Vegetation Mapping Research Articles

Impacts of Bedrocks on Vegetation Carbons in Typical Karst Areas: A Case Study in Puding County, Southwest China

An accurate estimation of vegetation carbon pools and their carbon sequestration potential is significant in global carbon cycle research but the existing estimations are still insufficient and largely uncertain. Here, we estimated the vegetation carbon density, carbon stocks, and carbon sequestration potential under three main bedrock types (limestone, dolomite, and non-carbonate) in Puding County, Guizhou Province, Southwestern China. The data used here included high-resolution vegetation maps of Puding, data from 274 sample plots, and the carbon contents measured previously in adjacent areas. The land area ratio of natural vegetation at an early stage (namely, grassland and shrub, excluding artificial forests and cultivated land) in carbonate rock areas is significantly larger than that in non-carbonate areas. The average existing carbon densities of vegetation in the non-carbonate, limestone, and dolomite areas were 31.59 ± 7.43, 16.75 ± 4.12, and 8.26 ± 2.45 Mg·ha−1, respectively, while their existing carbon stocks were 752.37 ± 172.85, 855.69 ± 210.65, and 208.49 ± 61.82 Gg, respectively. The maximum vegetation carbon densities of mature forests in the three bedrock types were 156.49 ± 12.92, 130.27 ± 6.05, and 117.41 ± 30.03 Mg·ha−1, respectively. Then, their average vegetation carbon sequestration potentials were 56.07 ± 23.06, 70.13 ± 11.39, and 59.11 ± 33.00 Mg·ha−1, respectively. In other words, vegetation carbon stocks in the non-carbonate, limestone, and dolomite areas increased by 1.34 ± 0.42, 3.58 ± 0.48, and 1.49 ± 0.51 Tg, respectively, after continuous evolution to mature forests. In conclusion, the potential growth of carbon density for karst vegetation is slightly higher than that of non-karst vegetation, despite its lower existing carbon density. Additionally, natural vegetation has a greater potential for carbon sequestration than plantations on all three bedrock types.

Revision of the North West province vegetation map

Revision of the North West province vegetation map

Combining satellite and field data reveals Congo's forest types structure, functioning and composition

AbstractTropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental drivers of the main forest types, their forest structure, their floristic and functional compositions and their faunistic composition. To build the map, we used Sentinel‐2 satellite images and recent deep learning architectures. We tested the effect of topographically determined water availability on vegetation type distribution by linking the map with a water drainage depth proxy (HAND, height above the nearest drainage index). We also described vegetation type structure and composition (floristic, functional and associated fauna) by linking the map with data from large inventories and derived from satellite images. We found that water drainage depth is a major driver of forest type distribution and that the different forest types are characterized by different structure, composition and functions, bringing new insights about their origins and successional dynamics. We discuss not only the crucial role of soil–water depth, but also the importance of consistently reproducing such maps through time to develop an accurate monitoring of tropical forest types and functions, and we provide insights on peculiar forest types (Marantaceae forests and monodominant Gilbertiodendron forests) on which future studies should focus more. Under the current context of global change, expected to trigger major forest structural and compositional changes in the tropics, an appropriate monitoring strategy of the spatio‐temporal dynamics of forest types and their associated floristic and faunistic composition would considerably help anticipate detrimental shifts.

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

Enhancing Accuracy in Historical Forest Vegetation Mapping in Yunnan with Phenological Features, and Climatic and Elevation Variables

Enhancing Accuracy in Historical Forest Vegetation Mapping in Yunnan with Phenological Features, and Climatic and Elevation Variables

Reference vegetation for restoration? Three vegetation maps compared across 76 nature reserves in Uganda and Kenya

AbstractForest and landscape restoration are increasingly popular nature‐based solutions to mitigate climate change and safeguard biodiversity. Restoration planning and monitoring implies that a reference ecosystem has been defined to which the restored site can be compared, but how to best select such reference? We tested three different potential natural vegetation (PNV) maps of the same areas in Kenya and Uganda for their utility as ecological references with independent data that were not used when those maps were made. These independent datasets included presence observations of woody species from 76 sites in forest reserves in Kenya and Uganda, and classification of surveyed species into a system that included “forest‐only” and “nonforest‐only” ecological types. Our tests show that (1) the three vegetation maps largely agree on the environmental envelopes/ranges within which forests occur. (2) There are large differences in how well the maps predict the presence of forest‐only species. (3) Two maps, based on empirical observations (V4A and White), predict forest types well, whereas the third, based on climate envelopes only (NS), performs poorly. (4) A large area in Uganda is potentially in one of two alternative stable states. We conclude that it is possible to evaluate the utility of PNV maps at a more detailed scale than the level of biome and ecoregion. This indicates that it is possible to map PNV at scales required for reference for restoration and management of forest vegetation. We recommend that empirically based maps of potential natural vegetation are used in restoration planning (biome and PNV maps based on climate envelopes alone may be unreliable tools) as a baseline model for predicting the distribution of reference ecosystems under current and future conditions. It could conveniently be done by deconstructing the existing biome maps, supported by rapid botanical surveys.

Climate‐Induced Uncertainty in Modeling Gross Primary Productivity From the Light Use Efficiency Approach

AbstractLight use efficiency (LUE) models, along with satellite‐based vegetation maps and climatic reanalysis products as drivers, are effective tools for estimating large‐scale gross primary productivity (GPP). However, the climate‐induced uncertainty in LUE‐based GPP remains poorly understood, particularly the temporal scaling uncertainty from ignored climatic fluctuations. Here, two 1‐hourly reanalysis products, along with site‐based observations, were employed to drive a two‐leaf LUE model at 194 eddy‐covariance (EC) sites. The observation‐driven and reanalysis‐driven GPP at the 1‐hourly resolution were evaluated against EC GPP to illustrate the uncertainty from reanalysis products, with mean absolute deviation (MAD) and Nash‐Sutcliffe efficiency (NSE) as criterion. Moreover, the climate‐induced temporal scaling uncertainty was characterized by comparing distributed GPP (modeled with 1‐hourly resolution climatic drivers) and lumped GPP (modeled with 6‐hourly resolution climatic drivers). At the 1‐hourly resolution, results demonstrated that the reanalysis‐driven GPP showed a weaker relationship with EC GPP (MAD = 0.14 gC m−2h−1, NSE = 0.48) than the observation‐driven GPP (MAD = 0.12 gC m−2h−1, NSE = 0.60), confirming the nonnegligible climate‐induced uncertainty from reanalysis products. Additionally, the climate‐induced uncertainty arising from gridded radiation was found to be significantly larger than that associated with temperature and vapor pressure deficit (VPD). At the 6‐hourly resolution, both the observation‐driven and reanalysis‐driven lumped GPP exhibited a lower relationship with EC GPP (MAD = 0.63 gC m−26h−1, NSE = 0.54) than the corresponding distributed GPP (MAD = 0.57 gC m−26h−1, NSE = 0.59), demonstrating that the climate‐induced temporal scaling uncertainty in 6‐hourly GPP estimates was significantly apparent. This study emphasizes the imperative to refine reanalysis products for more precise capture of short‐term fluctuations and to reduce scaling uncertainties in coarse temporal resolution GPP estimates.

Biodiversity conservation and ecological restoration dominated vegetation dynamics during the 1980s-2010s in Yunnan, China

Changes in both vegetation type and productivity can affect the structure, functioning, and services of terrestrial ecosystems. Understanding vegetation dynamics and their drivers is critical for biodiversity conservation and ecosystem management. Although land cover datasets, climate-vegetation models, and remote sensing vegetation indices have been frequently used to reflect vegetation dynamics, they generally lack biological information about vegetation, such as species compositions, community structures, and succession status. Yunnan, recognized as the most biodiverse province in China, has undergone considerable vegetation changes over recent decades. However, the roles of climate change and human activities remain unclear. This study integrated detailed vegetation maps, climate factors, and vegetation indices obtained in the 1980s (1986–1995) and 2010s (2006–2015) to comprehensively evaluate the coverage transformations and productivity changes of vegetation types in Yunnan and unravel the drivers of decadal vegetation changes. The results indicated that: 1) A greening trend was observed across all vegetation types, particularly in coniferous and temperate forests. 2) The decadal vegetation changes were dominated by: the restoration of savanna and shrubland, cropland expansion, and artificial afforestation, accounting for 23.7 %, 22.9 %, and 19.1 % of all observed changes, respectively. 3) Conservation and restoration efforts dominated vegetation greening in Yunnan (55 %), followed by artificial afforestation (23 %), and agricultural expansion (16 %). By comparing vegetation maps of separated stages, the inclusion of vegetation class information provided critical knowledge for a better understanding of the intrinsic mechanisms behind vegetation greening and range shifting. Our study highlighted the significant role of ecological conservation and restoration policies and practices in influencing the spatiotemporal dynamics of biodiversity and ecosystem functioning at a regional scale, within merely a few decades.

Quantitative assessment of Hurricane Ian’s damage on urban vegetation dynamics utilizing Landsat 9 in Fort Myers, Florida

Florida’s unique climatic and geographical features have profoundly influenced its hurricane history. This study quantitatively examines the effects of Hurricane Ian on urban vegetation in Fort Myers, Florida, using remote sensing data. We analyzed pre- and post-hurricane vegetation indices, including NDVI (Normalized Difference Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index). Our findings reveal varied spatial impacts, with NDVI changes ranging from −0.03 to 0.333, ARVI changes from −0.016 to 0.25, and SAVI changes from −0.04 to 0.5. Negative values indicate vegetation damage, while positive values suggest resilience or recovery. The study area experienced a 63.75% reduction in vegetation cover, from 67.10 km2 before Hurricane Ian to 24.325 km2 after. Pre-hurricane NDVI ranged from −0.2298 to 0.5663, while post-hurricane values ranged from −0.189 to 0.521, indicating overall vegetation stress. ARVI maxima decreased from 0.379 to 0.352, and SAVI maxima from 0.849 to 0.782, further confirming vegetation damage. Support Vector Machine classification achieved 89% accuracy (Kappa = 0.85) for pre-hurricane and 87% (Kappa = 0.83) for post-hurricane vegetation mapping. These findings enhance our understanding of hurricane impacts on urban green infrastructure, with significant implications for urban planning and disaster preparedness in coastal cities prone to extreme weather events. The outcomes enhance damage assessment methodologies and provide valuable insights into the ecological consequences of hurricanes on urban ecosystems.

Hindcasting and updating Landsat-based forest structure mapping across years to support forest management and planning

Forest vegetation mapping that integrates forest inventory data with multispectral remote sensing data provides valuable geospatial products for public land management agencies, but resource managers may require rapid updating of maps as new imagery becomes available (updating) or retrospective mapping for times prior to forest inventory plot measurement (hindcasting). While forest attribute mapping using Landsat multispectral imagery is common, the accuracy of applying models outside of reference epoch to support long-term forest monitoring is not normally quantified. We examine whether a Landsat-based mapping approach can support robust, temporally consistent multivariate mapping of forest structure and composition data in support of forest management planning and landscape analysis. Specifically, we ask: how accurate forest attribute mapping was when hindcasting or updating outside of a period of time when forest inventory plot data were available (reference epoch)? In the western Cascade Mountains of Oregon and California, USA, we used the gradient nearest neighbor approach to annually impute USDA Forest Inventory and Analysis (FIA) plot data (2001–2016) to all 30-m forested pixels based on temporally smoothed Landsat multispectral imagery (1986–2021), including basal area, canopy cover, quadratic mean diameter of dominant trees, stand height, and the density of large diameter trees. We made extrapolations from models fit to a 10-year reference epoch to both earlier periods (2001–2006 hindcast) and to later period (2011–2016 update) and quantified prediction accuracies relative to models based on the full data (2001–2016). To evaluate the influence of spatial scale on hindcasting and updating, we compared full and extrapolation model predictions at pixel-level (0.09 ha) and hexagon-level (780 ha).At the plot-level, we found no strong differences between the full and extrapolation model predictions for R2 and mean error nor among predicted vs. observed regression coefficients. At the pixel-level, average differences due to hindcasting and updating were near zero, though differences varied up to 20 % across pixels. At the hexagon-level, the range in map differences was small (+/- 5 %), but hindcasting resulted in lesser forest attribute predictions. We observed greater variability in pixel-level and hexagon-level prediction differences when hindcasting or updating was temporally further away from the reference period. Using 2001 hindcast and 2016 updated maps as a case study, we found that with hindcasting and updating map differences were spatially aggregated across the study region. Our results support Landsat-based hindcasting and updating of forest attribute mapping beyond the time period covered by forest plot data. Our results suggest aggregating data to coarse spatial resolutions may minimize differences due to hindcasting and updating. Further research is needed to identify the key drivers for prediction differences to improve the accuracy of both hindcasting and updating as a basis for forest monitoring.

Band configurations and seasonality influence the predictions of common boreal tree species using UAS image data

Key messageData acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees.ContextThe effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration.AimsThe objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots.MethodsTree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression.ResultsPrediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data.ConclusionThe MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions.

Mapping plant communities of the Karoo National Park, South Africa, using Sentinel-2 and topo-morphological data

AimsThis study aimed at classifying, mapping and describing the plant communities of the Karoo National Park using floristic surveys in conjunction with Sentinel-2 and topo-morphological data. Study areaKaroo National Park, Western Cape, South Africa. MethodsThe vegetation of the Karoo National Park was delineated into homogenous physiognomic-physiographic units using Sentinel-2 images. A total of 128 survey plots (100 m2 each) were surveyed within the different homogeneous units during the period 2016 to 2020. Within each survey plot, all rooted species were identified and their cover abundance estimated. Each plot was photographed and its geolocation recorded. The floristic data were captured using the Braun Blanquet Personal Computer suite and exported to the JUICE Software programme. A modified TWINSPAN classification was done to derive a first tabled synopsis of the plant communities. The different plant communities were subsequently classified and described according to their diagnostic and dominant species gleaned from the synoptic table. Species richness was determined by counting the number of different species per plant community while the Shannon–Wiener Index and Rich-Gini–Simpson Index of diversity (D) were used to derive indices of species diversity per plant community. Results12 major communities and two sub-communities that are distinctly linked to various abiotic factors were identified, described and mapped. The higher-lying rocky steep midslopes as well as the valley bottomland areas had the highest diversity and species richness. ConclusionsThis study proves the efficacy of using Sentinel-2 and topo-morphological data in classification, description and mapping vegetation of extensive natural areas. The vegetation map and classification of plant communities provide a baseline to inform management decisions.Taxonomic reference: SA-Plant Checklist-2019–2020, South African National Biodiversity Institute, 2020, Botanical Database of Southern Africa (BODATSA) (http://posa.sanbi.org/) [accessed January 2022].

Revealing the impact of spatial bias in survey design for habitat mapping: A tale of two sampling designs

Submerged aquatic vegetation, referring to benthic macroalgae and plants that obligately grow underwater, are critical components of marine ecosystems and are frequently found to provide preferential recruitment habitats. The mapping and monitoring of aquatic vegetation through remote sensing and machine learning is becoming an important aspect of managing coastal environments at scale. Accurate mapping and monitoring require robust sampling and occurrence data to assess predictive error and quantify submerged vegetation extents. The form of ground truthing survey design (preferential, random, grid-based or spatially balanced) could significantly influence predictive model outcomes and the overall accuracy of mapping and monitoring. Here, we test and contrast mapping aquatic vegetation extent ground-truthed using two different sampling designs: we used both preferential and spatially balanced sampling designs across four coastal sites along the midwest of Australia. We validate the map outcomes using spatial cross-validation and demonstrate that spatially balanced ground truthing significantly outperforms preferential sampling designs regarding modelled extent and map accuracy. In our comparison, we found that, on average, preferential designs overestimated vegetation extent by 25 percent compared to balanced designs and achieved an average kappa statistic, F1 score and Area under the Curve of 0.48, 0.615 and 0.517, respectively; whereas balanced designs achieved a kappa statistic, F1 score and AUC of 0.84, 0.85 and 0.83 respectively. We strongly recommend that sampling designs for remote sensing-derived habitat models be spatially balanced where habitat extent is proposed as a metric for monitoring.

Mangrove Vegetation Mapping with Remote Sensing Approach (Citra Landsat 8) in Lhokseumawe City

Mangrove Vegetation Mapping with Remote Sensing Approach (Citra Landsat 8) in Lhokseumawe City

Mapping the wildland-urban interface at municipal level for wildfire exposure analysis in mainland Portugal

The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology. We tested the procedure in four municipalities of mainland Portugal with different fire history, biophysical conditions, and sociodemographic contexts. We considered WUI as either intermix or interface. Our approach integrates both building location data and high-resolution vegetation maps, to calculate the density of buildings and forest cover proportion within different circular moving window sizes. Within each radius, we evaluated the total area and spatial distribution of the WUI types, as well as the number of buildings within WUI and within the fire perimeters recorded between the years 2000 and 2022 and analysed the differences between municipalities. We then compared the mapped WUI with previous WUI mappings for mainland Portugal, to identify common spots and potential spatial divergences. We found that the area mapped as WUI within all four municipalities ranged from about 400 km2 to 1135 km2 depending on the radius size. A distinct distribution for each type of WUI was observed as the radius size increased: the intermix WUI showed a tendency to increase, and the interface WUI increased only between the radius of 100 and 200 m, decreasing gradually in subsequent radii. Between 39.4% and 45.5% of the nearly 200,000 buildings in the study areas were within WUI, depending on radius size and a total of 5436 buildings were within the historic fire perimeter. Although the comparison with other maps showed fair agreement, due to differences in data and methodology, common areas mapped as WUI were found, which suggests that these areas should receive greater attention from decision-makers regarding fire management strategies, since their classification as WUI remains consistent across different methodologies.

A satellite-derived baseline of photosynthetic life across Antarctica

Terrestrial vegetation communities across Antarctica are characteristically sparse, presenting a challenge for mapping their occurrence using remote sensing at the continent scale. At present there is no continent-wide baseline record of Antarctic vegetation, and large-scale area estimates remain unquantified. With local vegetation distribution shifts now apparent and further predicted in response to environmental change across Antarctica, it is critical to establish a baseline to document these changes. Here we present a 10 m-resolution map of photosynthetic life in terrestrial and cryospheric habitats across the entire Antarctic continent, maritime archipelagos and islands south of 60° S. Using Sentinel-2 imagery (2017–2023) and spectral indices, we detected terrestrial green vegetation (vascular plants, bryophytes, green algae) and lichens across ice-free areas, and cryospheric green snow algae across coastal snowpacks. The detected vegetation occupies a total area of 44.2 km2, with over half contained in the South Shetland Islands, altogether contributing just 0.12% of the total ice-free area included in the analysis. Due to methodological constraints, dark-coloured lichens and cyanobacterial mats were excluded from the study. This vegetation map improves the geospatial data available for vegetation across Antarctica, and provides a tool for future conservation planning and large-scale biogeographic assessments.

Mapping Vegetation Types and Land Use Dynamics in Kanyabaha Wetland from 1990 to 2021

Wetlands are crucial ecosystems providing essential ecological services, yet they face increasing threats from human activities. This study focuses on Kanyabaha Wetland in Uganda, examining its vegetation dynamics over three decades (1990-2021) using Landsat satellite imagery. The research characterizes land use and cover types including papyrus, grasslands, farmlands, tree plantations, built-up areas, and woodlands. Remote sensing data was processed and classified using ArcMap software, validated through field verification, resulting in high overall accuracy (>75%) across all study years. The images were analyzed using a hybrid of unsupervised (ISO data) and supervised (Maximum Likelihood) classification techniques. Findings reveal significant shifts in vegetation cover, with papyrus dominating initially but declining over time due to expansion in farmlands and settlements. Grasslands also decreased, while areas under farming and built-up structures expanded. Transition matrices illustrate these changes, highlighting stable and shifting landscape dynamics. Statistical analyses indicate a decrease in papyrus cover from 51.5% in 1990 to 39.1% in 2021, while farmland and built-up areas increased from 3.0% to 31.6% and 3.2% to 5.1%, respectively. This study highlights the vulnerability of Kanyabaha Wetland to anthropogenic impacts, necessitating targeted conservation strategies to sustain its ecological integrity amid ongoing land use changes.

Long-term continuous changes of vegetation cover in desert oasis of a hyper-arid endorheic basin with LandTrendr algorithm

Ecological water diversion projects (EWDP) is one of the most important initiatives for oases restoration in arid endorheic basins. Fine monitoring of the long-term and continuous changes in vegetation cover before and after the EWDP is important to evaluate the effects of EWDP on ecosystem restoration. In this study, a trajectory-based change detection approach was applied with the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm on Google Earth Engine to map vegetation cover change trajectories in Ejina Oasis in the lower Heihe River Basin of China during 1990–2020. In addition to monotonic changes (i.e., continuous greening and continuous browning), LandTrendr also can flexibly capture the breaks of greening or browning events, including the greening with negative break, browning with positive break, browning to greening, and greening to browning. These changes mainly happened in shrub land and the area with land use transition. Approximately 82 % of the oasis ecosystem has been restored by the implementation of EWDP since 2000. However, in the past decade, the EWDP also caused some negative effects on vegetation cover in some areas. Both the categories of browning with positive break and greening to browning became widespread after 2010, with the cumulative proportions accounting for 79.4 % and 70.1 % of each category. The mean magnitude and slope of change in NDVI in these two categories after 2010 were −0.03 and −0.008 yr−1, −0.12 and −0.035 yr−1, respectively. This research demonstrated that the proposed approach was effective and useful for continuously monitoring the timing and extent of vegetation recovery processes in the hyper-arid endorheic basin. The EWDP plays an important role in vegetation restoration, but we also need to continuously monitor negative impacts which are crucial for environmental assessment and policy making in the arid endorheic basin.

Impact of greenery and waterbody on the cooling of city’s environment: a case of Rajshahi City

ABSTRACT Both plants and bodies of water are crucial to the health of the urban landscape. The degree to which these factors contribute to cooling appears, however, to change depending on factors such as location, time of year, and the mix of land use and land cover. The purpose of this study was to investigate the potential role of trees and water in Rajshahi City Corporation in reducing urban heat island effect. As a first step, satellite images are processed to generate the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI). The second step is to produce maps of vegetation and water features by using a decision tree (DT) algorithm fed with threshold values from the indices. After that, thermal bands from the Landsat 8 OLI and MODIS Terra Earth observation data are used to create a map of the land surface temperature (LST). Finally, this study assessed the effect of vegetation and water coverage on global surface temperatures by analyzing their spatial distributions. Different kinds of land cover can be found in and around urban centers. Therefore, this study chose two types of water bodies (big and small) and two types of vegetation (sparse and attached/compact) for experiment. Water bodies produce cooler temperatures than other land cover types, as shown by the data. It has been found, however, that cooling rates differ across large and small bodies of water. The surface temperatures of large bodies of water are typically lower than those of smaller bodies of water. Accordingly, it appears that expansive areas significantly impact the cooling of metropolitan environments. The findings also demonstrate a difference in surface temperature between areas of dense and sparse vegetation. The cooling impact of the sparse vegetation is much less than that of the dense and widespread vegetation. There was a significant relationship between surface warming and vegetation cover in this study (R2 = 0.81). Contrarily, the presence of water and vegetation is strongly correlated (R2 = 0.88) with surface warming. The findings also show that surface warming differs depending on land use. According to a study of four typical RCC locations, business districts and industrial zones have a far greater impact on local surface temperatures than do residential developments. The experiment demonstrates that the residential area with a high proportion of vegetation had a lower average temperature by nearly 2°C. The study’s findings highlight the value of maintaining urban parks and water features.

The 30 m vegetation maps from 1990 to 2020 in the Tibetan Plateau

The Tibetan Plateau (TP) is crucial for global climate change and China’s ecological security. Given recent drastic changes in vegetation from climate change and human activities, long-term vegetation monitoring is urgently required. This study produced the vegetation maps of the TP from 1990 to 2020 every ten years using random forest classifier and Landsat imagery. We selected the same stable samples and features for mapping to reduce errors between years and proposed spatial filtering to further improve the accuracy. The overall accuracy surpassed 95.00%, with all Kappa coefficients exceeding 0.95. A further assessment based on sampling sites from literature and field survey was higher than 80%. The importance ranking results indicated that in the TP, climate factors and terrain factors are the most important factors in the vegetation mapping. This study provides a method for mapping vegetation in alpine areas and data support for researching the dynamic change of vegetation on the TP and evaluating its response to climate change.