In the effort to achieve the Sustainable Development Goals (SDGs) related to food, health, water, and climate, an increase in pressure on land is highly likely. To avoid further land degradation and promote land restoration, multifunctional use of land is needed within the boundaries of the soil-water system. In addition, awareness-raising, a change in stakeholders’ attitudes, and a change in economics are essential. The attainment of a balance between the economy, society, and the biosphere calls for a holistic approach. In this paper, we introduce four concepts that we consider to be conducive to realizing LDN in a more integrated way: systems thinking, connectivity, nature-based solutions, and regenerative economics. We illustrate the application of these concepts through three examples in agricultural settings. Systems thinking lies at the base of the three others, stressing feedback loops but also delayed responses. Their simultaneous use will result in more robust solutions, which are sustainable from an environmental, societal, and economic point of view. Solutions also need to take into account the level of scale (global, national, regional, local), stakeholders’ interests and culture, and the availability and boundaries of financial and natural capital. Furthermore, sustainable solutions need to embed short-term management in long-term landscape planning. In conclusion, paradigm shifts are needed. First, it is necessary to move from excessive exploitation in combination with environmental protection, to sustainable use and management of the soil-water system. To accomplish this, new business models in robust economic systems are needed based on environmental systems thinking; an approach that integrates environmental, social, and economic interests. Second, it is necessary to shift from a “system follows function” approach towards a “function follows system” one. Only by making the transition towards integrated solutions based on a socio-economical-ecological systems analysis, using concepts such as nature-based solutions, do we stand a chance to achieve Land Degradation Neutrality by 2030. To make these paradigm shifts, awareness-raising in relation to a different type of governance, economy and landscape and land-use planning and management is needed.

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Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques.

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured OPEN ACCESS Remote Sens. 2014, 6 10889 or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.

Abstract:The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The large number of EO datasets and methods associated with the complex interactions among biophysical and social drivers of ecosystem changes make it difficult to apply aggregated EO indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends. Consequently, many uncertainties exist in regression models between rainfall, biomass and various indices that limit the ability of EO science to adequately assess and develop a consistent message on the magnitude of land degradation. We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term field-surveys and (7)