scholarly journals Corrigendum: Limitations of Remote Sensing in Assessing Vegetation Damage Due to the 2019–2021 Desert Locust Upsurge

2021 ◽  
Vol 3 ◽  
Author(s):  
Emily C. Adams ◽  
Helen B. Parache ◽  
Emil Cherrington ◽  
Walter L. Ellenburg ◽  
Vikalp Mishra ◽  
...  
2005 ◽  
Vol 60 (6) ◽  
pp. 1183-1188 ◽  
Author(s):  
Aki YAMANISHI ◽  
Atsushi TSUNEKAWA ◽  
Yutaka KIYOHARA ◽  
Takashi KAMUO ◽  
Hiroyoshi HIGUCHI

Insects ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 233 ◽  
Author(s):  
Igor Klein ◽  
Natascha Oppelt ◽  
Claudia Kuenzer

Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species—the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)—and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.


2021 ◽  
Vol 308 ◽  
pp. 02005
Author(s):  
Cheng Jin ◽  
Kai Yu ◽  
Ke Zhang

Mountainous vegetation recovery after major earthquakes has been significant for preventing post-seismic soil erosion and geo-hazards. Magnitude 7.9 Wenchuan earthquake struck western Sichuan, China in 2008, caused salient number of geological hazards and caused major vegetation damage. This recovery process could be a very long and fluctuating. And Remote sensing has been an important method of vegetation restoration monitoring. This study aims to use remote sensing technology data to analyze the post-seismic vegetation damage and recovery situation of the 2008 Wenchuan earthquake over years to 2020, and find the relevant factors affecting the restoration of ecological vegetation. This paper examined the vegetation recovery processes following the 2008 Wenchuan earthquake using 16-day interval MODIS normalized difference vegetation index time series from 2000 to 2020. It has been found that the vegetation recovery rate generally increased by years, the entire study area has recovered 49.89% by 2020. In addition, by combining remote sensing imagery and geographic information data, we also found that the heavily affected vegetation areas are mainly located along the southern part of the earthquake surface rupture, where have a very high slope which mainly over 60 degrees. It makes this part having higher probabilities to experiences secondary natural hazards and a fluctuating vegetation recovery rate. Through this research, it can be concluded that remote sensing is an effective method for monitoring vegetation dynamics in a long series. For soil and soil retention and ecological vegetation protection of landslides after the earthquake, it should be more concerned about the areas where have steep slope that over 60 degrees.


Since 1975, the Food and Agriculture Organization of the United Nations (FAO) has been pioneering the development of the use of satellite remote sensing techniques for improving the surveillance and forecasting capabilities of the centralized Desert Locust Reporting and Forecasting Service at FAO Headquarters and, indirectly, those of Regional Organizations and National Plant Protection Services. On the basis of findings from experimental activities on the use of Landsat and NOAA satellite AVHRR data for Desert Locust habitat detection and monitoring through vegetation assessment, and the use of Meteosat data for rainfall monitoring, FAO defined an operational system for satellite environmental monitoring in support of the FAO Desert Locust Plague Prevention Programme and the FAO Global Information and Early Warning System on Food and Agriculture. The system, African Real Time Environmental Monitoring using Imaging Satellites (ARTEMIS) is an advanced computer hardware and software configuration, equipped for direct acquisition of hourly Meteosat digital data and for automated thematic processing of Meteosat and NOAA AVHRR data for large area precipitation and vegetation condition assessment, being the key environmental factors for supporting Desert Locust population development. Since August 1988, the ARTEMIS system has generated a number of operational products documenting the occurrence of rainfall and vegetation development in the Desert Locust recession area on a 10-day and monthly basis at spatial resolutions varying from 7.6-1.1 km. These products are being used by the FAO Emergency Centre for Locust Operations (ECLO), along with synoptic weather and locust data, for the preparation of the bulletins containing the Desert Locust situation summaries and forecasts. For making ARTEMIS output products and other relevant data available in a timely manner at regional and national levels, a dedicated satellite communications system, Data and Information Available Now in Africa (DIANA), is currently being developed by the European Space Agency in cooperation with the FAO Remote Sensing Centre. The DIANA system will, by mid-1991, provide a capability for high speed (64 kb s -1 ) two-way transfer of facsimile images of documents and maps, character- coded text documents and digital images in raw or processed form from computers at FAO Headquarters to personal computer based terminals of recipients, initially in Africa, by using the commercial Intelsat satellites.


2020 ◽  
Vol 12 (21) ◽  
pp. 3593
Author(s):  
Chaoliang Chen ◽  
Jing Qian ◽  
Xi Chen ◽  
Zengyun Hu ◽  
Jiayu Sun ◽  
...  

In history, every occurrence of a desert locust plague has brought a devastating blow to local agriculture. Analyses of the potential geographic distribution and migration paths of desert locusts can be used to better monitor and provide early warnings about desert locust outbreaks. By using environmental data from multiple remote-sensing data sources, we simulate the potential habitats of desert locusts in Africa, Asia and Europe in this study using a logistic regression model that was developed based on desert locust monitoring records. The logistic regression model showed high accuracy, with an average training area under the curve (AUC) value of 0.84 and a kappa coefficient of 0.75. Our analysis indicated that the temperature and leaf area index (LAI) play important roles in shaping the spatial distribution of desert locusts. A model analysis based on data for six environmental variables over the past 15 years predicted that the potential habitats of desert locust present a periodic movement pattern between 40°N and 30°S latitude. The area of the potential desert locust habitat reached a maximum in July, with a suitable area exceeding 2.77 × 107 km2 and located entirely between 0°N and 40°N in Asia-Europe and Africa. In December, the potential distribution of desert locusts reached its minimum area at 0.68 × 107 km2 and was located between 30°N and 30°S in Asia and Africa. According to the model estimates, desert locust-prone areas are distributed in northern Ethiopia, South Sudan, northwestern Kenya, the southern Arabian Peninsula, the border area between India and Pakistan, and the southern Indian Peninsula. In addition, desert locusts were predicted to migrate from east to west between these areas and in Africa between 10°N and 17°N. Countries in these areas should closely monitor desert locust populations and respond rapidly.


Sign in / Sign up

Export Citation Format

Share Document