Trends In Land Cover Change Research Articles

Long-term assessment of historical disturbances in the dry land ecosystems of Mutis Timau Nature Reserve, East Nusa Tenggara, Indonesia

Long-term assessment using historical satellite imagery is critical for gaining meta-information and identifying trends in land cover change. Informed policymaking about this assessment ensures forest sustainability and mitigates climate change to consider the identified trends in land cover changes and historical disturbances. Therefore, this study aimed to describe the trend of land cover change and historical disturbances in Mutis Timau Nature Reserve (CA Mutis Timau), Indonesia using the LandTrendr platform. The land cover classification is based on SNI 7645-1:2014, with a 1:50,000 scale. The data was processed using Google Earth Engine’s LandTrendr platform, which covered the vegetation index, specifically the normalized burn ratio (NBR). The results showed that the trend of land cover change in CA Mutis Timau remained stable from 1986 to 2023. These disturbance locations have low slopes and are located near neighborhoods. In specifics, the Eucalyptus forest land cover experienced the largest land cover change between 1986 and 2023, losing 945.68 ha of vegetation, or 8% of the total area of CA Mutis Timau. Over a duration of 38 years, from 1986 to 2023, the Eucalyptus forest had a cover change that turned into marble stone, barren land, grassland, and shrubs. Forest management policies, such as establishing a nature reserve area on Mount Mutis areas, had an impact on the long-term sustainability of vegetation land cover.

Applying a deep learning pipeline to classify land cover from low-quality historical RGB imagery

Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and class specificity. However, deploying deep learning algorithms to characterize present-day, modern land cover based on state-of-the-art data is insufficient for understanding trends in land cover change and identifying changes in and drivers of ecological and social variables of interest. These identifications require characterizing past land cover, for which imagery is often lower-quality. We applied a deep learning pipeline to classify land cover from historical, low-quality RGB aerial imagery, using a case study of Vancouver, Canada. We deployed an atrous convolutional neural network from DeepLabv3+ (which has previously shown to outperform other networks) and trained it on modern Maxar satellite imagery using a modern land cover classification. We fine-tuned the resultant model using a small dataset of manually annotated and augmented historical imagery. This final model accurately predicted historical land cover classification at rates similar to other studies that used high-quality imagery. These predictions indicate that Vancouver has lost vegetative cover from 1995–2021, including a decrease in conifer cover, an increase in pavement cover, and an overall decrease in tree and grass cover. Our workflow may be harnessed to understand historical land cover and identify land cover change in other regions and at other times.

Analysis of trends in productivity metrics in assessing land degradation: A case study in the Campania region of southern Italy

Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context.

Trends of Land Cover Change and Conservation Challenges of Grevy’s Zebra (Equus grevyi, Oustalet 1882) in Hallaydeghe Asebot Proposed National Park, Southeast Ethiopia

Large parts of biodiversity in protected areas (PAs) and their ecosystem services have been deteriorated due to land cover change over time. To reverse these effects, analysis of land cover change is essential. One of the protected areas, Hallaydeghe Asebot Proposed National Park (HAPNP), was assumed its land cover change and was undergone from one habitat type to others during the last three decades. As a result, wildlife species particularly Grevy’s zebra (Equus grevyi) in HAPNP are facing conservation challenges. Here, we aimed to examine the land cover changes of HAPNP for the period of 1990–2021 and assess Grevy’s zebra conservation challenges so as to improve the protection of the park and conservation of the species. Landsat images for the years 1990, 2006, and 2021 were generated to analyze land cover change of the study area. In addition, ground control points and information from elder and knowledgeable informants were collected to substantiate the result. Household survey, key informant interviews, and field observation checklist were utilized to collect data on conservation challenges. To generate land cover change maps, the satellite image for each period was classified using supervised classification. Content analysis was used to analyze conservation challenges of the species. The findings of this study revealed that 12.2% of grassland cover lost from 1990 to 2021 (i.e., an estimated annual average loss 0.4%). The remaining grassland cover in the HAPNP was aggregated in a small land area. On the other hand, the size of bushland increased by 26.4% during the same year (i.e., an estimated annual average gain 0.9%). These findings have implications for conservation and management of grazers. Major drivers for conservation challenges of Grevy’s zebra were habitat degradation, unintegrated development, and poor law enforcement practices. Grassland restoration program and species conservation challenges’ intervention strategy should be designed and implemented to safeguard the natural habitat and the species that occur in the HAPNP.

Climate Change Scenarios Reduce Water Resources in the Schuylkill River Watershed during the Next Two Decades Based on Hydrologic Modeling in STELLA

The Schuylkill River Watershed in southeastern PA provides essential ecosystem services, including drinking water, power generation, recreation, transportation, irrigation, and habitats for aquatic life. The impact of changing climate and land use on these resources could negatively affect the ability of the watershed to continually provide these services. This study applies a hydrologic model to assess the impact of climate and land use change on water resources in the Schuylkill River Basin. A hydrologic model was created within the Structural Thinking Experiential Learning Laboratory with Animation (STELLA) modeling environment. Downscaled future climate change scenarios were generated using Localized Constructed Analogs (LOCA) from 2020 to 2040 for Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 emission scenarios. Three regional land use change scenarios were developed based on historical land use and land cover change trends. The calibrated model was then run under projected climate and land use scenarios to simulate daily streamflow, reservoir water levels, and investigate the availability of water resources in the basin. Historically, the streamflow objective for the Schuylkill was met 89.8% of the time. However, the model forecasts that this will drop to 67.2–76.9% of the time, depending on the climate models used. Streamflow forecasts varied little with changes in land use. The two greenhouse gas emission scenarios considered (high and medium emissions) also produced similar predictions for the frequency with which the streamflow target is met. Barring substantial changes in global greenhouse gas emissions, the region should prepare for substantially greater frequency of low flow conditions in the Schuylkill River.

Assessing Coastal Land-Use and Land-Cover Change Dynamics Using Geospatial Techniques

Geospatial techniques can be used to assess the dynamic conditions of coastal land use and land cover in order to make informed decisions about future management strategies for sustainable development through a combination of remote sensing data with field observations of shoreline characteristics along coastlines worldwide. Geospatial techniques offer an invaluable method for analyzing complex coastal systems at multiple scales. The coastal land use and land cover from the Subarnarekha (Orissa) to the Rasulpur estuaries (West Bengal) along the Bay of Bengal are dynamically modified by a complex interaction between land and sea. This is due to various dominating factors of physical and anthropogenic activities, which cause changes in the landscape. The main objective of this study was to identify the periodical transformation and changes in land-use/land-cover (LULC) features by the USGS-LULC classification method using a maximum-likelihood classifier (MLC) algorithm and satellite images for the period 1975–2018. The entire study area was divided into three ‘littoral zones’ (LZs). This will help in understanding how LULC has changed over time, as well as providing insight into human activities impacting on coastal environments. This study focused on five features selected for LULC classification, namely, built-up, vegetation, soil, sand and shallow-water areas. The purpose of this study was to investigate human encroachment near shore areas as well as the transformation of soil and sand into built-up areas over a 43-year period from 1975 to 2018 using geospatial techniques. To estimate the changes in the areas, a geodatabase was prepared for each LULC feature. Finally, statistical analysis was performed on all available datasets, which allowed the researchers to identify trends in land-cover change from 1975–2018 within each category, such as increasing deforestation and urbanization rates due to increased population growth. The results of the study show the expansion of shallow-water areas, which is one of the major factors influencing coastal erosion. Maximum shallow-water-level enhancement was observed in LZ I and LZ II. In LZ I, shallow water increased from 1 km2 to 4.55 km2. In LZ II, the initial 1.7 km2 shallow-water area increased to 13.56 km2, meaning an increase of 11.86 km2 in shallow-water zones. A positive change was noticed in vegetation area, which increased from 2.82% (4.13 km2) to 15.46% (22.07 km2). Accuracy assessment was applied for all classified images, and more than 85% accuracy was considered for the overall accuracy assessment. Finally, Kappa coefficient statistics were adopted to complete the accuracy analysis, and 80% or more than 80% accuracy was obtained for all classified images. This information can also help inform policy makers about potential environmental impacts associated with certain activities, such as coastal development and agricultural expansion, so that appropriate steps can be taken towards mitigating these impacts before it is too late.

Деградація земних покривів Харківської області внаслідок воєнних дій

Background. The analysis of trends in land cover changes will allow to detect trends in land cover changes, which will contribute to the understanding of the reasons for changes in natural ecosystems and the development of strategies for their preservation and restoration. The analysis of land cover changes can be useful for assessing the impact of anthropogenic and natural factors on land using, which is important for making decisions about the rational using of natural resources, especially in the post-war period. The purpose of the research is to allow to detect the trends of land cover changes in Kharkiv region as a result of hostilities, using the satellite materials and statistical data from open sources. Methods. For the research, materials from remote sensing of the Earth were utilized, including methods of their processing and visualization. M a t h e m a t i c a l and statistical methods were applied to process statistical information and obtain index images resulting from the processing of satellite images and the generation of thematic raster data. The article conducts an analysis of the main trends in land cover changes in the Kharkiv region, highlighting trends for the most significantly altered land cover types, specifically forests and agricultural lands, for the period 2015-2022. Results. A general trend since 2015 has been the reduction of forested areas and agricultural lands, with a significant decrease in 2022, as a result of military actions and their consequences. Land cover degradation due to military activities is currently a prominent trend, representing the primary tendency in land cover changes. As a result of military actions in the Kharkiv region, all natural resources have suffered: lands from pollution and contamination with various types of waste, atmospheric air from emissions due to explosions and fires, water bodies, forest resources, and fauna from enemy equipment, pollution, and deliberate destruction. Conclusions. For a more detailed study of land cover changes related to military activities, especially regarding the destruction of the humus layer, soil pollution with metal fragments and explosive residues, field research and the use of high-resolution aerial or satellite imagery are necessary.

Study of changes in land cover categories in Ukraine based on remote sensing data

Land cover change has been a hot area of research on global ecological change and sustainable development due to its importance in global ecological change. Understanding land cover change trends is the basis for rational planning and management of land resources and is of important value for achieving land protection and sustainable development. Land transfer matrix has great value in the research of land cover change, its results are not disturbed by the land cover category and quantity, and the data can be analyzed in different time periods according to the demand. However, the land transfer matrix produced by traditional methods has the problems of long production period and certain requirements for hardware performance. In this paper uses Google Earth Engine to obtain the public land cover dataset of Ukraine and uses raster calculation to quickly construct the land transfer matrix. The matrix data show that the land cover change in Ukraine from 2000-2015 is modest, with a total change of 2.244%. The proportion of cropland decreased and the proportion of Urban and Built-up Lands increased. The results show that the methods can quickly and effectively obtain data on land cover change in the study area and provide assistance in analyzing trends and patterns of land cover change.

Modelling of land-use/cover change trajectories in a transboundary catchment of the Sio-Malaba-Malakisi Region in East Africa using the CLUE-s model

A comprehensive undertanding of land-use change processes and their future trajectories is essential for the development of land-use management plans required for conservation of ecological resources. In East Africa, however, there is still limited information on historical, current and future land cover change trends within the vulnerable watersheds. In such data constrained areas, a few studies have adapted spatial models, employing historical land cover change trends derived from satellite remote sensing to provide the requisite landscape metrics for ecological modelling and management. In this study, the ensemble Conversion of Land Use and its Effects at Small regional extent (CLUE-s) model was used to model potential land-use/cover change trajectories in the transboundary Sio-Malaba-Malakisi Watershed of Kenya and Uganda. Historical change trends were obtained from consistently classified land cover datasets. The model parameters, which included climatic, biophysical and human factors were estimated from physical datasets in a Geographical information System (GIS). Two scenarios that represented options for agricultural expansion (Scenarion I), and land reclamation (Scenarion II) were investigated for the period between 2027 and 2047 to support ongoing regional inititiaves towards ecological resource conservation and watershed management across the transboundary region. From the results, Scenario I indicated an increase in the cropland area by 24%, covering approximately 62% of the whole basin area by 2047. A Scenario II indicated a 30% increase in forest cover, indicating an improvement in forest recovery and restoration of ecosystem area. The results obtained can be used to support baseline studies on the management of the transboundary region with varied country-management options.

Change detection in a rural landscape: A case study of processes and main driving factors along with its response to thermal environment in Farim, Iran.

This study aims to investigate the alteration of Land Use/Land Cover (LULC) change and its response to changes in land surface temperature (LST) and heat island phenomena of a rural district known as Farim in the north of Iran from 1990 to 2020 using multi-date Landsat data. The random forest-based algorithm, supported by Google Earth Engine, is used to execute the LULC classification with an overall accuracy of more than 92%. Based on the LULC results, in terms of area changes, the classes of bare land, rice fields, and water bodies encountered an increase, but woods and dry farms decreased. The present study also incorporates the trends of land cover change that are analyzed using regression based on the temporal datasets of the three leading driving factors: temperature, precipitation, and population. The result demonstrates that the main changing factors of the mostly changed class (bare land) are population/precipitation and temperature/population. Additionally, the effect of LULC change on seasonal LST and urban heat island (UHI) is also analyzed in this study. The result witnessed a significant LST rise in the summer and winter seasons of about 12.87°C and 14.2°C, respectively over the study period. The Urban Thermal Field Variance Index (UTFVI), characterizing the heat island phenomenon, shows that the strongest UTFVI zone is in the central area and thenone UTFVI zone is in the surrounding region. Moreover, both seasons have seen a significant rise in none UTFVI zones compared to decreasing strongest UTFVI zone. The result of the present study will be helpful for urban planners and climate researchers who study future land cover change and its associated driving factors.

Perubahan Tutupan Lahan Pada Daerah Aliran Sungai Sekampung Hulu, Lampung Menggunakan Data Pengindraan Jauh

Land cover in the upstream watershed area is changing rapidly due to anthropogenic activity. This study aims to determine the dynamics of land cover change in the Sekampung Hulu watershed. The survey used Landsat satellite imagery data for 2008, 2015, and 2021 obtained from the United States Geological Survey and analyzed it using guided classification with the Object Oriented Classification algorithm using Ecognition and ArcGIS 10.3 software. The study's results obtained a kappa accuracy test of 95.85%, which showed a trend of land cover change for the 2008-2015 and 2015-2021 periods. Land cover change trends are classified into seven classes. In the 2008-2015 period, cloud cover decreased by 158.01 ha, water bodies rose by 1,630.03 ha, forests decreased by 2,298.83 ha, mixed gardens increased by 497.92 ha, coffee stands increased by 692.49 ha, open land decreased by 1,112.39 ha and shrubs increased by 760.79 ha. The 2015-2021 period, namely clouds rose by 649.65 ha, water bodies rose by 137.48, forests fell by 1,788.49 ha, mixed gardens fell by 648.95 ha, coffee stands rose by 593.41 ha, open land increased by 694.68 ha and shrubs increased by 1,766.18 ha. Forest cover, coffee stands, and shrubs are the highest fluctuating cover compared to another cover, and mixed forest and plantation cover is the dominating cover in the upstream Sekampung watershed.

Land cover change detection in the Turkish coastal zone based on 28-year (1990-2018) Corine data.

In this research, land cover changes in the coastal region of Turkey were analyzed using the Corine dataset between 1990 and 2018. Changes in each period were analyzed by using the rate of change and the annual rate of change, the transition matrix of net changes, and the transition probability matrix. In order to predict land cover change trends and to determine future land cover change probabilities, a combination of Markov and cellular automata models was used. It was determined that the highest increase in each study period was in artificial surfaces and the highest decrease was in the forest and the semi-natural area. The total forest areas were converted to the other land cover type in the first study period amounting to 2479.32 km2. Also, it was decreased and transformed by about 62.47 km2, 118.82 km2, and 203.09 km2 of the forest area that existed in the second, third, and fourth periods respectively. The results indicate that the probabilities of the increasing area will be covered by artificial surfaces and agricultural areas. It is estimated that the rate of 57% in 1990, the initial year of the forest areas, will decrease to 53.4% in 2034 and to 53% in 2050. Also, the rate of 2.1% in 1990, the initial year of the artificial surfaces, will increase to 4.5% in 2034 and to 5.0% in 2050. It is seen that more artificial surfaces will be needed in the Turkish coastal region due to the increasing population and number of tourists. It is important to evaluate and investigate the coastal areas where more artificial areas are expected to be needed within the scope of the coastal area management plans to be prepared at the national scale.

Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach

Both natural processes and anthropogenic actions alter land use and land cover but the impact of anthropogenic actions has dramatically increased since the last century in many regions of the world. This study attempts to investigate the land cover change of the Sundarbans and surrounding areas of Bangladesh in the last forty years by analyzing Landsat images. Sundarbans, having the richest and exceptional biodiversity, is the largest remaining tract of mangrove forest on earth facing multidimensional threats of degradation. Time series Landsat satellite images collected for five years are 1980 (MSS), 1990 (TM), 2000 (TM), 2010 (TM) and 2020 (OLI). By analyzing, comparing and visualizing the classified images using ArcMap 10.5 software the historical trend of land cover change of the study area is exhibited. The result revealed that a large amount of non-vegetated areas located outside of Sundarbans mangrove forest converted into water bodies due to shrimp cultivation. During this long time, the Sundarbans mangrove forest area remained unchanged except for little change occurred by soil erosion along the river bank and coastline. Kappa coefficient revealed the accuracy of the land cover map with a coefficient value of 0.88, 0.92, 0.90, 0.93 and 0.92 for 1980, 1990, 2000, 2010 and 2020 images respectively. This research will support policymakers and the forest department to improve management policy. Also, influence researchers to conduct case-specific micro-scale research in the region. • Land cover change of Sundarbans Mangrove forest and surrounding area was analysed using GIS and RS techniques. • Excessive shrimp cultivation altered land cover outside of Sundarbans. • Sundarbans Mangrove forest remained unchanged over the last 40 years except for little change occurred by soil erosion. • Cyclone, sea level rise and decreased river water flow affecting the coastal island and the river bank. • Kappa coefficient value of 0.88, 0.92, 0.90, 0.93 and 0.92 found for 1980, 1990, 2000, 2010 and 2020 images respectively.

KAJIAN PEMANTAUAN PERUBAHAN PENUTUPAN LAHAN BERBASIS PENGINDERAAN JAUH DI KOTAWARINGIN BARAT

The first step in monitoring deforestation is to measure and identify land cover changes. Our research uses remote sensing technology, because it has advantages such as: cheap, transparent, fast and has measurable accuracy and temporal analysis. This study aims to identify, measure, and analyze trends in land cover change in 1990-2000, 2000-2012, and 2012-2021. The results showed that there were 18 land cover classes with details of 3 forest classes; and 15 non-forest classes; Forest class decreased throughout 1990 to 2021, the trend of changing forest cover was deforestation into plantations, swamp shrubs, dryland agriculture mixed with shrubs, and swamps. The rate of deforestation in 1990-2000 was 3.07%, deforestation in 2000-2012 was 3.02%, and deforestation in 2012-2021 was 1.02%. The accuracy of land cover classification is 91%.

Multi-temporal analysis of past and future land cover change in the highly urbanized state of Selangor, Malaysia

BackgroundThis study analysed the multi-temporal trend in land cover, and modelled a future scenario of land cover for the year 2030 in the highly urbanized state of Selangor, Malaysia. The study used a Decision Forest-Markov chain model in the land change modeller (LCM) tool of TerrSet software. Land cover maps of 1999, 2006 and 2017 were classified into 5 classes, namely water, natural vegetation, agriculture, built-up land and cleared land. A simulated land cover map of 2017 was validated against the actual land cover map 2017. The Area Under the Curve (AUC) value of 0.84 of Total Operating Characteristics (TOC) and higher percentage of components of agreement (Hits + Correct rejection) compared to components of disagreement (Misses + False alarm + Wrong hits) indicated successful validation of the model.ResultsThe results showed between the years 1999 to 2017 there was an increase in built-up land cover of 608.8 km2 (7.5%), and agricultural land 285.5 km2 (3.5%), whereas natural vegetation decreased by 831.8 km2 (10.2%). The simulated land cover map of 2030 showed a continuation of this trend, where built-up area is estimated to increase by 723 km2 (8.9%), and agricultural land is estimated to increase by 57.2 km2 (0.7%), leading to a decrease of natural vegetation by 663.9 km2 (8.1%) for the period 2017 to 2030. The spatial trend of land cover change shows built-up areas mostly located in central Selangor where the highly urbanized and populated cities of Kuala Lumpur and Putrajaya and the Klang valley are located.ConclusionThe future land cover modelling indicates that built-up expansion mostly takes place at edges of existing urban boundaries. The results of this study can be used by policy makers, urban planners and other stakeholders for future decision making and city planning.

Identification of land cover changes before and after forest and land fires in conservation areas of North Sumatra, Indonesia

Damage to conservation areas in North Sumatra can be mitigated by understanding the pattern of land cover change, which can be performed by detecting hotspots and their temporal and spatial patterns. This study aimed to explore land cover spatially and temporally before and after forest fires in the conservation areas in North Sumatra. Data on the hotspots, satellite imagery, land cover maps, and field verification were used to see the transition of land cover changes before and after forest and land fires. Temporal and spatial analysis was employed to see the trend of land cover changes of the land before and after the fires. Field verification was conducted through observations and interviews related to land cover conditions in the field caused by forest and land fires. This study found three conservation areas with the highest number of hotspots in 2014 in the period 2001-2019, including Gunung Leuser National Park (GLNP), Dolok Surungan Wildlife Reserve, and Barumun Wildlife Reserve during the 2001-2019 period. In 2010 and 2014 there were strong indications of a large area of land burning in three conservation areas. The burned land was near the outer boundary of the conservation areas and continued to expand into the areas over time. The area of the non-forest cover was relatively stable between periods prior to the discovery of several hotspots. Changes in forest cover to non-forest have become more widespread after the highest number of hotspots were detected in 2010. Conservation area management and various parties need to prevent the expansion of forest clearing considering the strategic role especially in GNLP as a world heritage.

Impacts of land cover change on the plant resources of an endangered pollinator.

One of the key drivers of pollinator declines is land cover change. We documented for the first time the impacts of over three decades of land cover change in Mexico on the plant resources of an endangered migratory pollinator, the Mexican long-nosed bat, Leptonycteris nivalis. This species is considered endangered under national and international criteria due to population declines over 50% in the past 10 years. Pregnant females of this bat species migrate every year following the blooms of Agave spp. from central Mexico to the southern United States; moving pollen over its 1,200 km long migratory corridor and pollinating distant populations of Agave spp. Increases in human populations density and agricultural expansion may be reducing agave habitat over time. The objective of our study is to understand the land cover change trends in the northern range of the bat and identify potential fragmentation patterns in the region. We analyzed changes that occurred in three vegetation types where agaves are found in five time periods 1985, 1993, 2002, 2007 and 2011. The area of the three vegetation types selected was reduced by using only the overlap with potential agave habitat created with ecological niche modeling algorithms to obtain the available agave habitat. We then calculated fragmentation metrics for each period. We found a significant portion of habitat lost mainly due to expansion in agriculture. The total number of patches increased after 1985. Only 9% of the available agave habitat in 2011 is inside the limits of protected areas. We recommend restoring agave populations in depleted areas to help prevent soil erosion and provide multiple socio-economic benefits for the region in the short term, and, in the long-term maintaining foraging resources for nectar-feeding bats.

Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

Exploring the potential of soil moisture maps using Sentinel Imagery as a Proxy for groundwater levels in peat

Degraded peatlands are extremely vulnerable to the threat of fires and have been a major source of national greenhouse gas emissions. Maintaining a certain level of water in peatlands is an essential measure of disaster vulnerability in peatlands. During the dry season, when the lower part of the peat still retains water, fires only occur on the surface and are relatively easy to extinguish. However, one of the limiting factors in peatland management and its more comprehensive application has been the availability of sufficient and spatially distributed Groundwater Level (GWL) data. This study explores the soil moisture map as a proxy for peat condition indicators that correlate with groundwater level. The case studies conducted at Tumbang Nusa Research Forest and Peat Hydrological Unit of Kahayan Sebangau show that peatland conditions can be estimated through biophysical parameters detectable from remotely-sensed data. Soil Moisture Map (SMM) can be produced with a higher resolution (Sentinel 1 = 10m) using the free and open tools SEPAL based on cloud computing infrastructure. The Support-Vector-Regression machine learning approach is used to estimate soil moisture. There is a correlation between SMM and GWL. However, the response to land cover varies. There is high uncertainty in densely forested areas where the sensors cannot penetrate the canopy. As a result, in its implementation, the SMM can be combined with the vegetation index, which can describe trends of land cover changes.

The relationships between economic growth and cropland changes in Bangladesh: An evidence based on annual land cover data

Bangladesh is experiencing rapid economic progress since 1990s, at the same time, facing acute shortage of agricultural land. Using 19 years (2000-2018) time series remote-sensing data and Random Forest machine learning algorithm in Google Earth Engine (GEE), this study classifies the land covers in Bangladesh; determines the trend of land cover changes using least-square growth rate model; and finds the associations between the trends of cropland and growth of socio-economic factors using correlation and regression analysis. The annual average area of cropland decline is 29271 hectare, which is higher than the absolute changes of other major land covers, such as forest (25932 hectare) and built-up area (1649 hectare). With the annual growth of urban population (3.81%) and gross domestic product (GDP) (6.28%), the predicted declining rate of cropland is estimated 0.29%, which is very close to the observed annual declining rate (0.28%). However, the partial effects of urban population and GDP could not be detected. Therefore, the study infers that urbanization and economic growth in Bangladesh are happening simultaneously. As a result, the joint determination of both phenomena can explain the degradation of agricultural land in Bangladesh.