Remote Sensing-based Analysis Research Articles

Three Years of Google Earth Engine-Based Archaeological Surveys in Iraqi Kurdistan: Results from the Ground

This paper presents the results of a three-year survey (2021–2023), conducted in an area of approximately 356 km2 in Iraqi Kurdistan with the aim of identifying previously undetected archaeological sites. Thanks to the development of a multi-temporal approach based on open multispectral satellite data, greater effectiveness was achieved for the recognition of archaeological sites when compared to the use of single archival or freely accessible satellite images, which are typically employed in archaeological research. In particular, the Google Earth Engine services allowed for the efficient utilization of cloud computing resources to handle hundreds of remote sensing images. Using different datasets, namely Landsat 5, Landsat 7 and Sentinel-2, several products were obtained by processing entire stacks of images acquired at different epochs, thus minimizing the adverse effects on site visibility caused by vegetation, crops and cloud coverage and permitting an effective visual inspection and site recognition. Furthermore, spectral signature analysis of every potential site complemented the method. The developed approach was tested on areas that belong to the Land of Nineveh Archaeological Project (LoNAP) and the Upper Greater Zab Archaeological Reconnaissance (UGZAR) project, which had been intensively surveyed in the recent past. This represented an additional challenge to the method, as the most visible and extensive sites (tells) had already been detected. Three years of direct ground-truthing in the field enabled assessment of the outcomes of the remote sensing-based analysis, discovering more than 60 previously undetected sites and confirming the utility of the method for archaeological research in the area of Northern Mesopotamia.

Remote Sensing-Based Analysis of Precipitation Events: Spatiotemporal Characterization across China

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

Extreme weather events and their socioeconomic impacts: A remote sensing-based analysis of flood damages

Global warming is a primary cause of change in global climate patterns. Pakistan ranks among the eight nations that have been impacted by global warming. Pakistan is experiencing significant consequences due to the increasing frequency and intensity of floods. A recent flood in 2022 affected 33 million people and destroyed 2.3 million houses. The study used Sentinel-1 data to extract the precise flood extent and its impacts on infrastructure and cropland in Pakistan's Sindh province. According to the results, the recent flood caused extensive damage to about ∼7340 kilometers, from which ∼1220 kilometers of roads were damaged in the Dadu district. A total of ∼4343 acres of settlement areas was affected along with 0.82 million acres of croplands by flood across different districts of Sindh. Pakistan recently experienced a temperature of 40 °C in many cities during May. It is worth noting that the average temperature in Pakistan has been steadily rising over the past 70 years. In addition, numerous cities experienced unprecedented levels of precipitation within a single month and heavy rainfall within a single day. According to the study, the key factors contributing to this severe flood include heavy monsoon rains, extreme temperatures, rapid glacial melting, and inadequate governance. The study suggested potential solutions to avoid this type of crisis in the future, including the green revolution to avoid extreme temperatures, dam constructions, long-term political stability, raising awareness about climate change, and infrastructure advancement.

Wetland water balance estimation in an arid region using remote sensing technology and hydrological modelling

ABSTRACT Understanding the hydrological processes associated with wetland dynamics is fundamental to studying climate change impacts and the global water cycle. This study simulates the variation of water balance in the Ghorra Playa, Eastern Tunisia, over the period extending from September 2017 to August 2020. The playa is a seasonal habitat for a number of migratory bird species and its watershed is of vital agricultural importance. Water balance estimations were performed using two different approaches: remote sensing-based analyses (Sentinel-2B, Global Precipitation Measurements (GPM) and Famine Early Warning System Network (FEWS-NET)) and modelling founded on field data. The playa presents an average annual water balance of 1.1 million cubic metres. Water inflows come from direct rainfall, mostly in the fall and spring seasons. Groundwater flow into the playa significantly influenced the pattern of water flux (81%). The annual, seasonal and monthly water budget simulations show reasonably good agreement between the remote sensing-based analysis and the hydrological modelling exercise.

Remote sensing-based analysis of land use, land cover, and land surface temperature changes in Jammu District, India

ABSTRACT The study conducted in Jammu District, India, investigates land use and land cover (LULC) transformations over the past three decades using satellite data and remote sensing techniques. Analyzing data from 1990 to 2020, significant changes were observed. Agricultural land expanded by 157.76 km2 (6.71%), barren land by 151.69 km2 (6.45%), and settlements by 96.97 km2 (4.12%). However, vegetation decreased by 389.77 km2 (16.57%), while water bodies experienced minimal changes. Land Surface Temperature (LST) analysis, utilizing MODIS data (2000-2020), revealed warming trends, with temperatures ranging from 15.92°C to 42.77°C in 2010 and 14.04°C to 37.01°C in 2020. Notably, NDVI values peaked in 2020 (0.759) and were lowest in 1990 (−0.243), indicating healthier vegetation and lower surface temperatures. This inverse correlation highlights NDVI's potential as an indicator for assessing vegetation health and its impact on local temperature conditions. Man-Kendall Z statistics indicated negative trends for Tmax and Tmin, while rainfall data showed significant positive trend. Population growth, urbanization, climate change and agricultural intensification emerged as principal drivers of the LULC changes in the region. This study underscores the importance of geospatial tools in monitoring LULC changes, providing valuable insights for policymakers and planners to formulate sustainable land use planning and management strategies.

Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective

Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective

Planning for sustainability: Historical data and remote sensing-based analyses aid landscape design in one of the largest remnant European floodplains

Large floodplain rivers are among the most threatened ecosystems on Earth and their utilization is expected to grow. Here, we integrated historical data and remote sensing-based landscape analyses and applied stakeholder evaluation of present-day utilization of different river–floodplain habitat types to understand the process of landscape development and provide a basis for sustainable landscape design in one of the largest remnant floodplains of the Danube River, Hungary. Temporal trajectories indicated drastic transformation of the landscape over almost four centuries as a result of river regulation works. Of these, the most substantial were the canalization of the main channel of the Danube into its largest side arm and cutting of large meandering segments, which resulted in the conversion of wetlands to other land uses, particularly agricultural land. The total area of aquatic habitats decreased by more than five-fold, and substantial changes occurred in the extent and composition of river–floodplain habitat types. Evaluation of present-day land use indicated that protected areas are under less human influence and have higher potential for the maintenance of aquatic biodiversity than unprotected ones. Although the protected area network still includes representatives of all floodplain habitat types, past changes and present-day utilization of the landscape limit conservation and restoration possibilities. We provide implications for management and conclude that the joint analyses of historical landscape conditions and present-day evaluation of human utilization can be fruitful to aid the sustainability design and management of river–floodplain ecosystems.

Delineating reservoir area using geologic remote sensing analysis, Jizan (Saudi Arabia): Implications for geothermal exploration

Jizan Province is considered one of the potential geothermal areas in Southwest Saudi Arabia. However, the prospective geothermal reservoir is still not delineated and the geothermal energy reserve is not estimated properly. Therefore, this study aims to delineate the geothermal prospect area using remote sensing-based analysis. Permeable areas, the high anomaly of Land Surface Temperature (LST), and the existence of granitic rock were the parameters used in the remote sensing analysis. Digital Elevation Models (DEM) SRTM and Landsat 8 images were used as the data source. Automatically- and manually-extracted structural lineaments supported by fractures/faults interpretation were integrated to generate a final map of the permeable areas defined in the study area (with three main permeable zones; Zones 1, 2 and 3). The average value of five LST maps and the Natural Different Building Index (NDBI) were used to investigate the high anomaly of LST. Final fuzzy logic integration of the defined permeable area, high LST anomaly, and the existence of granitic rock allowed a unique opportunity for the delineation of the prospective geothermal reservoir area. Remote sensing results were validated using the field-based studies performed in the same area. Three zones are suggested as prospective geothermal potential areas (Zones A, B, and C), where Zone A is the most favourable due to its estimated largest area and is interpreted to belong to the same geothermal system as Zone C. This study improves the understanding of the prospective geothermal reservoir area in Jizan based on the delineation of high permeable zones, and high anomaly LST. The spatial distribution of radiogenic elements in the rock units is also considered an indicator of the heat source of the geothermal system.

The Peri-Urban Gentrification Process in Cisauk Subdistrict, Tangerang Regency, Indonesia

Gentrification is a phenomenon that occurs in many regions. Gentrification is characterized by the influx of more affluent residents into initially low-value areas. This influx increases the value of these areas, potentially leading to the displacement of people who cannot afford to remain in the neighborhood. This study aims to examine the cause and impacts of peri-urban gentrification in Cisauk District, Tangerang Regency. This study uses qualitative and remote sensing-based analysis to investigate gentrification's impact on six Cisauk sub-district villages. We used primary and secondary data for the analysis. The primary data was collected from observations, interviews, questionnaires, and Google Earth Engine data catalog to get Landsat 7 and 8 image-ries. The secondary was collected from the Central Bureau of Statistics. The Landsat data was used to identi-fy the change on the physical aspect, while the others were used to analyze the non-physical aspect. After-ward, positive and negative effects were explored, emphasizing residents' responses and adaptations. The re-sults showed that Cisauk Subdistrict faced a gentrification process driven by locational factors (i.e., proximity to toll road) and local government policies (i.e., spatial planning policy designating it as a high-density residential area). The positive impact of the gentrified area is that the region is developing economically much better, and many areas function better economically and so benefit the region and its people. On the other hand, the negative impacts of gentrified areas are that indigenous people are threatened with being forced to leave their villages, there are significant increases in land prices, and changes in people's social lives occur. To minimize the negative impact of gentrification, the government should promote the capacity of low-income households to access its benefits.

Assessing the ecological impact of the mass entry of Rohingya refugees in Ukhia, Cox's Bazar, Bangladesh: a remote sensing-based analysis

The massive influx of Rohingya refugees to Bangladesh from Myanmar in August 2017, impelled the government of Bangladesh to clear thousands of acres of forestland in Ukhia and Teknaf sub-districts to allow their accommodation. Subsequently, the refugees also cut down trees thus resulting in rapid deforestation in that region. Recently, however, the Rohingya refugees in collaboration with UNHCR, have undertaken a massive tree plantation project. In this study, we calculated a Normalized Difference Vegetation Index (NDVI) using remote sensing data in the Ukhia sub-district to analyze changes that occurred in the years following the 2017 influx and evaluated the effectiveness of the reforestation effort. After calculating the NDVI, we found that there was an implication of a slight increase in vegetation. The results show that the total area of vegetation coverage in February, 2017 was 215 square kilometers and in 2019 it had gone down to 197.92 and by 2023 it had recovered to 235 square kilometers. Thus, we can conclude that the reforestation process should be continued as the changes will become more visible. As Bangladesh is highly vulnerable to climate change, continued deforestation will only aggravate the situation and thus this raises the importance of reforestation.

A Synoptic- and Remote Sensing-based Analysis of a Severe Dust Storm Event over Central Asia

A severe dust storm blanketing Central Asia on 3–4 November 2021 was investigated employing satellite remote-sensing, synoptic meteorological observations, reanalysis and HYSPLIT back-trajectories. The prevailing meteorological conditions showed an intensification of air subsidence over eastern Kazakhstan, featured in a typical omega-blocking system over the region and two troughs to its west and east axis, one day before the dust storm. The prevailing high-pressure system and temperature gradients over Kazakhstan modulated the dominant anticyclonic wind pattern generated from the south Balkhash basin toward the Caspian Sea, causing a huge dust storm that covered the southern half of Kazakhstan and large parts of Uzbekistan, Tajikistan and Turkmenistan. The dust storm originated in the steppes of southern Kazakhstan by violent downdraft winds. Initially it swept over eastern parts and then the whole of Uzbekistan, reaching the Caspian Sea in the west. Meteorological measurements and HYSPLIT back-trajectories at selected sites in Central Asia (Turkmenabat, Khujand and Tashkent) showed a remarkable dust impact that reduced temperature (by 2–4°C) and visibility to below 1 km at different periods, as the thick dust plume expanded in various directions. The extremely high PM concentrations (PM10 > 10,000 µg m–3 in Tashkent) could endanger both human health and the environment, especially in a region suffering from high susceptibility to wind erosion and significant land degradation and desertification. Effective and immediate stabilising measures to control wind erosion in vulnerable areas of Central Asia are warranted.

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin.

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices.

Spatio-temporal variations in the water quality of the Doorndraai Dam, South Africa: An assessment of sustainable water resource management

The problem of water scarcity and clean water in sub-Saharan Africa is a growing concern. This study aims to quantify the water quality on a temporal scale in the Doorndraai dam site in sub-Saharan Africa to design possible management options. Here, an integrated approach using both in-situ measurements of water quality parameters and remote sensing data was used to derive the water quality index (WQI) and inherent optical properties of water to deduce the factors governing seasonal and annual variability. The results show that all the water quality parameters analyzed fall under the permissible limit of the World Health Organization (WHO) for drinking water, except turbidity. The average value of turbidity for the dry and wet periods was 12.52 and 3.39 NTU, respectively. WQI value ranges from good to excellent during the wet season, and poor in the dry season owing to the high values of turbidity in the water samples. Both in-situ and remote sensing-based analysis shows that during the last five years, the value of suspended particulate matter (SPM) based on Landsat-8 increased gradually in the study area. The Sentinel-2 derived modified normalized difference water index (MNDWI) shows a decreasing trend in the water area due to encroachment. The strong correlation between in-situ and remote sensing data supports the usefulness of remote sensing techniques for water resource management, especially in data-scarce regions. Looking at the spatio-temporal trend of water quality evolution, the findings of this study will help local decision-makers design sustainable plans for water resource management of Doorndraai dam.

Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh

Agricultural transformation is one of the important factors of rural planning and sustainable land management. There are natural and man-made reasons of this transformation, which brings both positive and negative impacts on the physical environment, food security, and human livelihoods. This study aims to explore a case of land use conversion from traditional agricultural land to the man-made wetland for fisheries in Rajshahi, Bangladesh. The conversion might be a result of purposeful land use and land cover changes to continue agricultural production for the market demands. A hybrid model of logistic regression and water index has been used to investigate the dynamics of LULC (Land Use Land Cover) changes. Moderate-resolution multi-temporal Landsat imageries of 2000, 2010, and 2020 have been used for visual interpretation and quantitative analysis. During the study period of 20 years, wetlands for entrepreneurial pond culture were remarkably increased, while agricultural land and vegetation experienced a decreasing trend. It is predicted that wetlands are going to be further increased by diminishing agricultural land and vegetation until 2030. Despite the fact of suitable data unavailability, this study also shows that the remote sensing approach provides a powerful tool for analysing and monitoring spatiotemporal agricultural transformation. Moreover, the empirical findings of land use modelling might be useful for agricultural planning and decision-making in rural Bangladesh.

Remote Sensing-Based Analysis of Urban Landscape Change in the City of Bucharest, Romania

The paper investigates the urban landscape changes for the last 50 years in Bucharest, the capital city of Romania. Bucharest shows a complex structural transformation driven by the socialist urban policy, followed by an intensive real-estate market development. Our analysis is based on a diachronic set of high-resolution satellite imagery: declassified CORONA KH-4B from 1968, SPOT-1 from 1989, and multisensor stacked layers from Sentinel-1 SAR together with Sentinel-2MSI from 2018. Three different datasets of land cover/use are extracted for the reference years. Each dataset reveals its own urban structure pattern. The first one illustrates a radiography of the city in the second part of the 20th century, where rural patterns meet the modern ones, while the second one reveals the frame of a city in a full process of transformation with multiple constructions sites, based on the socialist model. The third one presents an image of a cosmopolitan city during an expansion process, with a high degree of landscape heterogeneity. All the datasets are included in a built-up change analysis in order to map and assess the spatial transformations of the city pattern over 5 decades. In order to quantify and map the changes, the Built-up Change Index (BCI) is introduced. The results highlight a particular situation linked to the policy development visions for each decade, with major changes of about 50% for different built-up classes. The GIS analysis illustrates two major landscape transformations: from the old semirural structures with houses surrounded by gardens from 1968, to a compact pattern with large districts of blocks of flats in 1989, and a contemporary city defined by an uncontrolled urban sprawl process in 2018.

Remote Sensing-Based Analysis of Spatial and Temporal Water Colour Variations in Baiyangdian Lake after the Establishment of the Xiong’an New Area

The Forel-Ule Index (FUI) is an important parameter that can be calculated from optical remote sensing data to assess water quality based on water colour. Using Sentinel-2 images from April to November within the 2016–2020 period coupled with the Google Earth Engine Platform, we calculated FUI to analyse the spatial distribution, seasonal variations, and inter-annual variations of water colour in Baiyangdian Lake in the Xiong’an New Area established on 1 April 2017. The lake was divided into seven sub-regions, A–G; subsequently, high and low FUI values were observed in the south and north, respectively. Additionally, the mean FUI values of G and F zones in the south were 11.9 and 12.7, respectively, whereas those for the A, B, C, D, and E zones in the north were 10.5, 9.8, 10.4, 11.1, 11.2, respectively. The seasonal variations in the Baiyangdian Lake and seven sub-regions were consistent, with turbid water in spring and autumn, and clear water in summer. Inter-annual variations analyses for 2016–2020 indicated that the zone of A became progressively turbid, whereas the B, C, D, E, F, and G zones exhibited slow and gradually decreasing trends. Our findings suggest that the overall water quality of Baiyangdian Lake may be better, which may be related to the governance policies of the region.

Remote Sensing of Grassland Production and Management—A Review

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Drought Risk to Agricultural Systems in Zimbabwe: A Spatial Analysis of Hazard, Exposure, and Vulnerability

The devastating impacts of drought are fast becoming a global concern. Zimbabwe is among the countries more severely affected, where drought impacts have led to water shortages, declining yields, and periods of food insecurity, accompanied by economic downturns. In particular, the country’s agricultural sector, mostly comprised of smallholder rainfed systems, is at great risk of drought. In this study, a multimethod approach is applied, including a remote sensing-based analysis of vegetation health data from 1989–2019 to assess the drought hazard, as well as a spatial analysis combined with expert consultations to determine drought vulnerability and exposure of agricultural systems. The results show that droughts frequently occur with changing patterns across Zimbabwe. Every district has been affected by drought during the past thirty years, with varying levels of severity and frequency. Severe drought episodes have been observed in 1991–1992, 1994–1995, 2002–2003, 2015–2016, and 2018–2019. Drought vulnerability and exposure vary substantially in the country, with the south-western provinces of Matabeleland North and South showing particularly high levels. Assessments of high-risk areas, combined with an analysis of the drivers of risk, set the path towards tailor-made adaptation strategies that consider drought frequency and severity, exposure, and vulnerability.

Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China

With the rapid economic development of industrial rural areas in Southern Jiangsu, the rural landscape and ecological environment of these industrial rural areas are getting damaged. Based on GIS and RS techniques, Landsat Satellite remote sensing images from 1981, 1991, 2001, 2011 and 2018 were collected for Jiangyin, Zhangjiagang, Changshu and Kunshan, to extract landscape pattern indexes and spatial distribution data. Landscape pattern indexes of the patch-class level and landscape level from each year were calculated by FRAGSTATS. After analyzing and comparing landscape pattern variation of five years, progress, characteristics and driving forces of landscape pattern evolution were explored. At the patch-class level, construction land had continuously encroached on green and cultivated land, exhibiting trends of expansion and centralization. At the landscape level, the number of small patches and degree of landscape fragmentation generally increased. The direct cause of landscape pattern evolution in industrial rural areas of Southern Jiangsu was the encroachment and segmentation of green and cultivated land by construction land, and the dominant factors driving the changes in construction land in the industrial rural areas of Southern Jiangsu were the effects of land and population aggregation exerted by the development of township enterprises and rural industries.

Is the push-pull paradigm useful to explain rural-urban migration? A case study in Uttarakhand, India.

The present study explored the motivation of rural-urban migrants who moved from the Himalaya foothills of Uttarakhand to its capital city, Dehradun. A survey of 100 migrant families reported their socio-economic profile before and after migration, personal and general reasons for migration, problems in the village and in the city, and perception of push- and pull factors. A remote sensing-based analysis of land cover and forest changes was conducted for two villages of the migrants’ origin, aiming to link the reasons for migration to land cover changes. This was contextualised by reported large scale changes in forest cover. Major reasons for migration mentioned in this study were education, employment opportunities with the associated income, and facilities. These were perceived as both, push and pull factors, whereas environmental factors ranked very low. Declining environment or agriculture were never mentioned spontaneously as personal reason, and only occasionally as a presumed general reason for migration, but were frequently confirmed as a major problem in the village. Thus, although such problems existed, they seemed not a major driver of rural-urban migration. For most of the respondents their migration resulted in a profound change of livelihoods and significantly improved their socio-economic situation. Land and forest cover around the chosen villages fluctuated by up to 15% with a trend to increasing forest cover in recent years. At the district and state scales, forest cover was rather stable. These results question the narrative of deforestation and environmental degradation in the Himalayas as major push-factors for rural-urban migration in Uttarakhand. Even if environmental constraints were felt, it was rather the differences in socio-economic opportunities (education, employment, facilities) that drove people to migrate to the city. Regarding the push-pull paradigm, we conclude that scenarios of external conditions under which people migrate cannot be evaluated without taking the migrants’ attitudes and choices into account.