Changes In Vegetation Index Research Articles

A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data

In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work.

Modeling and assessing the variation of land surface temperature as determinants to normalized difference vegetation index and land cover changes in Nigerian cities

Modeling and assessing the variation of land surface temperature as determinants to normalized difference vegetation index and land cover changes in Nigerian cities

Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province

Land use/land cover (LULC) changes are among the most significant human-caused global variations affecting the natural environment and ecosystems. Pakistan’s LULC patterns have undergone huge changes since the 1900s, with no clear mitigation plan. This paper aims to determine LULC and normalized difference vegetation index (NDVI) changes as well as their causes in Pakistan’s Southern Punjab province over four different periods (2000, 2007, 2014, and 2021). Landsat-based images of 30 m × 30 m spatial resolution were used to detect LULC changes, while NDVI dynamics were calculated using Modis Product MOD13Q1 (Tiles: h24 v5, h24 v6) at a resolution of 250 m. The iterative self-organizing (ISO) cluster method (object meta-clustering using the minimal distance center approach) was used to quantify the LULC changes in this research because of its straightforward approach that requires minimal human intervention. The accuracy assessment and the Kappa coefficient were calculated to assess the efficacy of results derived from LULC changes. Our findings revealed considerable changes in settlements, forests, and barren land in Southern Punjab. Compared to 2000, while forest cover had reduced by 31.03%, settlement had increased by 14.52% in 2021. Similarly, forest land had rapidly been converted into barren land. For example, barren land had increased by 12.87% in 2021 compared to 2000. The analysis showed that forests were reduced by 31.03%, while settlements and barren land increased by 14.52% and 12.87%, respectively, over the twenty year period in Southern Punjab. The forest area had decreased to 4.36% by 2021. It shows that 31.03% of forest land had been converted to urban land, barren ground, and farmland. Land that was formerly utilized for vegetation had been converted into urban land due to the expansion of infrastructure and the commercial sector in Southern Punjab. Consequently, proper monitoring of LULC changes is required. Furthermore, relevant agencies, governments, and policymakers must focus on land management development. Finally, the current study provides an overall scenario of how LULC trends are evolving over the study region, which aids in land use planning and management.

Land Use Land Cover (LULC) and Surface Water Quality Assessment in and around Selected Dams of Jharkhand using Water Quality Index (WQI) and Geographic Information System (GIS)

Abstract Surface water quality deterioration is mainly occurring due to anthropogenic activities at an alarming rate in developing countries. Jharkhand has been undergoing exponential urbanisation and mining, causing immense surface water pollution and water stress. The state is heavily dependent on artificial dams for its daily water supply demands. Therefore, an effort is made to monitor and ascertain the surface water quality and the influence of nearby land use pattern on water quality, in the selected five dams, namely, Hatia dam, Kanke dam, Getalsud dam, Galudih barrage, and Chandil dam are done. These dams are built on the Subarnarekha river basin, located in the Jharkhand state on a seasonal basis and associated land use land cover (LULC) changes, changes in vegetation cover using normalised difference vegetation index (NDVI) and water body changes using normalised difference water index (NDWI) that have occurred in a 5-year gap i.e. 2016 and 2021. The secondary data for the year 2016 was obtained from the Jharkhand pollution control board report published by the government of Jharkhand, India. For the year 2021, the samples were collected from sampling sites for pre, post and monsoon seasons. The chemical analysis of collected water samples was done in the laboratory for parameters like pH, dissolved oxygen, biological oxygen demand, total calcium and magnesium, hardness, total dissolved and suspended solids, alkalinity, chlorine etc. and compared with the standard values prescribed by world health organisation (WHO) and Indian standards (IS) 10500:2012. The seasonal water quality status was analysed using the water quality index (WQI) for the pre, post and monsoon seasons of 2016 and 2021. Then, the use of supervised classification method for land use land cover (LULC), normalised difference vegetation index (NDVI) and normalised difference water index (NDWI) was opted to understand the relation between the change in water quality and quantity concerning its land use and land cover, by comparison of results from the year 2016 to 2021. LULC were found using the supervised maximum likelihood classification method in ArcGIS and its accuracy was checked using the kappa accuracy method, which was found to be varying from 87 to 95% for all sites. The results showed that the overall water quality varied from good to poor indicating that it can be used for human activities but may need pre-treatment before drinking. NDWI showed a massive increase in severe drought areas for Hatia, Kanke, Chandil and Galudih barrage, whereas moderate drought regions increased for Hatia, Getalsud, and Kanke. NDVI showed dense and moderate vegetation both decreased massively for all the dam sites indicating an alarming situation and the need to adopt better land management practices.

Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh

Drought is a widespread hazard that can tremendously affect the biodiversity, habitat of wild species, and ecosystem functioning and stability, especially in the dry region. Due to its geographic location, the north-western region of Bangladesh has a comparatively arid climate which is very much susceptible to drought occurrence and is marked as a red zone. Despite the growing evidence of the impact of drought on food security and ecosystem functioning, little effort has been paid to mitigate the drought in this region. The present study aimed to assess the drought condition of the north-western region of Bangladesh using earth observation techniques. For this purpose, Landsat data from 1990 to 2020 was used to determine various vegetation indices such as Normalized Difference Vegetation Index (NDVI), Water Index (NDWI), Moisture Index (NDMI) and Soil Adjusted Vegetation Index (SAVI), along with Land Surface Temperature (LST). Results show that the depletion of forests (2832 km2) and water bodies (6773 km2) resulted from the expansion of settlement (6563 km2) and agricultural land (1802 km2) for the period 1990–2020. Examination of the temporal changes of vegetation indices and LST showed that the values of all indices decreased while the LST increased. The negative correlation between NDVI value and LST indicates that the vegetation in our study was subject to drought-induced shocks. This study reveals the current situation of the vegetation health in the north-western region of Bangladesh in relation to the drought conditions. The findings of this study have practical implications for the policymakers in implementing necessary measures for agriculture, forests, water development, and economic zone planning.

Effects of aspect on phenology of Larix gmelinii forest in Northeast China

The response of vegetation phenology to global climate change is one of the main forms in terrestrial ecosystem change, the study of vegetation phenology is an important complement to the understanding of how global climate change affects ecosystems in multiple dimensions. We selected the distribution area of Larix gmelinii in The Greater Khingan Mountains as a case area by eliminating the heterogeneity of vegetation types, with the support of Google Earth Engine platform, we studied the effects of different aspects and land surface temperature (LST) on remote sensing phenology (RSP) that is defined as start of growing season (SOS), end of growing season (EOS) and length of growing season (LOS) respectively in the study area through Normalized Difference Vegetation Index (NDVI) changes. The results showed that SOS advanced in different aspects during the study period, and the advance amplitude of SOS on the east and west aspect was greater than that on the south and north. Except for the east aspect, EOS showed a slight postponed, and LOS was prolonged on all aspects. The latitude difference between 51° and 53° N had no significant effect on L. gmelinii in different aspects. LST had an obviously direct effect on the RSP of L. gmelinii in different aspects, and the effect of LST on SOS and LOS was significantly greater than that on EOS. The effect of LST on SOS and LOS was significant in April and spring. The main contributor to the increase of LOS was the advance of SOS, while the postponed of EOS has a relatively small contribution to LOS. Due to the redistribution of meteorological factor by aspect, the spatial and temporal heterogeneity of RSP tends to be complex, so determining the same aspect is one of the main ways to reduce the phenological heterogeneity in the study of vegetation RSP.

Exploring Long-Term Anomalies in the Vegetation Cover of Peri-Urban Parks Using the Fisher-Shannon Method.

The main goal of this study was to evaluate the potential of the Fisher-Shannon statistical method applied to the MODIS satellite time series to search for and explore any small multiyear trends and changes (herein also denoted as inner anomalies) in vegetation cover. For the purpose of our investigation, we focused on the vegetation cover of three peri-urban parks close to Rome and Naples (Italy). For each of these three areas, we analyzed the 2000-2020 time variation of four MODIS-based vegetation indices: evapotranspiration (ET), normalized difference vegetation index (NDVI), leaf area index (LAI), and enhanced vegetation index (EVI). These data sets are available in the Google Earth Engine (GEE) and were selected because they are related to the interactions between soil, water, atmosphere, and plants. To account for the great variability exhibited by the seasonal variations while identifying small multiyear trends and changes, we devised a procedure composed of two steps: (i) application of the Singular Spectrum Analysis (SSA) to each satellite-based time series to detect and remove the annual cycle including the seasonality and then (ii) analysis of the detrended signals using the Fisher-Shannon method, which combines the Shannon entropy and the Fisher Information Measure (FIM). Our results indicate that among all the three pilot test areas, Castel Volturno is characterized by the highest Shannon entropy and the lowest FIM that indicate a low level of order and organization of vegetation time series. This behaviour can be linked to the degradation phenomena induced by the parasite (Toumeyella parvicornis) that has affected dramatically the area in recent years. Our results were nicely confirmed by the comparison with in situ analyzed and independent data sets revealing the existence of subtle, small multiyear trends and changes in MODIS-based vegetation indices.

Analysis of Changes in Vegetation Index during the Rapid Urban Spatial Development Period (1990–2020) in Tehran Metropolis, Iran

Rapid urbanisation, economic growth, and urban spatial development in developing countries, such as Iran, have resulted in tremendous loss of green cover and associated ecological problems. Any effort to achieve sustainable urban development should be supported by recognising and evaluating the ecological health of vegetation cover. This study investigates vegetation cover reduction and changes in the Tehran Metropolis, Iran and identifies the most important factors influencing the observed changes. The aim of this study is two-fold: first, to assess the spatio-temporal changes in vegetation cover in Tehran between 1990 and 2020, and second, to identify the factors contributing to the changes. The Normalised Difference Vegetation Index (NDVI) is used as an indicator of green cover. The spatial and statistical data used in this study were extracted from Landsat satellite imagery and the last approved Master Plan of Tehran (2006). Geographically Weighted Regression (GWR) and geographical modelling methods were employed to analyse vegetation cover in all municipal districts of the Tehran metropolis. The results show that the vegetation density in the Tehran metropolis decreased significantly (from 38,936.80 hectares to 4663.23 hectares) between 1990 and 2020. The expansion of construction lands and the increase of population density were the most significant factors affecting the reduction in vegetation cover in Tehran. In contrast, the growth of industrial units in the urban areas of Tehran had no significant relationship with vegetation cover. The results of this study can help urban planners understand the significant drivers of vegetation loss and identify appropriate interventions to prevent it.

A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification.

Holocene vegetation and climate reconstructions from pollen records in the Mu Us Sandy Land, China

Knowledge of changes in vegetation and climate during the Holocene is important for improved assessments of future climate change and for mitigating climate warming. Owing to the relative difficulty in obtaining high-resolution sedimentary records and identifying pollen, few paleovegetation reconstructions are available from the Mu Us Sandy Land, China. This study analyzes a naturally-exposed fluvio-lacustrine section in the hinterland of the Mu Us Sandy Land. Based on a chronological framework constructed by AMS 14C, a pollen-based random forest model was used to quantitatively reconstruct changes in Holocene climate and normalized difference vegetation index (NDVI) changes. The relationships between NDVI, pollen and precipitation, as well as pollen assemblage characteristics were combined to reconstruct the evolution of the Holocene vegetation in the Mu Us Sandy Land. Our results show that the climate was cold and humid, and the dominant vegetation type was Ephedra shrub-encroached steppe between 11.2 and 8.3 ka BP; the climate reached an optimum, and vegetation evolved into Artemisia–Taraxacum-type–Thalictrum steppe from 8.3 to 2.6 ka BP; and finally, the climate gradually deteriorated and vegetation became an Artemisia–Chenopodiaceae steppe after 2.6 ka BP. In comparisons with other climate records, we found that the Mu Us Sandy Land climate optimum occurred synchronously at 8.3–2.6 ka BP, and the pattern of climate change is closely consistent with the East Asian summer monsoon. We suggest that millennial-scale Holocene climatic change in this region was mainly driven by the influences of high-latitude ice volume and summer solar radiation on the East Asian summer monsoon.

Linking remote sensing and insect defoliation biology – A cross-system comparison

The success of space-based forest defoliation monitoring beyond thematic classification hinges on the ability to link units of continuous foliar change with associated units of spectral or vegetation index change across different systems. To explore this, we used multi-temporal Landsat sensor data with respect to three defoliation metrics (shoot-based estimates (%), frass biomass (kg·ha−1), and proportion of foliar loss) across three different forest systems in the United States. While shoot-based defoliation was measured in the field, the later two defoliation metrics were estimated based on the insect phenology model, BioSIM. The systems include spruce budworm (SBW, Choristoneura fumiferana C). in heterogenous mixed forests, Minnesota, jack pine budworm (JPBW, Choristoneura pinus F). in homogenous pine forests, Wisconsin, and spongy moth (SGM, Lymantria dispar dispar L). in mixed broad-leaved forests, Maryland. Our generalized annual defoliation model for budworm was poor (R2adj = 0.35, RMSE = 23.70%) due to difficulty in detecting SBW defoliation in Minnesota. Thus, individual annual JPBW shoot-based defoliation models (R2adj = 0.27 to 0.75 and RMSE = 19.16% to 11.32%) outperformed the SBW models (R2adj = 0.08 to 0.27, RMSE = 23.18% to 34.13%). Cross-system differences between rates of change in ground-based metrics of defoliation with respect to observed change in spectral index values (dVI) were significant for shoot-based estimates of percent annual defoliation for SGM vs. JPBW (p-values <1.00 × 10−4) and SGM vs. SBW (p-values < 4.60 × 10−3). However, such comparison between SGM and JPBW were not significant when either frass biomass (kg·ha−1, p-value = 0.46) or proportion of foliage loss was used (p-value = 0.67). For a given unit of defoliation, the magnitude of vegetation index change, dVI, was significantly different between SGM and JPBW for each defoliation metric used (p-values <1.00 × 10−4), but not between SGM and SBW (p-value = 0.47) or between JPBW and SBW (p-value = 0.06) when shoot-based annual defoliation metrics were used. This study is first to report that, for a given quantity of defoliation, substantial differences in both the magnitude and direction of vegetation index change exists for different defoliator systems, which currently precludes the development of a viable, unified defoliation model. However, outcomes of this research provide an important step in our understanding of these defoliator systems that may enable specific scaling of continuous defoliation at landscape levels and across defoliator systems in the future.

Vegetation Dynamics in the Qinling-Daba Mountains through Climate Warming with Land-Use Policy

The Qinling-Daba Mountains in central China (also known as the north–south transitional zone) comprise an ideal area to study land cover change, climate change, and human activities. The normalized difference vegetation index (NDVI) change and associated driving factors are highly sensitive to vegetation cover change. To discover the long-term vegetation trends in the transition zone and determine the driving factors of NDVI change in recent decades, this study analyzed the NDVI variation trend and its spatial variation with elevation, slope, and land-use type based on annual growing season NDVI data from 1990–2019 (Landsat 30 m; Google Earth Engine). The results show that NDVI values in the Qinling-Daba Mountains significantly increased and experienced a dynamic change process, involving an initial decrease and subsequent increase over this time period. The period of 2000–2005 showed a remarkable increasing stage of the NDVI in the transition zone. Such NDVI changes are sensitive to elevation and slope. For example, areas at elevations &lt; 1500 m or with slopes of 5°–25° exhibited a stronger rate of NDVI increase than in other places. The NDVI change was also found to be positively affected by human land use and climate warming, both of which had a stronger impact than precipitation. The area with rapid NDVI growth was also the region with the greatest impact of human cropland and host to the Grain-for-Green project. This demonstrates that human land use has had a positive impact on the NDVI change in recent decades, although urbanization had led to a decrease in the NDVI in surrounding areas. Land-use policies have contributed to the large increase in NDVI values, especially those for forest conservation and expansion programs such as the Grain-for-Green project.

Impact of climate and NDVI changes on catchment storage–discharge dynamics in southern Taiwan

This study used streamflow recession analysis to assess the changes in the groundwater–streamflow relationships using streamflow data. The groundwater response to climate and Normalized Difference Vegetation Index (NDVI) changes was considered as a variable in the recession analysis. The integrated impact could thus be compared to assess the consistency of these effects on the catchment discharge processes. The results showed that the recession characteristics is linked to regional topography and streamflow condition. Variation of storage–discharge sensitivities in most catchments was not linked to the variation of groundwater due to the lower capacity of groundwater contribution. The quantified impacts aided in assessing the relationship between climate, land cover, groundwater storage and discharge. This study provides a simple explanation of effects of environmental coevolution on storage–discharge processes.

Spatio-temporal vegetation cover analysis to determine climate change in Papua New Guinea

PurposeThe climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.Design/methodology/approachThe investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.FindingsThe yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.Originality/valueThis study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

Early Onset Yellow Rust Detection Guided by Remote Sensing Indices

Early warning systems help combat crop diseases and enable sustainable plant protection by optimizing the use of resources. The application of remote sensing to detect plant diseases like wheat stripe rust, commonly known as yellow rust, is based on the presumption that the presence of a disease has a direct link with the photosynthesis capability and physical structure of a plant at both canopy and tissue level. This causes changes to the solar radiation absorption capability and thus alters the reflectance spectrum. In comparison to existing methods and technologies, remote sensing offers access to near real-time information at both the field and the regional scale to build robust disease models. This study shows the capability of multispectral images along with weather, in situ and phenology data to detect the onset of yellow rust disease. Crop details and disease observation data from field trials across the globe spanning four years (2015–2018) are combined with weather data to model disease severity over time as a value between 0 and 1 with 0 being no disease and 1 being the highest infestation level. Various tree-based ensemble algorithms like CatBoost, Random Forest and XGBoost were experimented with. The XGBoost model performs best with a mean absolute error of 0.1568 and a root mean square error of 0.2081 between the measured disease severity and the predicted disease severity. Being a fast-spreading disease and having caused epidemics in the past, it is important to detect yellow rust disease early so farmers can be warned in advance and favorable management practices can be implemented. Vegetation indices like NDVI, NDRE and NDWI from remote-sensing images were used as auxiliary features along with disease severity predictions over time derived by combining weather, in situ and phenology data. A rule-based approach is presented that uses a combination of both model output and changes in vegetation indices to predict an early disease progression window. Analysis on test trials shows that in 80% of the cases, the predicted progression window was ahead of the first disease observation on the field, offering an opportunity to take timely action that could save yield.

Dynamic Change of Vegetation Index and Its Influencing Factors in Alxa League in the Arid Area

While there have been various studies on global vegetation dynamics, limited studies have been conducted to understand vegetation changes in arid areas. Vegetation distribution patterns can be affected by multiple factors, so understanding their interactions can help improve the capability of predicting future vegetation dynamics. This study, therefore, analyzed the dynamic vegetation changes in Alxa League, China, using the Normalized Difference Vegetation Index (NDVI) dataset (2000–2019), with the consideration of land cover types, digital elevation model, air temperature, precipitation, soil moisture, total evaporation, and air quality. The results show that the NDVI in Alxa League is small. Before 2012, the NDVI value fluctuated, while after 2012, the NDVI value dropped sharply and then slowly recovered after 2015. High NDVI values were found in areas with high and frequent human activities (city centers). The NDVI in the northwest region showed a slight degradation trend, and the southeast showed a slight improvement trend. According to the land cover type analysis, the NDVI value was the largest when the land cover type was tree cover, and the NDVI value was the smallest when the land cover type was bare/sparse vegetation. Alxa League was dominated by a bare/sparse vegetation distribution. The terrain analysis indicates that when the height was between 1800 and 3492 m, the NDVI value was the highest, and high NDVI values were mainly distributed in the area with a slope &amp;gt; 25°. When the slope was flat, the NDVI value was the smallest. Considering climate factors, the NDVI was negatively correlated with air temperature, precipitation, soil moisture, and total evaporation in space, and only precipitation and soil moisture were positively correlated in time. Moreover, the population size has a strong positive correlation with the NDVI in this area. The monthly variation of the NDVI and absorbable particulate matter (PM10) was negatively correlated, i.e., strongly negatively correlated in spring, summer, and autumn, but only weakly positively correlated in winter. The seasonal variation of the NDVI was as follows: summer &amp;gt; autumn &amp;gt; spring &amp;gt; winter, and the seasonal variation of PM10 was spring &amp;gt; winter &amp;gt; summer &amp;gt; autumn. The interannual variation of the NDVI and PM10 was positively correlated. This suggests that the absorbable particulate matter (PM10) may be an essential factor for the normalized vegetation index to underestimate the dynamic changes of vegetation in arid regions. This study provides a theoretical basis for the dynamic changes of vegetation in the dry Alxa League.

Shifts in land-greening hotspots in the Yellow River Eco-Economic Belt during the last four decades and their connections to human activities

The spatial patterns of Normalized Differences Vegetation Index (NDVI) changes and their potential causes in the Yellow River Eco-Economic Belt (YREEB) during the last four decades remain unclear. To clarify this issue, this study first depicts the spatial patterns of the NDVI changes by combining GIMMS3g NDVI data and MODIS NDVI data; second, regression residual analysis, cluster analysis and the generalized additive model (GAM) approach are used to explore the potential causes of the NDVI changes. The results show that (1) the regional mean NDVI exhibited an uptrend with a slope of 0.001 per decade from 1982 to 2019, which consists of two prominent uptrend periods, i.e, 1982–1990 and 2004–2019, with slopes of 0.038 per decade and 0.027 per decade, respectively, as well as one trendless period between them. (2) There has been greening hotspots shift. The first prominent greening trend from 1982 to 1990 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2004 to 2019 mainly occurred in the Loess Plateau. (3) The greening trend and greening hotspots shift were slightly caused by climate change while mainly caused by human activities. The first greening trend was closely related to the winter wheat sowing area, and the second greening trend was related to the afforestation area.

Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape

Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides triggered by a summer rainstorm in Jølster (Norway), based on changes in the normalised difference vegetation index (NDVI) between pre- and post-event images. Pre- and post-event multi-temporal images were then created by reducing across all available images within one month before and after the landslide events, from which final change detection image composites were produced. We used the mean of backscatter intensity in co- (VV) and cross-polarisations (VH) for Sentinel-1 synthetic aperture radar (SAR) data and maximum NDVI for Sentinel-2. The NDVI-based mapping increased the number of registered events from 14 to 120, while spatial bias was decreased, from 100% of events located within 500 m of a road to 30% close to roads in the new inventory. Of the 120 landslides, 43% were also detectable in the multi-temporal SAR image composite in VV polarisation, while only the east-facing landslides were clearly visible in VH. Noise, from clouds and agriculture in Sentinel-2, and speckle in Sentinel-1, was reduced using the multi-temporal composite approaches, improving landslide visibility without compromising spatial resolution. Our results indicate that manual or automated landslide detection could be significantly improved with multi-temporal image composites using freely available earth observation images and Google Earth Engine, with valuable potential for improving spatial bias in landslide inventories. Using the multi-temporal satellite image composites, we observed significant improvements in landslide visibility in Jølster, compared with conventional bi-temporal change detection methods, and applied this for the first time using VV-polarised SAR data. The GEE scripts allow this procedure to be quickly repeated in new areas, which can be helpful for reducing spatial bias in landslide databases.

Response of enhanced vegetation index changes to latent/sensible heat flux and precipitation over Pakistan using remote sensing.

For a sustainable development and ecological integrity, it is of worth importance to monitor land use/ land cover (LULC) changes and related land-atmosphere fluxes. To serve this purpose, we have used moderate resolution imaging spectroradiometer (MODIS) retrieved-enhanced vegetation index (EVI), MERRA-2 re-analysis surface heat fluxes (latent heat flux, sensible heat flux and specific humidity), TRMM rainfall data, and OMI retrieved aerosol index (AI) over Pakistan during 2000 to 2021. High EVI (0.66) is observed in May 2021 as compared to May 2000 over Muzaffarabad, Srinagar, north and northwest of Khyber Pakhtunkhwa, east of Punjab and along the Indus River in Sindh. The highest increase in vegetative area is observed in Baluchistan (~ 366%), followed by Manavadar (~ 60%), Khyber Pakhtunkhwa (~ 41%), Sindh (~ 37%), and Punjab (~ 20%) whereas Gilgit-Baltistan and Jammu and Kashmir show reduction in vegetative area by 21% and 11% respectively. The coefficient of determination (R2) is found to be highest between rainfall and latent heat flux (R2 = 0.59) followed by rainfall and specific humidity (R2 = 0.35), and rainfall and sensible heat flux (R2 = 0.06). The latent heat flux shows increasing trend at the rate of 0.003 Wm-2 winter-1, 0.0065 Wm-2 pre-monsoon-1 and 0.0272 Wm-2 post-monsoon-1 during 1980-2021 whereas sensible heat flux shows decreasing trend at the rate of 0.00056 Wm-2 winter-1, 0.00249 Wm-2 pre-monsoon-1 and 0.0037 Wm-2 post-monsoon-1 during 1980-2021. Specific humidity depicts increasing trend at the rate of 0.0002 Wm-2 winter-1, 0.0038 Wm-2 pre-monsoon-1 and decreasing trend at the rate of 0.0080 Wm-2 post-monsoon-1 during 1980-2021. The interannual variations in AI show highest AI of 2.28 in 2021 with maximum positive percentage anomaly of 28.06% during 2007.

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies.