Spatio-temporal evolutional characteristics of landscape patterns in the Loess Plateau in China — A landscape metrics-based assessment

Author(s):  
Liying Guo ◽  
Liping Di
2018 ◽  
Vol 42 (4) ◽  
Author(s):  
Rodrigo Nogueira Martins ◽  
Selma Alves Abrahão ◽  
Danilo Pereira Ribeiro ◽  
Ana Paula Ferreira Colares ◽  
Marco Antonio Zanella

ABSTRACT The aim of this study was to quantify the spatio-temporal changes in land use/ cover (LULC), as well as analyze landscape patterns over a 20-year period (1995 - 2015) in the Catolé watershed, northern Minas Gerais State, using landscape metrics. The LULC maps were obtained using Landsat 5 and 8 data (Processing level 1) through supervised classification using the maximum likelihood classifier. Seven thematic classes were identified: dense vegetation, sparse vegetation, riparian vegetation, cropland, planted forest, bare soil, and water. From the LULC maps, classes related to the natural landscape (dense, sparse, and riparian vegetation) were grouped into forest patches, which was then ordered by size: very small (< 5 ha); small (5 - 10 ha); medium (10 - 100 ha); large (100 ha); and a general class (no distinction of patch size). Then, metrics of area, size and density, edge, shape, proximity and core area were calculated. The dense vegetation portion of the study area decreased considerably within a given time, while the portion of cropland and bare soil increased. Overall, in the Catolé river basin, the total area of natural vegetation decreased by 3,273 hectares (4.62%). Landscape metrics analysis exhibited a reduction in the number of very small patches, although the study area was still considered as fragmented. Moreover, a maximum edge distance of 50 m is suggested for conducting studies involving core area metrics in the Catolé watershed, as values above this distance would eliminate the very small patches.


2021 ◽  
Author(s):  
Zhihui Wang ◽  
Peiqing Xiao

&lt;p&gt;&lt;strong&gt;Conversion of cropland to forest/grassland has become a key ecological restoration measure on the Loess Plateau since 1999. Accurate mapping of the spatio-temporal dynamic information of conversion from cropland into forest/grassland is necessary for studying the effects of vegetation change on hydro-ecological process and soil and water conservation on the Loess Plateau, China. Currently, the accuracy of change detection of farmland and forest/grassland at 30-m scale in this area is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, an innovative method for continuous change detection of cropland and forest/grassland using all available Landsat time-series data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% &amp;#177; 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation.&lt;/strong&gt;&lt;/p&gt;


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