以下辽河平原为研究对象,选取1980年、2010年和2018年Landsat TM/ETM+OLI卫星图像进行解译,得到3期土地利用数据,定量分析下辽河平原近40年土地利用时空变化特征.结合地形、交通通达度及限制转化因子采用FLUS(Future Land Use Simulation)模型对流域未来土地利用变化情景及景观格局进行了预测.结果表明:1980-2018年水田、林地、草地、沼泽面积均减少,其中水田面积减少量最大,占比减少了8.59％,旱地、水域和城镇的面积均有所上升,旱地的增长面积最大,占比增加了6.19％;水田、林地、水域转入为旱地面积最大,旱地转出为建设用地面积最大;1980-2018年景观格局发生了较大变化,景观的破碎化程度降低,斑块之间的连通度、聚集程度升高,土地利用的集约化程度增大;2018-2040年,下辽河平原建设用地和水田的变化面积最大,城市化过程更加显著,景观的多样性及空间异质性降低,人类对环境的干扰能力变大. 相似文献
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
Methane content in coal seam is an essential parameter for the assessment of coalbed gas reserves and is a threat to underground coal mining activities. Compared with the adsorption-isotherm-based indirect method, the direct method by sampling methane-bearing coal seams is apparently more accurate for predicting coalbed methane content. However, the traditional sampling method by using an opened sample tube or collecting drill cuttings with air drilling operation would lead to serious loss of coalbed methane in the sampling process. The pressurized sampling method by employing mechanical-valve-based pressure corer is expected to reduce the loss of coalbed methane, whereas it usually results in failure due to the wear of the mechanical valve. Sampling of methane-bearing coal seams by freezing was proposed in this study, and the coalbed gas desorption characteristics under freezing temperature were studied to verify the feasibility of this method. Results show that low temperature does not only improve the adsorption velocity of the coalbed gas, but also extend the adsorption process and increase the total adsorbed gas. The total adsorbed methane gas increased linearly with decreasing temperature, which was considered to be attributed to the decreased Gibbs free energy and molecular average free path of the coalbed gas molecular caused by low temperature. In contrast, the desorption velocity and total desorbed gas are significantly deceased under lower temperatures. The process of desorption can be divided into three phases. Desorption velocity decreases linearly at the first phase, and then, it shows a slow decreases at the second phase. Finally, the velocity of desorption levels off to a constant value at the third phase. The desorbed coalbed gas shows a parabolic relation to temperature at each phase, and it increases with increasing temperature at the first phase, and then, it poses a declining trend with increasing temperature at the rest phases. The experimental results show that decreasing the system temperature can restrain desorption of coalbed methane effectively, and it is proven to be a feasible way of sampling methane-bearing coal seams.
