State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, China.
Department of Tourism, School of Tourism and Hospitality, University of Johannesburg, Johannesburg, South Africa.
PLoS One. 2020 Feb 20;15(2):e0229298. doi: 10.1371/journal.pone.0229298. eCollection 2020.
Previously, applications of intensity analysis (IA) on land use and land cover change (LULCC) studies have focused on deviations from uniform intensity (UI) and failed to quantify the reasons behind these deviations. This study presents the application of IA with hypothetical errors that could explain non-uniform LULCC in the context of IA at four-time points. LULCC in the Ashi watershed was examined using Landsat images from 1990, 2000, 2010 and 2014 showing the classes: Urban, water, agriculture, close canopy, open canopy and other vegetation. Matrices were created to statistically examine LULCC using IA. The results reveal that the seeming LULCC intensities are not uniform with respect to the interval, category and transition levels of IA. Error analysis indicates that, hypothetical errors in 13%, 19% and 11.2% of the 2000, 2010 and 2014 maps respectively could account for all differences between the observed gain intensities and the UI; while errors in 12%, 21%, and 11% of the 1990, 2000 and 2010 maps respectively could account for all differences between the observed loss intensities and the UI. A hypothetical error in 0.6% and 1.6% of the 1990 map; 1.5% and 4% of the 2000 map; 1.2% and 2.1% of the 2010 map could explain divergences from uniform transitions given URB gain and AGR gain during 1990-2000, 2000-2010 and 2010-2014 respectively. Evidence for a specific deviation from the relevant hypothesized UI is either strong or weak depending on the size of these errors. We recommend that users of IA concept consider assessing their map errors, since limited ground information on past time point data exist. These errors will indicate strength of evidence for deviations and reveals patterns that increase researcher's insight on LULCC processes.
先前，强度分析（IA）在土地利用和土地覆被变化（LULCC）研究中的应用主要集中在偏离均匀强度（UI）上，而未能量化这些偏离的原因。本研究提出了在四个时间点应用带有假设误差的 IA，以解释非均匀 LULCC。利用 1990 年、2000 年、2010 年和 2014 年的 Landsat 图像，对 Ashi 流域的土地利用和土地覆被变化进行了研究，结果显示有 6 个类别：城市、水、农业、密林、疏林和其他植被。创建矩阵以使用 IA 对 LULCC 进行统计检验。结果表明，在 IA 的区间、类别和转移水平上，看似 LULCC 的强度并不均匀。误差分析表明，2000 年、2010 年和 2014 年地图的假设误差分别为 13%、19%和 11.2%，可以解释观测增益强度与 UI 之间的所有差异；而 1990 年、2000 年和 2010 年地图的假设误差分别为 12%、21%和 11%，可以解释观测损失强度与 UI 之间的所有差异。在 1990 年地图中，0.6%和 1.6%的假设误差；2000 年地图中，1.5%和 4%的假设误差；2010 年地图中，1.2%和 2.1%的假设误差可以解释 1990-2000 年、2000-2010 年和 2010-2014 年 URB 增益和 AGR 增益期间的非均匀转换差异。特定偏离相关假设 UI 的证据强弱取决于这些误差的大小。我们建议 IA 概念的使用者考虑评估他们的地图误差，因为过去时间点数据的地面信息有限。这些误差将表明偏离的证据强度，并揭示增加研究人员对 LULCC 过程的洞察力的模式。
