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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.

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.

Mapping heathland habitats is generally challenging due to fine-scale habitats as well as spectral ambiguities between different classes. A multi-seasonal time-series of multispectral RapidEye data from several phenological stages was analysed towards the classification of different vegetation communities. A 3-level hierarchical dependent classification using Import Vector Machines was tested, based on the assumption that a probabilistic output per class would help the mapping. The first level of the hierarchical classification was related to the moisture gradient, which was derived from Ellenberg’s moisture indicative value. The second level aimed to separate plant alliances; the third level differentiated individual plant associations. For the final integration of the three classification levels, two approaches were implemented: (i) the F1-score and (ii) the maximum classification probability. The overall classification accuracies of both methods were found to be similar, around 0.7. Nevertheless, based on our expert knowledge we found the probabilistic approach to provide a more realistic picture and to be more practical compared to the result using the F1-score from the management point of view. In addition, the overall performance of the maximum probabilistic approach is better in the sense that the same accuracy of 0.7 was achieved with a differentiation of 33 classes instead of only 13 classes for the F1-score, meaning that the method is able to separate more spectral classes at a more detailed level providing the same accuracy.

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.

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).

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.