9 results about "Cover-abundance" patented technology

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The application discloses a kind of transmission and transformation engineering area vegetation recovery evaluation method and system, applied to electric power engineering vegetation evaluation technical field, method includes obtaining the point cloud data and remote sensing image of target transmission and transformation engineering area;Point cloud data is processed to obtain canopy height model, each pixel in remote sensing image is identified using canopy height model, to obtain vegetation pixel set and soil pixel set;According to the reflectivity value of each pixel in vegetation pixel set and soil pixel set, the vegetation coverage of target transmission and transformation engineering area is calculated;The vegetation structure parameter and spectral vegetation index of each vegetation pixel are calculated, based on each vegetation structure parameter and each spectral vegetation index, to obtain vegetation biomass map;Using point cloud data and remote sensing image to calculate to obtain heterogeneity index map, based on vegetation coverage, vegetation biomass map and heterogeneity index map, to obtain vegetation recovery evaluation result, the accuracy of vegetation recovery condition evaluation is effectively improved by the method.

This invention discloses a method for jointly regulating water resource utilization of desert riverbank vegetation based on ecological gate groups. The method includes: obtaining vegetation change patterns using a fitting method based on the kNDVI vegetation index; dividing the vegetation in the desert watershed into three vegetation distribution zones based on the vegetation change patterns and vegetation coverage; acquiring all ecological gates in the desert watershed and dividing them into multiple ecological gate groups; determining the weights of the three vegetation distribution zones for ecological protection objectives using the analytic hierarchy process (AHP), and calculating the comprehensive score of the regulation zone based on the proportion of ecological functional zones in the regulation zone; solving the joint model of the ecological gate groups using a single-objective genetic algorithm to obtain the ecological water demand and ecological water supply of each control zone when the total water shortage of all ecological gate groups is minimized; dividing the ecological gate groups into five levels based on the comprehensive score, and then activating the ecological gate groups to irrigate the irrigation area according to the ecological gate group level, ecological water demand, and the importance of the irrigation area.

The application discloses a degraded grassland ecological restoration method and system based on ecological water demand inside and outside rivers and lakes, and comprises the following steps: conducting field investigation, analyzing the main characteristics of the degraded grassland to divide the degradation grade area, wherein the main characteristics include but are not limited to vegetation coverage, species diversity index and soil bulk density; carrying out ecological unit grid division based on terrain self-adaption; constructing a multi-objective function and constraint condition of the degraded grassland ecological restoration based on the ecological water demand inside and outside rivers and lakes according to water resource allocation, grassland restoration, grazing management and economic benefits; on the basis of the ecological water demand inside and outside rivers and lakes and in combination with the specific grassland degradation grade of the degraded area, the multi-objective function and the constraint condition are used to carry out restoration priority ranking, dynamic adjustment strategy and water resource dynamic adjustment, so that an optimal degraded grassland ecological restoration scheme is obtained; the application is a comprehensive ecological restoration scheme combining water resource utilization, ecological water demand, grazing management and economic benefits, and is helpful to promoting sustainable restoration of the grassland.

This invention discloses an intelligent monitoring method for vegetation in spoil heaps based on optimal layout, belonging to the field of open-pit mine ecological restoration monitoring technology. This invention employs spatial variogram and simulated annealing algorithms to achieve optimal monitoring point layout; it automatically acquires multi-angle images and videos using adjustable supports, and performs keyframe extraction, multi-angle registration, and adaptive denoising preprocessing; the images are input into a lightweight dual-stream feature fusion network, and vegetation areas are accurately segmented through RGB and NDVI dual-stream attention fusion and combined loss function optimization; based on the segmentation results and environmental factors, vegetation coverage, plant height, and tiller number are automatically calculated; the time series is decomposed using STL, and the logistic growth model is integrated into the LSTM network as a physical constraint to output a growth trend prediction that conforms to biological laws. This invention achieves fully automated monitoring from layout to prediction, significantly improving the accuracy and efficiency of monitoring reclaimed grassland in spoil heaps.

The application discloses a kind of based on big data analysis's mine area subsidence area landscape ecological risk early warning method, comprising: by synthetic aperture radar interferometry obtains and processes subsidence time sequence, extracts cumulative amount and rate parameter and divides subsidence grade area;Based on the area, obtain landscape type and species distribution data, construct hierarchical response model to obtain fragile feature description;Accordingly, extract vegetation coverage and land use conversion law, assess habitat quality trend and generate ecological risk grade;When risk exceeds threshold, trigger early warning and dynamically adjust model parameters, through iterative optimization sensitive grading scheme;Finally, based on time series analysis, determine long-term degradation trend and risk path, form landscape ecological risk assessment file.The application realizes the whole process dynamic early warning from ground deformation to landscape ecological risk, improves the accuracy and timeliness of mine area landscape ecological risk identification, and provides technical basis for ecological protection and sustainable management.

This invention discloses a method for measuring soil saturated hydraulic conductivity, belonging to the field of soil testing. The method involves acquiring remote sensing images of the area to be measured, and collecting vegetation cover data and slope data from these images, while simultaneously collecting ground temperature data in the field. The vegetation cover data, slope data, and ground temperature data are preprocessed to obtain vegetation cover correction factors, slope correction factors, and ground temperature correction factors for the area to be measured. An optimized coefficient for measuring soil saturated hydraulic conductivity is then constructed. This optimized coefficient is used to adjust the baseline value of soil saturated hydraulic conductivity, enabling more accurate measurement of soil saturated hydraulic conductivity and providing more precise guidance for agricultural production.

This invention discloses a remote sensing identification method for debris flows in steep mountainous areas based on a vegetation restoration model, belonging to the field of geological disaster remote sensing identification technology. The invention includes acquiring multi-source remote sensing data of the target area; calculating the theoretical vegetation restoration potential and remaining restoration potential index for each pixel; removing vegetation canopy interference to construct a realistic surface model; performing micro-topographic enhancement processing on the realistic surface model; identifying and extracting historical debris flow disaster datasets based on debris flow depositional landform characteristics; performing overlay analysis on the remaining restoration potential index and the historical debris flow disaster datasets to establish a quantitative correspondence between the remaining restoration potential index and vegetation cover; determining the evolutionary stage and occurrence age of historical debris flows; and forming historical debris flow identification results that combine spatial location and temporal attributes. This invention, by constructing a nonlinear evolutionary model of vegetation cover and remaining restoration potential index, can quantitatively determine the evolutionary stage of historical debris flows.