Research on pastoralism in France: state of knowledge and current issues

Relative importance of climate change and human activities for vegetation changes on China's silk road economic belt over multiple timescales

Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation.

Monitoring changes in vegetation at high-latitude and alpine treeline ecotones is critical for characterizing changes to carbon and energy budgets, plant species richness, and habitat suitability and is often considered a bellwether of a changing climate. Herein, we used transects of airborne laser scanning (ALS) data to identify alpine treeline ecotones in the Yukon Territory of Canada, and assessed changes in vegetation greenness using a time-series of Landsat imagery over a 30 year period from 1985 to 2015. Specifically, we calculated the enhanced vegetation index (EVI) from annual Landsat composites and assessed temporal trends within 500 m of detected forest-lines (i.e., transition point from continuous forest into treeline ecotones) using Theil–Sen’s nonparametric regression. Across 74 detected treeline ecotones, 27.5% of Landsat pixels displayed a significant positive trend in EVI and 5.6% of pixels displayed a significant negative trend (p < 0.05). By using ALS data to determine vegetation structural class, we found that non-treed pixels had the highest percentage of significant positive trends in vegetation greenness (40.8%), followed by shrubs (30.5%), with lower percentages in sparse forests (18.9%) and open/dense forests (13.3%). These results suggest herbaceous and shrub vegetation types are undergoing the most significant changes in greenness, likely due to increases in shrub cover and herbaceous biomass in areas associated with these alpine treeline ecotones. The limited increases in EVI in forests likely indicates that vegetation cover is changing less rapidly in forests than in shrub and herbaceous vegetation types. Moreover, EVI may not be capturing increased height growth in forests near the treeline. Combining ALS data and Landsat time-series data provides a useful approach to locate and characterize alpine treeline ecotones, and enables the direct assessment of which vegetation structural classes are experiencing the greatest greening trends, thereby providing new insights to ecosystem change.

Automatic detection of vegetation cover changes in urban-rural interface areas

This research aims at monitoring changes in vegetation and water covers in the district of Ramadi, for the period 1990 - 2010, by using the technologies of Remote Sensing and Geographic Information Systems (GIS).The researcher made use of three Satellite images for three years1990-2001 -2010 of the types TM, ETM +, for winter seasons of these years. For the purpose of land cover classification, several steps have been carried out namely: A field work has been done to identify the type of land cover, for many locations of the area under study , and determine its coordinates using a (GPS). Application of Supervised Classification was applied to the search area, as well as the equation of manual vegetation NDVI was used to detect changes in vegetation cover and its density. The results indicate that ; the area of water cover was 636 km ² in 1990, decreased to 463 km ² in 2001, and then to 355 km ² in 2010. The vegetation cover has been an area of 282 km ² in 1990, increased to 387 km ² in the year 2001, while becoming an area of 327 km ² in 2010.This was the result of the conditions of foreign occupation of Iraq and the drought conditions of the the region.

With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.

Aim The upland moorlands of Great Britain form distinctive landscapes of international conservation importance, comprising mosaics of heathland, acid grassland, blanket bog and bracken. Much of this landscape is managed by rotational burning to create gamebird habitat and there is concern over whether this is driving long‐term changes in upland vegetation communities. However, the inaccessibility and scale of uplands means that monitoring changes in vegetation and burning practices is difficult. We aim to overcome this problem by developing methods to classify aerial imagery into high‐resolution maps of dominant vegetation cover, including the distribution of burns on managed grouse moors.Location Peak District National Park, England, UK.Methods Colour and infrared aerial photographs were classified into seven dominant land‐cover classes using the Random Forest ensemble machine learning algorithm. In addition, heather (Calluna vulgaris) was further differentiated into growth phases, including sites that were newly burnt. We then analysed the distributions of the vegetation classes and managed burning using detrended correspondence analysis.Results Classification accuracy was c. 95% and produced a 5‐m resolution map for 514 km2 of moorland. Cover classes were highly aggregated and strong nonlinear effects of elevation and slope and weaker effects of aspect and bedrock type were evident in structuring moorland vegetation communities. The classification revealed the spatial distribution of managed burning and suggested that relatively steep areas may be disproportionately burnt.Main conclusions Random Forest classification of aerial imagery is an efficient method for producing high‐resolution maps of upland vegetation. These may be used to monitor long‐term changes in vegetation and management burning and infer species–environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.

A classification scheme is presented for seasonal floodplains of the Boro-Xudum distributary of the Okavango Delta, Botswana. This distributary is subject to an annual flood-pulse, the inundated area varying from a mean low of 3 600 km2 to a mean high of 5 400 km2 between 2000 and 2006. A stratified random sample of 30 sites was surveyed for species composition and abundance in March–June 2007, using multiple quadrats along transects orthogonal to the floodplain long axis. A combination of indicator species analysis and ordination was used to derive a hierarchical classification system for floodplains, based on species assemblages. Indicator species analysis was used to identify ecologically meaningful levels of division, at four and nine classes. The four main classes of floodplain were: (1) Dry Floodplain Grassland (main indicators Urochloa mosambicensis, Ipomoea coptica, Chloris virgata and Pechuel-Loeschea leubnitziae); (2) Seasonally Flooded Grassland (Nicolasia costata, Eragrostis lappula, Cyperus sphaerospermus and Setaria sphacelata); (3) Seasonally Flooded Sedgeland (Eleocharis dulcis, Leersia hexandra, Oryza longistaminata and Cyperus articulatus); and (4) Seasonal Aquatic Communities (Sacciolepis typhura, Eleocharis variegata, Fuirena pubescens and Cycnium tubulosum). The resultant dendrogram provides an objective routine for classifying floodplains in the Boro-Xudum distributary in an ecologically meaningful way. This classification will assist in monitoring changes in vegetation resulting from hydrological change.

Vegetation cover mapping from NOAA/AVHRR

An outline of the vegetation change monitoring program at UNEP/GRID/TSUKUBA is introduced. A satellite image mosaic map and a vegetation index map in South-East Asia were produced from NOAA AVHRR LAC imageries with 1 km spatial resolution to assess vegetation distribution in the region. Also land-cover change detection with multi-temporal AVHRR images was examined for monitoring changes in vegetation on a continental scale. >

Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery

The article focuses on the application of remote sensing methods to assess the impact of human activities on the environment. Emphasis is placed on the need to accurately monitor anthropogenic impacts, such as deforestation and water pollution, to maintain environmental balance. The Materials and Methods section describes passive and active sensing methods, including infrared imaging, radar sensing, and laser scanning. Passive sensing analyzes the spectral characteristics of objects, which is useful for monitoring vegetation changes, while radar sensing can detect oil spills even in adverse weather conditions. Laser sensing is used for precise measurement of terrain changes. The Results and Discussion section provides examples of practical application of these methods: analysis of deforestation in the Kirov region using Landsat 8 and 9 satellite data, and investigation of an oil spill in Novorossiysk using radar images from the Sentinel-1 satellite. These examples demonstrate the effectiveness of remote sensing for monitoring changes and identifying the negative consequences of human activities. The conclusion highlights the importance of remote sensing as a powerful tool for environmental research and monitoring.

Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.

Monitoring vegetation change and their potential drivers are important to environmental management. Previous studies on vegetation change detection and driver discrimination were two independent fields. Specifically, change detection methods focus on nonlinear and linear change behaviors, i.e., abrupt change (AC) and gradual change (GC). But driver discrimination studies mainly used linear coupling models which rarely concerned the nonlinear behaviors of vegetation. The two diagnoses need be treated as sequential flow because they have inner causality mechanisms. Furthermore, ACs concealed in time series may induce over/under-estimate contributions from human. We chose the Yangtze River Basin of China (YRB) as a study area, first separated ACs from GCs using breaks for additive and seasonal trend method, then discriminated drivers of GCs using optimized Restrend method. Results showed that (1) 2.83% of YRB were ACs with hotspots in 1998 (30.2%), 2003 (10.4%), and 2002 (7.6%); 66.7% of YRB experienced GC with 94.8% of which were positive; and (2) climate induced more area but less dramatic GCs than human activities. Further analysis showed that temperature was the main climate driver to GCs, while human-induced GCs were related to local eco-policies. The widely occurring ACs in 1998 were related to the flooding catastrophe, while the dramatic ACs in sub-basin 12 in 2003 may result from urbanization. This paper provides clear insights on the vegetation changes and their drivers at a relatively long perspective (i.e., 34years). Sequential combination of specifying different vegetation behaviors with driver analysis could improve driver characterizations, which is key to environmental assessment and management in YRB.

Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world’s three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is used as an indicator to monitor vegetation changes. GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation changes in Hulun Buir Grassland. The responses of vegetation coverage to climatic change on the yearly, seasonal and monthly scales were analyzed combined with temperature and precipitation data of seven meteorological sites. In the past 30 years, vegetation coverage was more correlated with climatic factors, and the correlations were dependent on the time scales. On an inter-annual scale, vegetation change was better correlated with precipitation, suggesting that rainfall was the main factor for driving vegetation changes. On a seasonal-interannual scale, correlations between vegetation coverage change and climatic factors showed that the sensitivity of vegetation growth to the aqueous and thermal condition changes was different in different seasons. The sensitivity of vegetation growth to temperature in summers was higher than in the other seasons, while its sensitivity to rainfall in both summers and autumns was higher, especially in summers. On a monthly-interannual scale, correlations between vegetation coverage change and climatic factors during growth seasons showed that the response of vegetation changes to temperature in both April and May was stronger. This indicates that the temperature effect occurs in the early stage of vegetation growth. Correlations between vegetation growth and precipitation of the month before the current month, were better from May to August, showing a hysteresis response of vegetation growth to rainfall. Grasses get green and begin to grow in April, and the impacts of temperature on grass growth are obvious. The increase of NDVI in April may be due to climatic warming that leads to an advanced growth season. In summary, relationships between monthly-interannual variations of vegetation coverage and climatic factors represent the temporal rhythm controls of temperature and precipitation on grass growth largely.