Research Progress on Changes in Land Use and Land Cover in the Western Himalayas (India) and Effects on Ecosystem Services

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Modern pollen dispersal in relation to present vegetation distribution and land use in the Baspa valley, Kinnaur, western Himalayas

Interpretation of a fossil pollen data for the vegetation and climate reconstruction of any region needs a modern pollen-vegetation analogue for its calibration. We analyzed the surface sediments and moss polsters for the pollen and microcharcoal records to understand the modern pollen-vegetation relationship and human activities in the Baspa Valley, Kinnaur, Himachal Pradesh. Presently, valley is occupied by the arboreal and non-arboreal vegetation of temperate to subalpine habitats and land use activities. The recovered pollen assemblages showed variability in the dispersal behavior of pollen of taxa growing along the valley transect and also captured the signals of human activities over land use. The overall dominance of arboreal pollen in the recovered pollen assemblage corresponds with the dominant growth of conifers and broadleaf tree taxa and represents the valley vegetation at a regional scale. However, the profuse pollen production of a few arboreal taxa and long distance pollen transport from one vegetation zone to other by the strong upthermic valley winds could bias the pollen representation of in-situ vegetation. The high pollen frequency of non-arboreal taxa in the open meadows represents the near vicinity to their plant source. Human activities like fire burning and cultivation by the local population are evident by the recovery of microcharcoal particles and pollen of plants belonging to Cerealia Poaceae, Asteraceae, Amaranthaceae, Polygonaceae, Rosaceae, Juglandaceae, etc. The dataset taken as modern pollen-vegetation analogue is useful to assess past changes in the vegetation and land cover in relation to climate and human factors for future sustenance.

Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling

Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.

Scenario-based analysis of the impacts of lake drying on food production in the Lake Urmia Basin of Northern Iran

In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts by dying lakes are as yet unknown. The food production in the Urmia region is predominantly regional and relies on local water sources. To explore the current and projected impacts of the dying lake on food production, we investigated changes in the climatic conditions, land use, and land degradation for the period 1990–2020. We examined the environmental impacts of lake drought on food production using an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected and reduced food production over the past three decades. Based on a combination of cellular automaton and Markov modeling, we project the food production for the next 30 years and predict it will reduce further. The results of this study emphasize the critical environmental impacts of the Urmia Lake drought on food production in the region. We hope that the results will encourage authorities and environmental planners to counteract these issues and take steps to support food production. As our proposed integrated geoinformation approach considers both the extensive impacts of global climate change and the factors associated with dying lakes, we consider it to be suitable to investigate the relationships between environmental degradation and scenario-based food production in other regions with dying lakes around the world.

Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan

Abstract Here, we investigated the relationship between Plant communities and the environment from the moist temperate vegetation of Lakoo mountainous forest District Swat. We sampled data from 162 sampling units (Quadrates) using 1x1m2 for herbs 5x5m2 and shrubs, while 10 x10m2 for trees, systematically considering six elevation gradients between the altitudinal from 1970m to 3095m. We performed statistical analysis like Canonical correspondence analysis (CCA) and TWINSPAN (two-way indicator species analysis) for ecological assessment and clustering of plant communities. To check upon the correlation of species (CR) with topographic and edaphic variables we used statistical software PC-ORD version 7. We recognized 264 species plants belonging to thirty families. We recorded key sampling measurements of density, frequency, and cover for all these species which are vital for community description. The results showed Shannon-Winner,s, and Simpson diversity values as 19.18 and 3.17 respectively. The importance value indexes (IVI) were used to identify the leading and rare species of plant in each community or cluster group. In total we recognized eleven different communities as: Berberis- Abies- Bergeni, Picea - Indigofera- Poa,Abies- Parrotiopsis- Poa, Quercus- Viburnum- Poa, Picea- Salix- Primula, Abies- Viburnum- Poa, Viburnum- Taxus- Poa, Pinus- Viburnum- Lithospermum, Abies-Berberis- carex, Pinus- Viburnum- Poa and Parrotiopsis- Picea- Poa through hierarchical cluster analysis (TWINSPAN). CCA analysis revealed that of all studied edaphic and topographic variables altitude, silt, calcium carbonate, and organic matter were the strongest factors determining plant community diversity and composition in each microclimate of the eleven communities. Visually the vegetation of the forest was dominated by small-sized trees followed by shrubs, and regenerates indicating the stage of secondary regeneration. We found severe human interference in disturbing the existing biodiversity, which requires immediate conservation to ensure sustainable management and utilization of natural resources of the Lalkoo moist temperate forest.