In order to control severe soil erosion, large-scale ecological restoration programs (ERPs) were undertaken, which greatly increased vegetation cover in the Chinese Loess Plateau. Although this has ...generated positive impacts on soil erosion reduction, the conflicts between water supply and the ERPs in the Loess Plateau remain debatable. The impacts of ERPs and climate change on soil erosion and water supply in the future received little attention. Therefore, the objective of this study is to analyze the potential impacts of ERPs on soil erosion and water yield by 2050 in northern Shaanxi, the Chinese Loess Plateau. Soil erosion and water yield were modelled for 2050 based on land use and land cover (LULC) retrospective datasets and downscaled climate scenarios. We designed three 2050 conservation scenarios (protection, business as usual (BAU), and No LULC change) and compared them to the 2015 baseline. The results indicate that soil erosion under the protection and BAU scenarios showed similar decreasing trends compared with the 2015 baseline. The water yield decreased for all three scenarios: by 28% (No LULC change scenario), 29% (BAU scenario), and 37% (protection scenario), indicating that climate change and ecological restoration are likely to place substantial pressures on water by 2050. Considering the water scarcity and climate scenarios in this region, stabilization of the vegetation cover at the 2015 levels may best support soil and water conservation in the future in northern Shaanxi. This study is expected to provide insights for decision-making to develop optimal soil and water conservation strategies in the semi-arid environment in China.
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•Water-related ecosystem services were evaluated between 1988 and 2050.•Soil erosion decreased after the implementation of ecological restoration programs.•Climate change will be the major driver of water yield decline by 2050.•Large-scale afforestation practices could accelerate water yield decline by 2050.•Current level of vegetation cover (2015) could support water and soil conservation.
The specific requirements of UAV-photogrammetry necessitate particular solutions for system development, which have mostly been ignored or not assessed adequately in recent studies. Accordingly, this ...paper presents the methodological and experimental aspects of correctly implementing a UAV-photogrammetry system. The hardware of the system consists of an electric-powered helicopter, a high-resolution digital camera and an inertial navigation system. The software of the system includes the in-house programs specifically designed for camera calibration, platform calibration, system integration, on-board data acquisition, flight planning and on-the-job self-calibration. The detailed features of the system are discussed, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The developed system is extensively tested for precise modeling of the challenging environment of an open-pit gravel mine. The accuracy of the results is evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy are assessed. The experiments demonstrated that 1.55 m horizontal and 3.16 m vertical absolute modeling accuracy could be achieved via direct geo-referencing, which was improved to 0.4 cm and 1.7 cm after indirect geo-referencing.
Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification ...of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.
We examined the use of ecosystem service (ES) approaches at the landscape scale to assess two major ecological restoration programs (ERPs) in China. We found 68 papers highlighting four aspects: (1) ...most papers considered only one ES, and all ES categories were not covered equally; (2) regional-scale and short-term assessments dominated the reviewed papers, and few papers evaluated the impacts of ERPs on ESs at multiple spatial scales; (3) the majority of datasets were from global and national databases; and (4) 40% of studies used mostly proxy models and did not report model validation. Finally, we identify four needs: (1) for a deeper understanding of the interactions between multiple ESs; (2) to establish multiple temporal and spatial scales on ERP assessments including future scenarios and balanced efforts of ERP assessments over the entire territory; (3) to establish multiple data scales; and (4) to develop robust modeling approaches.
Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete ...spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass FM and dry biomass DM, respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Unmanned aerial vehicles have become popular platforms for remote-sensing applications, particularly when spaceborne technology, manned airborne techniques, and in situ methods are not as efficient ...for various reasons. These reasons include the temporal and spatial data resolutions, accessibility over time and space, cost efficiency, and operational safety. Given that most commercial developers tend to focus on the hardware development of unmanned aerial systems, less attention is paid to the development and evaluation of their data processing techniques. Therefore, critical reviews of previous studies are required to describe the current state of research using data from unmanned remote sensing platforms. Accordingly, this article presents the results of a comprehensive review of applications of unmanned aerial imagery for the management of agricultural and natural resources. This review attempts to demonstrate that developing robust methodologies and reliable assessments of results are significant issues for successful applications of unmanned aerial imagery.
Over the last few years, several of the world's national park networks have implemented ecological integrity monitoring programs. These programs are based on a series of indicators to detect changes ...in ecosystem integrity. There are many scientific and logistical challenges in developing these programs due to limits in both our knowledge of ecosystems functioning and the resources for implementing such programs. Thus, the relatively quick and simple implementation of many monitoring programs has been to the detriment of their scientific validity. Few studies have focused on evaluating an entire monitoring program. This project presents an approach to evaluate the ecological and statistical relevance of ecosystem integrity indicators measured within a program with the goal of iterative optimization. The approach is based on three complementary elements: (1) spatial characterization of park ecosystems based on the classification of satellite imagery, (2) ecological validation of indicators based on ecosystem conceptual models and (3) statistical validation of indicators based on analyses of statistical power. This innovative approach allows a systematic, quantified, reproducible and generalizable review of the indicators of an ecological integrity monitoring program. It provides managers with an overview of the spatial representativeness of indicators, their ecological and statistical relevance according to different parameters such as the period monitored, the amount of change to be detected, and the degree of significance. Thus, the approach identifies monitoring gaps and offers various alternatives for improving sampling. The approach was developed and tested in the network of Quebec national parks, more specifically in the Frontenac, Jacques-Cartier and Bic national parks. The results clearly identify the strengths and weaknesses of the current program in place and possible improvements are proposed for these parks. This approach is a relevant tool for park networks, particularly for those that have limited resources for monitoring ecological integrity.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Buckthorns (Glossy buckthorn, Frangula alnus and common buckthorn, Rhamnus cathartica) represent a threat to biodiversity. Their high competitivity lead to the replacement of native species and the ...inhibition of forest regeneration. Early detection strategies are therefore necessary to limit invasive alien plant species' impacts, and remote sensing is one of the techniques for early invasion detection. Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images. Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images. The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area. Three machine learning classifiers (Support Vector Machines, Random Forest and Extreme Gradient Boosting) were applied to WorldView-3, GeoEye-1 and SPOT-7 satellite imagery. The Random Forest classifier performed well (Kappa =
0
.72). The SVM and XGBoost's coefficient Kappa were 0.69 and 0.66, respectively. However, buckthorn distribution in understory was identified as the main limit to this approach, and LiDAR data could be used to improve buckthorn mapping in similar environments.
Wetlands are affected by climate and anthropogenic changes, which influence the ecosystem services (ES) they provide. This study presents a spatially explicit quantification of wetland ESs. The study ...site is the Yamaska river watershed located in Quebec, Canada. The proposed approach includes four main steps: (1) statistical selection of function indicators (FI) to build a composite ecosystem service indicator (ESI); (2) temporal land use mapping for past (1984), recent (2011) and future scenarios (2050); (3) mapping and quantification of FIs and ESIs at all temporal and spatial scales; and (4) synthesis of multispatial and multitemporal information using a diagram representation.
Results present the spatiotemporal evolution of the ES on maintaining habitat provided by wetlands in the studied watershed. The historical characterization shows a general degradation of this service on the entire study area for the last 30 years. The proposed approach can target priority sectors in which this service has deteriorated or is lacking. Future scenarios show the urgency to act in order to preserve current intact areas, because even the optimistic scenario indicates that the studied ES would not return to its 1984 state. The proposed approach allows a spatiotemporal mapping of ESs combined with a visualization of their ecological, social, and economic components in a context of territorial management scenarios. This multi-scale method is reproducible, robust and can be replicated for other ESs in different territories.
•Wetland ecosystem services (ES) are degrading all over the world.•Spatially explicit quantification of ES remains under-explored.•A systematic and reproducible approach to map wetland ES is proposed.•The spatiotemporal analysis of wetland ES can be used to guide conservation priorities.
Invasive alien plant species (IAPS) have negative impacts on ecosystems, including the loss of biodiversity and the alteration of ecosystem functions. The strategy for mitigating these impacts ...requires knowledge of these species' spatial distribution and level of infestation. In situ inventories or aerial photo interpretation can be used to collect these data but they are labor-intensive, time-consuming, and incomplete, especially when dealing with large or inaccessible areas. Remote sensing may be an effective method of mapping IAPS for a better management strategy. Several studies using remote sensing to map IAPS have focused on single species detection and were conducted in relatively homogeneous natural environments, while other common, more heterogeneous environments, such as urban areas, are often invaded by multiple IAPS, posing management challenges. The main objective of this study was to develop a mapping method for three major IAPS observed in the urban agglomeration of Quebec City (Canada), namely Japanese knotweed (Fallopia japonica); giant hogweed (Heracleum mantegazzianum); and phragmites (Phragmites australis). Mono-date and multi-date classification approaches were used with WorldView-3 and SPOT-7 satellite imagery, acquired in the summer of 2020 and in the autumn of 2019, respectively. To estimate presence probability, object-based image analysis (OBIA) and nonparametric classifiers such as Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were used. Overall, multi-date classification using WorldView-3 and SPOT-7 images produced the best results, with a Kappa coefficient of 0.85 and an overall accuracy of 91% using RF. For XGBoost, the Kappa coefficient was 0.81 with an overall accuracy of 89%, whereas the Kappa coefficient and overall accuracy were 0.80 and 88% for SVM classifier, respectively. Individual class performances based on F1-score revealed that Japanese knotweed had the highest maximum value (0.95), followed by giant hogweed (0.91), and phragmites (0.87). These results confirmed the potential of remote sensing to accurately map and simultaneously monitor the main IAPS in a heterogeneous urban environment using a multi-date approach. Although the approach is limited by image and reference data availability, it provides new tools to managers for IAPS invasion control.
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