Remote Sensing

Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefighters’ lives to danger. Thus, remote sensing technologies have become one of the most demanded strategies to fight against wildfires, especially UAV-based remote sensing technologies. They have been adopted to detect forest fires at their early stages, before becoming uncontrollable.
This work established a framework to identify and analyze the technical feasibility of roofs for integrating urban agriculture, rainwater harvesting, and photovoltaic systems using various remote sensing. The framework was applied to a region north of Barcelona. Three levels of solar access requirements for tomatoes, leafy crops, strawberries, and microgreens were established. The case study included compact and disperse urban forms, residential and nonresidential building uses and various building typologies.
Ethiopia has experienced rapid urbanization over the past three decades. Several cities expanded rapidly and many satellite towns sprung up around the major cities. The high rate of urbanization and urban growth resulted in high demand for urban land, mainly for industrial, commercial, and residential purposes. In order to meet the demand, an enormous amount of land has been made available for urban use, mainly through land conversion. However, we know very little about how efficiently cities use urban land.
Tracking progress toward the Sustainable Development Goals (SDGs) requires monitoring of various social-ecological indicators over space and time, including the ratio of land consumption rate to population growth rate (LCRPGR), an indicator of land-use efficiency (SDG 11.3.1). In this study, we analyzed state-of-the-art Earth observation data (1975–2015) to address three key questions. First, how has the LCRPGR varied over space and time? Second, how is built-up expansion related to population increase across regions?
Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts.
In the face of the growing challenges brought about by human activities, effective planning and decision-making in biodiversity and ecosystem conservation, restoration, and sustainable development are urgently needed. Ecological models can play a key role in supporting this need and helping to safeguard the natural assets that underpin human wellbeing and support life on land and below water (United Nations Sustainable Development Goals; SDG 15 & 14).
The natural world has multiple, sometimes conflicting, sometimes synergistic, values to society when viewed through the lens of the Sustainable Development Goals (SDGs), Spatial mapping of nature's contributions to the SDGs has the potential to support the implementation of SDG strategies through sustainable land management and conservation of ecosystem services. Such mapping requires a range of spatial data.
Agricultural landscapes cultivated in hilly and mountainous areas, often with terracing practice, could represent for some regions historical heritages and cultural ecosystem services. For this reason, they deserve to be protected. The complex morphology that characterises them, however, makes these areas intrinsically susceptible to hydrogeological instability, such as soil loss due to surface erosion or more severe mass movements. We can identify three major critical factors for such landscapes.
Sustainable Development Goal (SDG) indicator 15.1.1 proposes to quantify “Forest area as a proportion of total land area” in order to achieve SDG target 15.1. While area under forest cover can provide useful information regarding discrete changes in forest cover, it does not provide any insight on subtle changes within the broad vegetation class, e.g. forest degradation. Continental or national-level studies, mostly utilizing coarse-scale satellite data, are likely to fail in capturing these changes due to the fine spatial and long temporal characteristics of forest degradation.
The use of crop evapotranspiration data has allowed the estimation of crop water requirements and consumptive groundwater use.