A review on drone-based data solutions for cereal crops

Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.</p

Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series

Increasing awareness of the issue of deforestation and degradation in the tropics has resulted in efforts to monitor forest resources in tropical countries. Advances in satellite-based remote sensing and ground-based technologies have allowed for monitoring of forests with high spatial, temporal and thematic detail. Despite these advances, there is a need to engage communities in monitoring activities and include these stakeholders in national forest monitoring systems. In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources

Design and Implementation of an Interactive Web-Based Near Real-Time Forest Monitoring System.

This paper describes an interactive web-based near real-time (NRT) forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) NRT forest disturbance detection based on satellite time-series, 3) presentation of forest disturbance data through a web-based application and social media and 4) interaction of the satellite based disturbance alerts with the end-user communities to enhance the collection of ground data. The system is developed using open source technologies and has been implemented together with local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. The results show that the system is able to provide easy access to information on forest change and considerably improves the collection and storage of ground observation by local experts. Social media leads to higher levels of user interaction and noticeably improves communication among stakeholders. Finally, an evaluation of the system confirms the usability of the system in Ethiopia. The implemented system can provide a foundation for an operational forest monitoring system at the national level for REDD+ MRV applications

Study area located in the UNESCO Kafa Biosphere Reserve in the Southern Nations, Nationalities and Peoples Republic (SNNPR) state of southwestern Ethiopia.

<p>Biosphere Reserve zones and location of local expert disturbance reports (deforestation and degradation) and additional reference data (no-change) are shown. The locations of map tiles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147121#pone.0147121.g009" target="_blank">Fig 9</a> are shown as boxes labeled A to D.</p

Maps of deforestation and degradation at four sites.

<p>The probability of deforestation and degradation are shown as red and blue colour maps, respectively. Local expert reports of deforestation (X) or degradation (+) collected between 2012 and 2015 are overlaid on the maps. The base images are SPOT5 images (band 2; 2.5m spatial resolution) acquired between 2009 and 2011. Dark shaded areas represent forest in the SPOT5 image, and light areas are non-forest land cover types (e.g. cropland or wetland). The locations of each tile (A to D) are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147121#pone.0147121.g001" target="_blank">Fig 1</a>.</p

Spectral bands on the Landsat TM, ETM+ and OLI sensors.

<p>Spectral bands on the Landsat TM, ETM+ and OLI sensors.</p

Iterative calibration and validation of change classes.

<p>Boxplots of random forest class probabilities for the deforestation (<i>P</i>(<i>DEF</i>)), degradation (<i>P</i>(<i>DEG</i>)) or no-change (<i>P</i>(<i>NOCH</i>)) computed for <i>in situ</i> data having DEF or DEG labels are shown for the training phase (top panel) and operational phase (bottom panel) of the monitoring activities. The model updating approach is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147121#pone.0147121.g006" target="_blank">Fig 6</a>.</p

Time series over a degraded forest site for four spectral bands: SWIR2, NDVI, NBR and TCW.

<p>The RLM-fitted season-trend model for each segment is shown as a dotted line. Local disturbance photo evidence for this site is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147121#pone.0147121.g003" target="_blank">Fig 3B</a>.</p

Photo evidence from local disturbance reports documenting deforestation (A) and degradation (B).

<p>The location shown in panel A corresponds to the time series shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147121#pone.0147121.g004" target="_blank">Fig 4</a>, and the location shown in panel B corresponds to the time series shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147121#pone.0147121.g005" target="_blank">Fig 5</a>.</p

Flowchart of methods used in this study.

<p>Processes are shown as rectangles and data and results are shown as parallelograms.</p