Analyzing and predicting the trend of land cover degradation and determining the changes in landscape metrics using remote sensing

Introduction

Land use changes caused by human activities have widespread effects on landscape. Examining and quantifying these changes can be beneficial in planning and sustainable land management. Landscape metrics can be used as a basis for comparing different scenarios of landscape or recognizing changes and developments in landscape over time. The use of landscape metrics, while saving time, makes it possible to assess the environmental impact of human activities in the shortest time. Also, detecting and predicting land use changes provide benefit information about the trend of land development and degradation. The purpose of this research is to investigate land use changes in the past and to predict land use in the future using remote sensing and Land Change Modeller (LCM) and quantitative analysis of these changes based on landscape metrics in Halilrud basin

Materials and Methods

In this study, the data of Landsat 5 Thematic Mapper (TM) sensor (1990), Enhanced Thematic Mapper Plus (ETM+) sensor (2002), Landsat 8 Operational Land Imager (OLI) sensor (2019) were used to evaluate the trend of LULC changes in Halilrud basin. The land use maps were classified into seven land use including dam lake, residential lands, agricultural lands, rock, orchards, rangelands, bare lands. The classification was done based on Maximum Likelihood method. Then, the land use map of 2040 was simulated using land change modeler and artificial neural network. Finally, landscape metrics were calculated at both landscape and class levels using Fragstats 4.2 to quantify structural changes. The metrics used in class level include Class Area (CA), Largest Patch Index (LPI), Percentage of Lands (PLAND), NP (Number of patch). Contagion (CONTAG), Shannon’s Diversity Index (SHDI), Simpson Diversity Index (SIDI), Interspersion juxtaposition index (IJI) and Patch Density (PD) are used to quantity changes in landscape level.

Results and Discussion

The results showed that during the 1990 to 2019, and the future period (2040), the landscape of the studied area has been changed in terms of structure and composition. The significant increase in the area of agricultural, barren and residential lands and a decrease in the area of rangelands are evident in this basin. Shannon's diversity index and Simpson Diversity Index shows an increase in 2019 and 2040 compared to 1990, which shows that the study area has become more fragmented and heterogeneous under the influence of exploitation and human activities. In addition, the value of the patch density index has increased during the study period, which indicates the division of the landscape into smaller parts. The IJI index also has increased during 1990 to 2040, which indicates landscape diversity in the study area. The value of Contagion index has decreased in the study period, which indicates that the patches has been spatially separated from each other. These results are consistent with the study of Shi et al. (2008), et al. del Castillo (2015). The results of the metrics at the class level are in line with the results of the metrics at the level of the landscape. In general, the analysis of landscape metrics has shown the extensive replacement of average rangelands by agricultural, residential lands, orchards and bare lands. According to PLAND and CA metrics, the patches of agriculture, residential land, orchard and bare land have increased and the patches of rangeland and dam lake have decreased during the study periods. The results obtained from the application of the metrics used in the research show the effectiveness of the metrics of Percentage of Lands (PLAND), Class Area (CA), Largest Patch Index (LPI), Patch Number (NP) in examining and analyzing the changes, which is with the findings of the research of Matsushita et al. (2006), Buyantuyev et al. (2009), Azareh et al. (2019). The efficiency of Patch Number (NP) metric has also been proved by Ji (2008), Abdullah and Nakagoshi, (2006), which is consistent with the results of this research. According to the results of the analysis of this metric at the level of the agricultural land class, the LPI metric was initially at the lowest level, and then with the increase of agricultural land, its values increased from 0.86% to 2.26% during 1990 to 2019 and will increase in the next period (2040) and will reach to 3.8%. Also, the rangeland class has faced an increase in the patch number during 1990-2040, and this indicates that the existing patches have fragmented and lost their integrity over time.

Conclusion

With regard to the uncontrolled growth of residential and agricultural lands in recent years, to prevent more degradation and also to preserve rangelands, it is suggested to accomplish land use planning based on the concepts of landscape. The change in the landscape structure has taken in land use types with different degrees, and quantifying these changes using landscape metrics is one of the issues that can help to analyze the pattern of spatial changes. According to the high ability of landscape metrics in quantifying the landscape pattern, the results of these studies can be used in the planning and integrated planning of the landscape.