A New Framework for Modelling and Monitoring the Conversion of Cultivated Land to Built-up Land Based on a Hierarchical Hidden Semi-Markov Model Using Satellite Image Time Series

How Do Natura 2000 Areas Intersect with Peoples’ Livelihood Strategies in High Nature Value Farmlands in Southern Transylvania?

The establishment of the Natura 2000 network in Romania constitutes a turning point for the policy on biodiversity conservation in this country. The presence of human communities in certain Natura 2000 areas determines complex interactions between social and ecological systems, particularly in the case of High Nature Value farmlands that are assigned to this network of protected natural areas. A large part of Romania’s biodiversity depends on traditional farming systems that are under pressure from either agricultural intensification or land abandonment, which reflects socio-economic changes that have pushed rural households into developing new livelihood strategies. This paper explores the particular context of traditional rural communities from Southern Transylvania which is a High Nature Value farmland area largely included in the Natura 2000 network. We conducted an empirical analysis that focused on two main issues. The first was applying quantitative methods aimed at identifying the linkages between livelihood capitals and livelihood strategies of people living in Natura 2000 areas. The second was analyzing differences in local development levels which correlate with the share of territorial administrative units belonging to Natura 2000 areas. Our results are based on questionnaire and interview data collected from 40 rural administrative-territorial units within Southern Transylvania as well as on mapping land use changes using Landsat satellite images of 1985, 2003 and 2015. The results indicate that rural communities living in Natura 2000 areas turn to migration as an additional household strategy besides usual on-farm and off-farm activities, leading to rural shrinkage and farmland abandonment.

An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land

The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring.

Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain

Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000–2018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.