Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km 2 ). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10-20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for Remote Sens. 2017, 9, 1119; doi:10.3390/rs9111119 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1119 2 of 21 estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr −1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources.

Bushmeat is the main source of protein and the most important source of income for rural people in the Congo Basin, but intensive hunting of bushmeat species is also a major concern for conservationists. Although spatial heterogeneity in hunting effort and in prey populations at the landscape level plays a key role in the sustainability of hunted populations, the role of small-scale heterogeneity within a village hunting territory in the sustainability of hunting has remained understudied. We built a spatially explicit multiagent model to capture the dynamics of a system in which hunters and preys interact within a village hunting territory. We examined the case of hunting of bay duikers (Cephalophus dorsalis) in the village of Ntsiété, northeastern Gabon. The impact of hunting on prey populations depended on the spatial heterogeneity of hunting and prey distribution at small scales within a hunting area. Within a village territory, the existence of areas hunted throughout the year, areas hunted only during certain seasons, and unhunted areas contributed to the sustainability of the system. Prey abundance and offtake per hunter were particularly sensitive to the frequency and length of hunting sessions and to the number of hunters sharing an area. Some biological parameters of the prey species, such as dispersal rate and territory size, determined their spatial distribution in a hunting area, which in turn influenced the sustainability of hunting. Detailed knowledge of species ecology and behavior, and of hunting practices are crucial to understanding the distribution of potential sinks and sources in space and time. Given the recognized failure of simple biological models to assess maximum sustainable yields, multiagent models provide an innovative path toward new approaches for the assessment of hunting sustainability, provided further research is conducted to increase knowledge of prey species' and hunter behavior.

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This paper examines the relevance of a perception-based regional level mapping tool in rural Niger. Two regions in Niger are examined. Results permit to assume that such a tool helped to fill several gaps: (i) a scale gap between local and nation wide studies; (ii) a scientific gap between biophysical and socio-anthropological sciences; and (iii) a methodological gap of integration between data sources. Moreover, this method is fast, cheap and action-oriented. Data are easily understandable and usable both by rural communities and development agencies. It provides information about human dynamics at a regional level, which cannot be achieved by other methods.

a b s t r a c tThe aim of this article is to analyze the impact of development interventions on the population of three Nigerien villages that differ in terms of their agro-ecological, social and economic characteristics. This is performed by simulating the behavior of individuals in an agent-based modeling framework which integrates the village characteristics as well as the family internal rules that condition access to economic and production activities. Villagers are differentiated according to the social and agro-ecological constraints they are subjected to. Two development project interventions are simulated, assuming no land scarcity: increasing the availability of inorganic fertilizers for farmers and an inventory credit technique based on millet grain. Two distinct approaches were used to model the rationale of farmers' decision making: gains or losses in economic value or gains or losses in within-village ''reputation''. Our results show that village populations do not respond en masse to development interventions. Reputation has little effect on the population behavior and should be considered more as a local proxy for wealth amongst villagers, suggesting the monetization of these societies. Populations involve themselves in the two simulated development interventions only at sites where savings are possible. Some level of household food security and investment capacity is actually required to take part in the development interventions, which are largely conditioned by family manpower and size. As long as uncultivated land remains available in the village territory, support for inorganic fertilizers has little impact in the absence of any intensification process. Inventory credit engages a maximum of 25% of the population at the site with medium agro-ecological conditions. Therefore, both interventions should be viewed as a potential support tool for a limited part of the population capable of going beyond the survival level, but not as a generic poverty-alleviation panacea.

Since the s, the northern part of the Amazonian region of Ecuador has been colonized with the support of intensive oil extraction that has opened up roads and supported the settlement of people from Outside Amazonia. These dynamics have caused important forest cuttings but also regular oil leaks and spills, contaminating both soil and water. The PASHAMAMA Model seeks to simulate these dynamics on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. The aim of the present paper is to describe this model, which integrates two dynamics: (a) Oil companies build roads and oil infrastructures and generate spills, inducing leaks and pipeline ruptures a ecting rivers, soils and people. This infrastructure has a probability of leaks, ruptures and other accidents that produce oil pollution a ecting rivers, soils and people. (b) New colonists settled in rural areas mostly as close as possible to roads and producing food and/or cash crops. The innovative aspect of this work is the presentation of a qualitative-quantitative approach explicitly addressed to formalize interdisciplinary modeling when data contexts are almost always incomplete.

Accidental oil spills were assessed in the north-eastern Ecuadorian Amazon, a rich biodiversity and cultural heritage area. Institutional reports were used to estimate oil spill volumes over the period 2001–2011. However, we had to make with heterogeneous and incomplete data. After statistically discriminating well- and poorly-documented oil blocks, some spill factors were derived from the former to spatially allocate oil spills where fragmentary data were available. Spatial prediction accuracy was assessed using similarity metrics in a cross-validation approach. Results showed 464 spill events (42.2/year), accounting for 10,000.2 t of crude oil, equivalent to annual discharges of 909.1 (±SD = 1219.5) t. Total spill volumes increased by 54.8% when spill factors were used to perform allocation to poorly-documented blocks. Resulting maps displayed pollution ‘hotspots’ in Dayuma and Joya de Los Sachas, with the highest inputs averaging 13.8 t km−2 year−1. The accuracy of spatial prediction ranged from 32 to 97%, depending on the metric and the weight given to double-zeros. Simulated situations showed that estimation accuracy depends on variabilities in incident occurrences and in spill volumes per incident. Our method is suitable for mapping hazards and risks in sensitive ecosystems, particularly in areas where incomplete data hinder this process.