption feature of atmospheric oxygen at 762 nm, which fits better with the wavelength of chlorophyll fluorescence than the Fraunhofer absorption line at 656 nm. The results from ROSIS show maps of sun-induced chlorophyll fluorescence of green vegetation. New spatial and temporal features are observable on agricultural fields supporting the hypothesis that information on photosynthesis and plant stress may be extracted in the future from areal observation of sun-induced fluorescence.

The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence.