Spatiotemporally improving the rural access index - a remote sensing based approach

Many countries, especially in the global south still lack the ability to effectively pursue basic policies, which can lead, in the worst case, to armed conflicts. Access to markets is a key factor for economic growth and an important component in reducing poverty. The SDG 9.1.1 addresses the proportion of the rural population who live within 2km of an all-season road, which can be mapped by the Rural Access Index (RAI), introduced by the World Bank in 2006. This requires the road network of so-called all season roads, population distribution and rural areas. We developed a fully automated approach, using remote sensing and other open source data to calculate the RAI on an annual basis between 2013 and 2020 for the Lake Chad region. We achieved an overall accuracy between 97.0% and 97.5% in detecting all-season roads using a Random Forest classification. Our method shows similar results to those published by the World Bank. However, our approach provides a higher spatial and temporal resolution measuring the RAI compared to previous studies and is independent of field studies

Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola

Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes. We compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources. View Full-Tex

Aggregation of heterogeneous data sources for the early acquisition of satellite images in case of flood events

Satellite-based emergency mapping is crucial for supporting local disaster response in anticipation of, during and following a disaster. With the increasing frequency of natural disasters like floods and the large availability of web-based and remotely sensed data, there is an opportunity to automate and optimize rapid mapping processes. Early tasking of satellites has already enhanced the timeliness of crisis information products. In this context, ongoing research focuses on automatically deriving potentially affected Areas Of Interest (AOI) by monitoring and aggregating heterogeneous data sources such as official alerts, and remote sensing-based data using a Discrete Global Grid System (DGGS). These AOIs can initiate the early tasking of very-high resolution satellites

Ad-hoc situational awareness during floods using remote sensing data and machine learning methods

Recent advances in machine learning and the rise of new large-scale remote sensing datasets have opened new possibilities for automation of remote sensing data analysis that make it possible to cope with the growing data volume and complexity and the inherent spatio-temporal dynamics of disaster situations. In this work, we provide insights into machine learning methods developed by the German Aerospace Center (DLR) for rapid mapping activities and used to support disaster response efforts during the 2021 flood in Western Germany. These include specifically methods related to systematic flood monitoring from Sentinel-1 as well as road-network extraction, object detection and damage assessment from very high-resolution optical satellite and aerial images. We discuss aspects of data acquisition and present results that were used by first responders during the flood disaster

Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola

Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes. We compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources

Demand-driven data services for humanitarian aid

In a crisis event, humanitarian aid organisations often do not have the latest spatial information at the required scale, which they would urgently need for many decision-making assessments. In the project "Demand-driven Data Services for Humanitarian Aid" (Data4Human), remote sensing data and other data sources are to be analysed in order to identify they possible use in the humanitarian context. The topics were jointly defined by aid organisations and DLR scientists and include current technical aspects such as artificial intelligence, the use of web-based data sources or the creation of dynamic interfaces for data exchange. The areas of application range from disaster control and food security to the violation of human rights, and are adapted to the needs of specific users