This article presents a 2017 LiDAR-DEM guided 1-m resolution examination of field-surveyed elevation and soil property variations (5 × 5 m spacings) conducted in 1977 across a hummocky New Brunswick field used fo...This article presents a 2017 LiDAR-DEM guided 1-m resolution examination of field-surveyed elevation and soil property variations (5 × 5 m spacings) conducted in 1977 across a hummocky New Brunswick field used for potato production. This examination revealed that the field incurred minor elevation differences likely due to upslope erosion as revealed through increasing Sand % and CF % with increasing elevation, and increasing Silt % along low-lying areas. Soil moisture, field capacity, permanent wilting and nitrate nitrogen (NO<sub>3</sub>-N) also increased at downslope locations. Directly as well as indirectly, soil pH, ammonium nitrogen (NH<sub>4</sub>-N), Caesium<sup>137</sup> (Cs<sup>137</sup>) and Mehlich-3 extracted Ca, Mg, K, Fe, Mn, Cu, and Zn were likewise affected by topographic location. Factor analyzing these variables led to: 1) a Soil Loss Factor that captured 24% of the textural variations;2) a Soil-Cropping Factor accounting for 16% of the N, P, K, Ca, Mg, Mn variations;3) a Soil Organic Matter (SOM) Factor relating 9% of the in-field variations for SOM, Fe, Zn, Cu to via organo-metal complexation and low NO<sub>3</sub>-N retention. Many of the topographic variations increased or decreased with the metric DEM-projected depth-to-water index (DTW) index. This index was set to 0 along DEM-derived flow channels with minimum upslope flow-accumulation areas of 0.1, 0.25, 0.5, 1 or 4 ha. Among these, the DTW > 4 ha threshold was useful for reproducing the textural variations, while the DTW > 0.25 ha threshold assisted in capturing trends pertaining to moisture retention and elemental concentrations.展开更多
为准确获取云雨天气较多地区的农作物分布信息,以敦化市西北部的黑市乡和额穆镇为研究区,以高分一号(GF-1)卫星宽视场(wide field view,WFV)传感器和哨兵一号(Sentinel-1)为数据源,通过WFV数据提取耕地范围,利用J-M(Jeffries-Matusita)...为准确获取云雨天气较多地区的农作物分布信息,以敦化市西北部的黑市乡和额穆镇为研究区,以高分一号(GF-1)卫星宽视场(wide field view,WFV)传感器和哨兵一号(Sentinel-1)为数据源,通过WFV数据提取耕地范围,利用J-M(Jeffries-Matusita)距离可分性进行Sentinel-1影像最优分类时相组合的判断,根据选取Sentinel-1数据通过最近邻分类器多次循环对研究区主要农作物的分布信息进行提取。研究结果表明,进行多次循环产生的分类结果优于单次循环结果,循环次数达到5次结果趋于稳定,总体分类精度为84.23%。可见利用Sentinel-1数据进行敦化市农作物分类是可行的,多次循环的最近邻算法有利于获取更精准的农作物分布信息。展开更多
文摘This article presents a 2017 LiDAR-DEM guided 1-m resolution examination of field-surveyed elevation and soil property variations (5 × 5 m spacings) conducted in 1977 across a hummocky New Brunswick field used for potato production. This examination revealed that the field incurred minor elevation differences likely due to upslope erosion as revealed through increasing Sand % and CF % with increasing elevation, and increasing Silt % along low-lying areas. Soil moisture, field capacity, permanent wilting and nitrate nitrogen (NO<sub>3</sub>-N) also increased at downslope locations. Directly as well as indirectly, soil pH, ammonium nitrogen (NH<sub>4</sub>-N), Caesium<sup>137</sup> (Cs<sup>137</sup>) and Mehlich-3 extracted Ca, Mg, K, Fe, Mn, Cu, and Zn were likewise affected by topographic location. Factor analyzing these variables led to: 1) a Soil Loss Factor that captured 24% of the textural variations;2) a Soil-Cropping Factor accounting for 16% of the N, P, K, Ca, Mg, Mn variations;3) a Soil Organic Matter (SOM) Factor relating 9% of the in-field variations for SOM, Fe, Zn, Cu to via organo-metal complexation and low NO<sub>3</sub>-N retention. Many of the topographic variations increased or decreased with the metric DEM-projected depth-to-water index (DTW) index. This index was set to 0 along DEM-derived flow channels with minimum upslope flow-accumulation areas of 0.1, 0.25, 0.5, 1 or 4 ha. Among these, the DTW > 4 ha threshold was useful for reproducing the textural variations, while the DTW > 0.25 ha threshold assisted in capturing trends pertaining to moisture retention and elemental concentrations.
文摘为准确获取云雨天气较多地区的农作物分布信息,以敦化市西北部的黑市乡和额穆镇为研究区,以高分一号(GF-1)卫星宽视场(wide field view,WFV)传感器和哨兵一号(Sentinel-1)为数据源,通过WFV数据提取耕地范围,利用J-M(Jeffries-Matusita)距离可分性进行Sentinel-1影像最优分类时相组合的判断,根据选取Sentinel-1数据通过最近邻分类器多次循环对研究区主要农作物的分布信息进行提取。研究结果表明,进行多次循环产生的分类结果优于单次循环结果,循环次数达到5次结果趋于稳定,总体分类精度为84.23%。可见利用Sentinel-1数据进行敦化市农作物分类是可行的,多次循环的最近邻算法有利于获取更精准的农作物分布信息。
