Fire Dynamics and Woody Cover Changes in the Serengeti-Mara Ecosystem 2000 to 2005 - A Remote Sensing Approach

The Serengeti-Mara savanna environment in East Africa is characterized by changing levels of woody cover and a dynamic fire regime. The relative proportion of woodland to grassland savanna affects animal habitat, biodiversity, and carbon storage, and is regulated by factors such as the fire regime (frequency, intensity, seasonality), and precipitation. The main objectives of this dissertation are to determine recent changes in woody cover at a regional scale and identify fire regimes and climate associated with these changes. Understanding these relationships is important for the assessment of future trajectories of woody cover under changing climate. Required spatially coherent data layers can only be obtained at the regional scale through the analysis of remote sensing data. Woody cover changes between 2000 and 2005 were derived from field data and a time series of MODIS satellite imagery at 500 m spatial resolution. Data layers on the controlling variables (fire frequency, seasonality, intensity and rainfall) were developed using a combination of remote sensing and model-based approaches. Burned areas were mapped using daily MODIS imagery at 250 m resolution. Outputs were used to make the requisite layers depicting fire frequency and seasonality. Fire intensity was derived using a model based on empirical relationships, mainly estimating fire fuel load as a function of rainfall and grazing. The combined data layers were analyzed using regression and decision tree techniques. Results suggest woody cover in central and northern Serengeti National Park continued to increase after 2000. Woody cover decreases were strongest in the wider Maswa Game Reserve area (MSW) under low precipitation conditions and late season burning. Woody cover losses in burned areas were also higher in the low fire frequency region of the Maasai Mara National Reserve (MNR). Fire seasonality was the most important fire regime parameter controlling woody cover in burned woodland savanna areas while fire intensity was most relevant for grassland savanna areas. Continued late season burning in drought years might cause further decrease of woody cover in MSW. MNR is expected to continue to be dominated by grassland savanna at similar fire frequency and browsing levels

UAV-Based forest health monitoring : a systematic review

CITATION: Ecke, S. et al. 2022. UAV-Based forest health monitoring : a systematic review. Remote Sensing, 14(13):3205, doi:10.3390/rs14133205.The original publication is available at https://www.mdpi.comIn recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.https://www.mdpi.com/2072-4292/14/13/3205Publisher's versio

Measurement of Within-Season Tree Height Growth in a Mixed Forest Stand Using UAV Imagery

Tree height growth measurements at monthly and annual time scales are important for calibrating and validating forest growth models, forest management and studies of forest ecology and biophysical processes. Previous studies measured the terminal growth of individual trees or forest stands at annual or decadal time scales. Short-term, within-season measurements, however, are largely unavailable due to technical and practical limitations. Here, we describe a novel approach for measuring within-season tree height growth using a time series of co-registered digital surface models obtained with a low-cost unmanned aerial vehicle in combination with ground control plates and Structure from Motion data processing. The test site was a 2-hectare temperate mixed forest stand of varying age and successional stage in central Europe. Our results show median growth rates between 27 May and 19 August of 68 cm for Norway spruce, 93 cm for Scots pine, 106 cm for Silver birch and 26 cm for European beech. The results agree well with published field observations for these species. This study demonstrates the capability of inexpensive, increasingly user-friendly and versatile UAV systems for measuring tree height growth at short time scales, which was not previously possible, opening up new avenues for investigation and practical applications in forestry and research

Measurement of Within-Season Tree Height Growth in a Mixed Forest Stand Using UAV Imagery

Tree height growth measurements at monthly and annual time scales are important for calibrating and validating forest growth models, forest management and studies of forest ecology and biophysical processes. Previous studies measured the terminal growth of individual trees or forest stands at annual or decadal time scales. Short-term, within-season measurements, however, are largely unavailable due to technical and practical limitations. Here, we describe a novel approach for measuring within-season tree height growth using a time series of co-registered digital surface models obtained with a low-cost unmanned aerial vehicle in combination with ground control plates and Structure from Motion data processing. The test site was a 2-hectare temperate mixed forest stand of varying age and successional stage in central Europe. Our results show median growth rates between 27 May and 19 August of 68 cm for Norway spruce, 93 cm for Scots pine, 106 cm for Silver birch and 26 cm for European beech. The results agree well with published field observations for these species. This study demonstrates the capability of inexpensive, increasingly user-friendly and versatile UAV systems for measuring tree height growth at short time scales, which was not previously possible, opening up new avenues for investigation and practical applications in forestry and research.https://doi.org/10.3390/f807023

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year\u27s or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts

UAV-Based Forest Health Monitoring: A Systematic Review

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools