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Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hotspot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of a mountainside that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hotspot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of a mountainside that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites.

The need for reliable landscape-scale monitoring of forest disturbance has grown with increased policy and regulatory attention to promoting the climate benefits of forests. Change detection algorithms based on satellite imagery can address this need but are largely untested for the forest types and disturbance regimes of the US Northeast, including management practices common in northern hardwoods and mixed hardwood-conifer forests. This study ground-truthed the "off-the-shelf" outputs of three satellite-based change detection algorithms using detailed harvest records and maps covering 43,000 ha of working forests in northeastern New York. [ABSTRACT FROM AUTHOR]

In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10−20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.

Abstract: The structure of a landscape is highly relevant for research and planning (such as fulfilling the requirements of the Water Framework Directive – WFD – and for implementation of comprehensive catchment planning). There is a high potential for restoration of linear landscape elements in most European landscapes. By implementing the WFD in Germany, the restoration of linear landscape elements could be a valuable measure, for example to reduce nutrient input into rivers. Despite this importance of landscape structures for water and nutrients fluxes, biodiversity and the appearance of a landscape, specific studies of the linear elements are rare for larger catchment areas. Existing studies are limited because they either use remote sensing data, which does not adequately differentiate all types of linear landscape elements, or they focus only on a specific type of linear element. To address these limitations, we developed a framework allowing comprehensive quantification of linear landscape elements for catchment areas, using publicly available biotope type data. We analysed the dependence of landscape structures on natural regions and regional soil characteristics. Three data sets (differing in biotopes, soil parameters and natural regions) were generated for the catchment area of the middle Mulde River (2700km2) in Germany, using overlay processes in geographic information systems (GIS), followed by statistical evaluation. The linear landscape components of the total catchment area are divided into roads (55%), flowing water (21%), tree rows (14%), avenues (5%), and hedges (2%). The occurrence of these landscape components varies regionally among natural units and different soil regions. For example, the mixed deciduous stands (3.5m/ha) are far more frequent in foothills (6m/ha) than in hill country (0.9m/ha). In contrast, fruit trees are more frequent in hill country (5.2m/ha) than in the cooler foothills (0.5m/ha). Some 70% of avenues, and 40% of tree rows, are discontinuous; in contrast, only 20% of hedges are discontinuous. Using our innovative framework, comprehensive information about landscape elements can now be obtained for regional applications. This approach can be applied to other regions and is highly relevant for landscape planning, erosion control, protection of waters and preservation of biotopes and species. [Copyright &y& Elsevier]

LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applications. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three different approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2)~incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spectroradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community. [ABSTRACT FROM PUBLISHER]

Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km2 of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes. [ABSTRACT FROM AUTHOR]

The worldwide demand for timely and accurate information about ecosystem dynamics at Landsat spatial scale is growing and as of today still exceeds the availability of information. The diversity of required disturbance metrics and trade-offs between sensitivity, reliability, timelines of information generation, and flexibility toward potential customizations suggests that a single system is not likely to fill such demand in the near future. To address this challenge, the scientific community has been developing and improving various Landsat-based algorithms for land change monitoring. We describe the Ecosystem Disturbance and Recovery Tracker (eDaRT) version 2.9 — a highly automated prototype system in continuous development, which has been operated since 2012 by the USDA Forest Service Pacific Southwest Region to generate most current disturbance maps at Landsat scale and provide customized information services and inputs to science and land management applications in the Region. The eDaRT processing system utilizes all three dimensions of dense Landsat image time series: spectral, temporal, and spatial. Two anomaly detection algorithms are sequentially applied, one estimating pixels' disturbance status metrics in every processed image and the other detecting disturbance events , the primary output of eDaRT. The first algorithm initially estimates change relative to a user-defined fixed baseline time period, using a stratified version of the Dynamic Detection Model (DDM; Koltunov et al., 2009) applied to Landsat bands and vegetation indexes that reflect canopy greenness, abundance, and moisture content. Using the model residuals and a probabilistic context analysis, the detected anomalies are further classified as disturbed, cloud/snow, or recovered. The resulting residuals, classification maps, and the associated disturbance confidence values provide the most rapid preliminary snapshot of the current cumulative effect of disturbance and regeneration. The second algorithm detects discrete disturbance events as regime changes in the dense time series of the residuals for each pixel. First, the residuals are compared against a recent baseline window and classified to find candidates for disturbance events. Next, candidate-events are accepted based on temporal consistency of their detection. The standard outputs from this algorithm include disturbance event timing (down to 8–16 day precision) and a detection confidence as a proxy for event magnitude. We initially evaluated eDaRT performance with high-resolution imagery and airborne LiDAR data in a test area in California for several types of annual disturbance events at 30-m scale. These tests modeled detection probabilities as functions of canopy cover loss, which estimated detection rates of 96%, 87% and 92% respectively for fire, harvest, and tree mortality events, when canopy loss values follow a uniform prior distribution. The error of commission varied between 10- 20% for most forest types (12% on average). Following ongoing optimizations and extended validations, eDaRT will expand beyond Landsat instruments to improve ecosystem monitoring, as an independent system and potentially as a part of an ensemble method. • eDaRT maps disturbance events, status in California to inform forest managers. • Works on dense image time series from Landsat satellites. • Processing data along multispectral, contextual, and temporal dimensions. • eDaRT 2.9 system and algorithms are presented. • High performance observed in tests with 35,000+ reference samples. [ABSTRACT FROM AUTHOR]

In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape's ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas. [ABSTRACT FROM AUTHOR]

The availability of Landsat data allows improving the monitoring and assessment of large-scale areas with land cover changes in rapid developing regions. Thus, we pretend to show a combined methodology to assess land cover changes (LCCs) in the Hamoun Wetland region (Iran) over a period of 30-year (1987-2016) and to quantify seasonal and decadal landscape and land use variabilities. Using the pixel-based change detection (PBCD) and the post-classification comparison (PCC), four land cover classes were compared among spring, summer, and fall seasons. Our findings showed for the water class a higher correlation between spring and summer (R2 = 0.94) than fall and spring (R2 = 0.58) seasons. Before 2000, ~ 50% of the total area was covered by bare soil and 40% by water. However, after 2000, more than 70% of wetland was transformed into bare soils. The results of the long-term monitoring period showed that fall season was the most representative time to show the inter-annual variability of LCCs monitoring and the least affected by seasonal-scale climatic variations. In the Hamoun Wetland region, land cover was highly controlled by changes in surface water, which in turn responded to both climatic and anthropogenic impacts. We were able to divide the water budget monitoring into three different ecological regimes: (1) a period of high water level, which sustained healthy extensive plant life, and approximately 40% of the total surface water was retained until the end of the hydrological year; (2) a period of drought during high evaporation rates was observed, and a mean wetland surface of about 85% was characterized by bare land; and (3) a recovery period in which water levels were overall rising, but they are not maintained from year to year. After a spring flood, in 2006 and 2013, grassland reached the highest extensions, covering till more than 20% of the region, and the dynamics of the ecosystem were affected by the differences in moisture. The Hamoun wetland region served as an important example and demonstration of the feedbacks between land cover and land uses, particularly as pertaining to water resources available to a rapidly expanding population. [ABSTRACT FROM AUTHOR]