Search

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 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.

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.

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.

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.

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.