Spatio-temporal Land Cover Changes Research Articles

Spatio-Temporal Changes in Land Cover and the Spatial Distribution of Soils in the Damyang Riverine Wetland

Spatio-Temporal Changes in Land Cover and the Spatial Distribution of Soils in the Damyang Riverine Wetland

Estimation of Spatiotemporal Changes of Land Cover in Llapi River Catchment (Northeastern Part of Kosova)

Aim of the studyIn recent decades, Kosova in general and the Llapi River catchment have experienced landscape transformation which resulted in Land Use and Land Cover (LULC) changes, mainly in flat terrains where most of country’s population is concentrated. Other landscapes have experienced changes too, but on a small scale. The aim of the study was to identify landscape transformation and driving forces behind it.Material and methodsSpatiotemporal changes of Land Use and Land Cover (LULC) were made using GIS techniques. Land Cover data from Copernicus Land Monitoring Service (CLMS) with spatial resolution 100 m for three different years: 2000, 2006 and 2018 were analyzed. DEM with 10 m spatial resolution was used to estimate slope steepness and catchments’ altitude. Population data for settlements inside the Llapi River catchment represented in features were downloaded from the Statistical Agency of Kosova, and those data include population numbers for official censuses between 1948 until 2011, while ArcMap 10.8 environment was used for analysis.Results and conclusionsWith GIS technique in analyzing Land Use and Land Cover data (LULC) and other open source data, we found that agricultural land have decreased in area by 9.53%, while the increase of artificial surface is 160.87%, or expressing by absolute values, total agricultural areas in 2000 were 37,827 ha, while in 2018 were 34,334 ha, meaning 0.3ha per capita, less than European level. In order to protect agricultural land, zoning and land use plans should be implemented.

Assessing the Impact of Spatiotemporal Land Cover Changes on the Urban Heat Islands in Developing Cities with Landsat Data: A Case Study in Zhanjiang

Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC’s impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle.

Mind the gap: reconciling tropical forest carbon flux estimates from earth observation and national reporting requires transparency

BackgroundThe application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be “managed” by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs.ResultsBetween 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (− 0.2 GtCO2yr−1), while Brazil’s NGHGI reported a net carbon source (+ 0.8 GtCO2yr−1). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory’s methodology, the EO net flux became a source of + 0.6 GtCO2yr−1, comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO2 yr−1), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO2 yr−1; Global EO: + 0.2 GtCO2 yr−1). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments.ConclusionsBy adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification.

Assessing spatiotemporal changes in landcover using geospatial and remote sensing techniques in the Southern fringes of Chennai.

Peri-urban areas are transitional zones on the outer boundaries of cities. These regions have immense growth potential, and it is necessary to observe the land use landcover changes to understand the dynamics of these transformations. The area selected for this study is towards the Southern fringe of Chennai, Tamil Nādu, India, and is analyzed using multi-spectral satellite imagery from Landsat 5 and 8. The primary objective of the study is to assess the change in landcover classes, namely water, land, and vegetation, over a 30-year study period between 1991 and 2021. The peri-urban regions majorly are arable land. Hence, NDVI is considered a suitable index to monitor the landcover changes. The spatiotemporal analysis indicates an increase of 19.43% in land /barren areas towards the Northern parts near the study area and along the transit and industrial corridors. No significant changes are observed in the areas of vegetation that could be attributed to efforts taken to conserve reserve forests and increase green zones in newer developments. A steep depletion of 46.86% of water bodies observed in the region also corresponds to water scarcity problems. Accuracy was assessed using ground-truthing methods, computing the confusion matrix and Kappa coefficient. NDVI is used efficiently in the landcover classification but does not indicate the difference between built-up areas and barren land. Change detection map prepared using ARCGIS indicates the areas that have been converted to other landcover over a period of 30 years. The study reveals an urgent need to bring in policy decisions to conserve waterbodies and green spaces in the initial stages of urban planning for sustainable developments in the fringe areas.

An improved framework for assessing the impact of different urban development strategies on land cover and ecological quality changes -A case study from Nanjing Jiangbei New Area, China

Land cover (LC) change has been highly valued by policy makers for driving the evolution of regional ecological and environmental quality. The core area of Nanjing Jiangbei New Area (NJNA) has undergone the development process from traditional urbanization to new-type urbanization (NTU), however the impacts of different urban development strategies or pathways on the change pace and direction of land cover and ecological quality are not adequately understood in a quantitative manner which is not conducive for similar regions to learn lessons from past development processes. Based on RapidEye, GF-1 and SPOT-6 images acquired in 2009, 2015 and 2021, a Modified Mask-RCNN framework was devised and implemented to create a high accuracy LC dataset first in this analysis. Then, the applicability or efficacy of the Remote Sensing Ecological Index (RSEI) model and the Regional Optimized Remote Sensing Ecological Index (RO-RSEI) model, developed from the Landsat observations was compared in characterizing the ecological quality. Finally, the attribution analysis of spatio-temporal changes in LC and their induced ecological quality evolutions was carried out in conjunction with socio-economic factors. The results showed that the overall accuracies of the LC classifications were estimated at 91.50 % (2009), 91.25 % (2015), and 92.75 % (2021), respectively. The mean values of RO-RSEI analyses were at 0.652, 0.649, and 0.656, respectively, indicating that in the past 12 years, the ecological quality first got degraded then got ameliorated along with the urbanization. The urban green spaces got restored and increased in the NTU process, and protecting the forest and cropland was also reinforced thanks to the strict enforcement of related policies. These positive changes are attributed to the popularization of ecological civilization philosophy and the escalation of social awareness of environmental protection and low carbon development. The proposed framework can provide technical reference for ecological quality monitoring under the progress of NTU.

Assessment of human-induced effects in the Sultan marshes (Ramsar Protection), Kayseri (Turkey).

This study examines the drying in the Sultan Marshes and the spatio-temporal change of different land cover classes. Corine land cover change outputs were examined for four periods (1990-2000; 2000-2006; 2006-2012; and 2012-2018). During these analyses, the period when the water area changes in the lakes occur the most was determined. Moreover, other land cover changes occurring in the region were defined. The LCC results were compared and discussed in terms of some human factors (i.e., human development index and terrestrial human footprint). According to the results of this study, it was observed that there was a severe decline in the lake surface water located in the Sultan Marshes National Park Area. The water's surface in the lakes decreased by 50% in the 2000s compared to previous years and decreased until 2006. This withdrawal was prominent especially in Lake Yay and Lake Çöl. Considering the human factors (Human Development Index) and variables (terrestrial Human Footprint) in terms of the spatio-temporal land cover change, it is seen that the human development in the region increased from 0.54 to 0.81 from 1990 to 2018, and the human footprint increased the most in 1993. Water area changes occurred at a high rate between 1990-2000 and 2000-2006. It results from the growing demand for basic needs (such as water consumption and food diversity) with increasing human development and expanded agricultural practices in the region and overuse of the ground and aboveground waters that are the source of the lakes. Especially between 1990 and 2000, the high number of human interventions in the region caused the human footprint to be higher in 1993 than in 2009. Unless the Sultan Marshes have the proper planning and policies, it faces the danger of complete drying up with the effects of climate change in the future.

Geospatial measurement of urban sprawl using multi-temporal datasets from 1991 to 2021: case studies of four Indian medium-sized cities

In recent decades, medium-sized Indian cities have experienced accelerated urban growth due to the saturation of large cities. Such rapid urban growth combined with inadequate urban planning has triggered urban sprawl in medium-sized Indian cities. In this context, the present study focuses on the geospatial measurement of urban sprawl in four rapidly expanding Indian medium-sized cities located in diverse physiographic regions, such as Lucknow urban agglomeration (UA), Bhubaneswar UA, Raipur UA, and Dehradun UA. Multi-temporal Landsat imageries from 1991 to 2021 were downloaded for land cover classification through the maximum likelihood classification tool in ArcGIS 10.3. Thereafter, spatiotemporal land cover change detection was performed based on the classified land cover maps. The presence of urban sprawl was detected using the relative entropy index while the urban expansion index quantified the urban sprawl typologies such as edge expansion, leapfrog development, and ribbon development. The results exhibited a rapid rise in built-up land cover from 1991 to 2021. The prevalence of urban sprawl was detected in all four cities as per the relative entropy index. Edge expansion typology of urban sprawl was dominant compared to leapfrog development and ribbon development. Such urban growth phenomenon creates a hindrance in promoting sustainable urban development in medium-sized Indian cities. The results obtained from this paper would assist urban planners and policymakers in developing strategies to encourage planned urban growth. This paper exhibits the potential of geoinformatics to monitor and analyze urban sprawl.

Delineation and evaluation of the captive limestone mining area change and its influence on the environment using multispectral satellite images for industrial long-term sustainability

Dynamics in Limestone mining sites are usually subjected to spatiotemporal land cover change, which has a long-term impact on the land and environment. The objectives of this article are mapping demarcation, and transformation assessment of active captive limestone mines and their impact assessment on the environment at the Yerraguntla cement industrial region in YSR Kadapa district, Andhra Pradesh, India from the year 2006–2019 using Remote Sensing & Geographical Information System (RS&GIS). In this research work, first, the Normalized Differential Vegetation Index (NDVI) is computed, second NDVI image is resampled using NDVI threshold values, and the Nearest-Neighbor sampling method uses the QGIS raster calculator, which produces the mines area of 469.92 ha with Sentinel-2A MSI and 555.39 ha with Landsat-8(OLI) which is evaluated with delineated high resolution (∼1m/pixels) Google Earth image (486.47 ha) from Google Earth Pro (freeware) and industry reports (487.10 ha) in 2019. Third active mines are demarcated using NDVI breakpoints. Results found that the limestone Mining area increased by 206.10% from 2006 to 2019, with Producer's Accuracy (PA), User's Accuracy (UA), and Kappa coefficients of 98.82%, 92.79%, and 0.92 in 2019, and 96.74%, 85.17 %t, and 0.82 in 2006. The compatibility between Landsat-8 and Sentinel-2A NDVI images is determined with Pearson's correlation coefficient (PCC) of 0.84 in 2019. In 2019, a linear negative Regression slope was revealed between NDVI & LST with a Correlation coefficient of 0.248, indicating negative environmental influences and vegetation control on LST. This research will aid industry and environmental engineers in comprehending the effects of limestone mine changes on LST and proposing an environmentally acceptable land-environmental management plan for long-term sustainability.

Overview of recent land cover changes, forest harvest areas, and soil erosion trends in Nordic countries

Mapping spatiotemporal land cover changes offers opportunities to better understand trends and drivers of environmental change and helps to identify more sustainable land management strategies. This study investigates the spatiotemporal patterns of changes in land covers, forest harvest areas and soil erosion rates in Nordic countries, namely Norway, Sweden, Finland, and Denmark. This region is highly sensitive to environmental changes, as it is experiencing high levels of human pressure and among the highest rates of global warming. An analysis that uses consistent land cover dataset to quantify and compares the recent spatiotemporal changes in land cover in the Nordic countries is missing. The recent products issued by the European Space Agency and the Copernicus Climate Change Service framework provide the possibility to investigate the historical land cover changes from 1992 to 2018 at 300 m resolution. These maps are then integrated with time series of forest harvest areas between 2004 and 2018 to study if and how forest management is represented in land cover products, and with soil erosion data to explore status and recent trends in agricultural land. Land cover changes typically involved from 4% to 9% of the total area in each country. Wetland showed the strongest reduction (11,003 km2, −11% of the wetland area in 1992), followed by forest (8,607 km2, −1%) and sparse vegetation (5,695 km2, −7%), while agriculture (15,884 km2, 16%) and settlement (3,582 km2, 84%) showed net increases. Wetland shrinkage dominated land cover changes in Norway (5,870 km2, −18%), followed by forest and grassland with a net gain of 3,441 km2 (3%) and 3, 435 km2 (10%), respectively. In Sweden, forest areas decreased 13,008 km2 (−4%), mainly due to agriculture expansion (9,211 km2, 29%). In Finland, agricultural areas increased by 5,982 km2 (24%), and wetland decreased by 6,698 km2 (−22%). Settlement had the largest net growth in Denmark (717 km2, 70%), mainly from conversion of agriculture land. Soil erosion rates in Nordic countries are lower than the global average, but they are exacerbating in several locations (especially western Norway). The integration of the land cover datasets with maps of forest harvest areas shows that the majority of the losses in forest cover due to forestry operations are largely undetected, but a non-negligible share of the forest-to-agriculture (up to 19%) or forest-to-grassland (up to 51%) transitions overlap with the harvested sites. Forestry activity in the study region primarily involves small-scale harvest events that are difficult to be detected at the 300 m resolution of the land cover dataset. An accurate representation of forest management remains a challenge for global datasets of land cover time series, and more interdisciplinary international efforts are needed to address this gap. Overall, this analysis provides a detailed overview of recent changes in land cover and forest management in Nordic countries as represented by state-of-the-art global datasets, and offers insights to future studies aiming to improve these data or apply them in land surface models, climate models, landscape ecology, or other applications.

Analysis of spatio-temporal changes of land cover and landscape pattern in Guangdong-Hong Kong-Macao Greater Bay Area

Analysis of spatio-temporal changes of land cover and landscape pattern in Guangdong-Hong Kong-Macao Greater Bay Area

Using SCS-CN and Earth Observation for the Comparative Assessment of the Hydrological Effect of Gradual and Abrupt Spatiotemporal Land Cover Changes

In this study a comparative assessment of the impacts of urbanization and of forest fires as well as their combined effect on runoff response is investigated using earth observation and the Soil Conservation Service Curve Number (SCS-CN) direct runoff estimation method in a Mediterranean peri-urban watershed in Attica, Greece. The study area underwent a significant population increase and a rapid increase of urban land uses, especially from the 1980s to the early 2000s. The urbanization process in the studied watershed caused a considerable increase of direct runoff response. A key observation of this study is that the impact of forest fires is much more prominent in rural watersheds than in urbanized watersheds. However, the increments of runoff response are important during the postfire conditions in all cases. Generally, runoff increments due to urbanization seem to be higher than runoff increments due to forest fires affecting the associated hydrological risks. It should also be considered that the effect of urbanization is lasting, and therefore, the possibility of an intense storm to take place is higher than in the case of forest fires that have an abrupt but temporal impact on runoff response. It should be noted though that the combined effect of urbanization and forest fires results in even higher runoff responses. The SCS-CN method, proved to be a valuable tool in this study, allowing the determination of the direct runoff response for each soil, land cover and land management complex in a simple but efficient way. The analysis of the evolution of the urbanization process and the runoff response in the studied watershed may provide a better insight for the design and implementation of flood risk management plans.

A Statistical Approach to Detect Land Cover Changes in Mediterranean Ecosystems Using Multi-Temporal Landsat Data: The Case Study of Pianosa Island, Italy

The normalized difference vegetation index (NDVI) is commonly used to detect spatiotemporal changes of vegetation cover. This study modeled the spatiotemporal changes of land cover on Pianosa Island, Italy, in the period 1999–2015, using the multi-temporal Landsat images. Since the end of the 1990s, the natural vegetation has been re-colonizing an area of abandoned agricultural land and the island is undergoing a process of re-naturalization in harsh (drought and hot) environmental conditions. Hence, it is an ideal test site to monitor the effects of anthropogenic and climatic stressors on vegetation dynamics under Mediterranean climate. In this work, we proposed a new statistical approach based on a pixel-by-pixel analysis of multi-temporal Landsat images. Mean (µ) and standard deviation (σ) values of the NDVI images taken in 2015 were used for the determination of the pixel thresholds (µ ± 3σ). The evaluation of land cover change was carried out by comparing the µ value of a single NDVI pixel for 2015 with the same pixel of different years of the study period. The results indicate that surface reflectance (SR) Landsat images are more suitable in detecting the vegetation dynamics on the island than the top of atmosphere (TOA) ones and highlight an increasing trend of vegetation cover on Pianosa Island, mainly during the early seven years following the land abandonment in all the main land cover classes: abandoned crops and pastures, Mediterranean macchia, and woodland. However, the abandoned agricultural and pasture areas showed a higher increase in the vegetation cover and a shift in the shape of the normalized frequency distribution of the SR NDVI data during the study period, suggesting that a colonization process from other vegetation classes is occurring (i.e., Mediterranean macchia and trees are colonizing the abandoned land, partly replacing herbaceous species). Our data highlight that the statistical approach applied in this study is suitable for detecting vegetation cover changes associated with anthropogenic and climatic drivers in a typical Mediterranean environment and could be proposed as a new methodological approach in several other land monitoring studies.

Spatio-Temporal Land Cover Changes in Wassa Amenfi East and Upper Denkyira East Districts of Ghana

Spatio-Temporal Land Cover Changes in Wassa Amenfi East and Upper Denkyira East Districts of Ghana

Impact of human and environmental factors on land cover changes of an oak silvopastoral system

Silvopastoral systems integrate the complexity of forestry and forage-livestock management in an economically and ecologically sustainable way. However, these systems have undergone intensive pressure over the past decades caused by changes in land uses, as well as, in local socioeconomic priorities that suppressed traditional practices such as livestock grazing. These changes have caused land cover spatiotemporal diversifications whose effects were investigated in a traditional oak silvopastoral system at western Greece. In order to investigate the effect of human activities over the study area, the spatiotemporal land cover changes in conjunction with human and animal population changes and the environmental factors altitude, soil depth, inclination and rock type were analyzed. Also, forest-crown density changes were examined as a complimentary factor for the investigation of human intervention in the study area. Data about land-cover changes were derived from aerial orthorectified photographs of years 1945 and 2008, complimented by maps of environmental factors. Data digitization was implemented by the use of ArcGis. Based on the data analysis it derives that land cover changes are mainly attributed to direct human intervention. Environmental factors affect the ecosystem in two ways; first by affecting the location, type and intensity of human activities in the area, for example, soil depth determines where land is more productive and so predetermines the location, type and intensity of agricultural activities. Secondly, in the areas without or with minimal human intervention, environmental factors affect directly the land cover in conjunction with natural species necessities and their antagonistic capabilities.

Investigating the interaction between agricultural lands and Urmia Lake ecosystem using remote sensing techniques and hydro-climatic data analysis

Urmia Lake (UL) located in the northwest of Iran, is one of the largest hypersaline lakes in the world. In recent years, most of the lake has been rendered to unusable lands. Drought and rapid increase in agricultural activities are the most important reasons behind the shrinkage of the lake. In this study, hydro-climatic data, Landsat satellite images and image processing techniques were used to detect the spatio-temporal land cover changes and salinization progress in Urmia Lake Basin (ULB) between 1975 and 2019. Increasing the area of irrigated lands from 1265 km2 in 1975 to 5525 km2 in 2011 in contrast to decreasing the water surface area of UL from 5982 km2 in 1995 to 586 km2 in 2014 and extension of salinization in the basin are the most important and thoughtful results of this study. Even the agricultural lands in the regions close to the lake have been affected by this environmental problem. The climatic conditions have gradually improved after 2014 and the government has released more water from dams to the lake. On the other hand, the area of irrigated lands has gradually decreased by 12% in the same period. As a result of these positive changes, the water surface area of the lake has gradually increased over 1000 km2. Based on the results of this study, both anthropogenic and climatic factors have played a positive role in UL restoration. Improvement of agricultural methods and providing a sustainable agricultural water management system under a changing climate can play the most effective role in the lake rehabilitation.

Assessment of Small-Scale Ecosystem Conservation in the Brazilian Atlantic Forest: A Study from Rio Canoas State Park, Southern Brazil

The efficiency of the environmental management of a territory largely depends on previous surveys and systematic studies on the main elements and conditions of the physical environment. We applied remote sensing and digital image processing techniques (Principal Component Analysis and supervised classification) to Landsat imagery for analyzing the spatiotemporal land cover changes occurred in the Rio Canoas State Park in Brazil and its surrounding area from 1990 to 2016. Reforested areas around the park with exotic species is a part of the region’s economy and a number of industries depend on it for raw materials. However, it is a matter of concern to avoid contamination with such invasive species, due to the proximity of the Park. From 1990 to 2004, more than 95% of the study area was unchanged and showed minimal distinction in land cover over the 14 years. This was mainly due to the continuous presence of agricultural monocultures around the Park without significant increases (only 3.1% of land cover change during this period). Regarding the interior of the Rio Canoas State Park, from 1990 to 2004, there was no increase in the area of exposed soil. The analysis of the surrounding areas of the park from 2004 to 2016 showed that 5663.78 ha (12.2% of the area) of the land cover has been changed, in most areas, due to reforestation by Pinus sp. Notable changes occurred within the park (established in 2004) between 2004 and 2016—there was a partial regeneration of natural species diversity, a small number of invasive species (Pinus sp.) and removal of agricultural activities within the park, which contributed a 6.6% (75.45 ha) change in its land cover. We verified that 92.51% (1048.40 ha) of the areas inside the park were unchanged. The results demonstrated that actions were conducted to preserve the natural vegetation cover within the park and to reduce the impacts of anthropogenic activities, including the invasion of exotic species from the surrounding reforested areas into the natural habitat of the park. Given this, our study can aid the environmental management of the Park and its surrounding areas, enabling the monitoring of environmental legislation, the creation of a management plan, and can guide new action plans for the present study area and can be applied to other similar regions.

Land cover change modelling in Hyrcanian forests, Northern Iran: a landscape pattern and transformation analysis perspective

The main objective of this study is to analyze the spatio-temporal changes in land cover and land use, (1984–2010), as well to simulate future land cover for 2030 in the Neka River Basin, including the Hyrcanian forest, in northern Iran. For this purpose, we used detailed land cover maps for the years 1984, 2001 and 2010. The results showed that the highest deforestation occurred in the boundaries between forest and agriculture areas between 1984 and 2010. Comparing the observed and predicted land cover in 2010 yielded agreement of 96.41%. From 1984 to 2010, landscape metrics showed that the forest area evolved to more fragmentation, with less shape complexity and less connectivity. Projections for the future are consistent with observed changes for the Neka landscape, with a tendency to continue disaggregating and increasing diversity in a number of different patch types. Between 2010 and 2030, we observed the arrival of new crops, rangelands, and urban areas within the remaining areas of homogeneous forest. Changes in the Hyrcanian forest will cause alteration in ecosystem services, such as erosion control, water yield, timber harvest, and ground water reservation. Results of this work may represent a useful tool to provide strategies and territorial planning for sustainable management of the fragile Hyrcanian forest ecosystems in the Neka Basin.

Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method

Land use has changed dramatically in the Inner Mongolia Autonomous Region because of rapid economic growth and human disturbances. However, little information is available about the medium- and long-term land use changes in this region. The effects of ecological recovery policies have also been evaluated rarely. In this study, we employed the self-organizing map neural network method to identify the land cover changes in Inner Mongolia between 2000 and 2014. MOD13Q1, Landsat, and DMSP/OLS night-time light data were used as the data resources. The dynamic change map was characterized using the grid cell method. The results showed that urban area of Inner Mongolia increased by more than five times during the 15-year study period, while the mining area also increased. In addition, 35.3% of the farmland was changed into grassland, which may have been caused by the “Grain to Green” policy. The most significant environmental issue in Inner Mongolia is the loss of wetland. >40% of the wetland was converted into other land use types between 2000 and 2014. Grassland increased by 6.05%, but areas of open water and woodland remained about the same. In terms of the geographical distribution, cropland increased in the eastern and middle parts of the region. The transformation from wetland to grassland mainly occurred in the north. Grassland degradation occurred in the west. Thus, environmental policy has resulted in some ecological improvements in Inner Mongolia. However, new environmental problems associated with rapid economic development should be addressed in a timely manner.

Landsat-Derived Estimates of Mangrove Extents in the Sierra Leone Coastal Landscape Complex during 1990-2016.

This study provides the first assessment of decadal changes in mangrove extents in Sierra Leone. While significant advances have been made in mangrove mapping using remote sensing, no study has documented long-term changes in mangrove extents in Sierra Leone—one of the most vulnerable countries in West Africa. Such understanding is critical for devising regional management strategies that can support local livelihoods. We utilize multi-date Landsat data and cloud computational techniques to quantify spatiotemporal changes in land cover, with focus on mangrove ecosystems, for 1990–2016 along the coast of Sierra Leone. We specifically focus on four estuaries—Scarcies, Sierra Leone, Yawri Bay, and Sherbro. We relied on the k-means approach for an unsupervised classification, and validated the classified map from 2016 using ground truth data collected from Sentinel-2 and high-resolution images and during field research (accuracy: 95%). Our findings indicate that the Scarcies river estuary witnessed the greatest mangrove loss since 1990 (45%), while the Sierra Leone river estuary experienced mangrove gain over the last 26 years (22%). Overall, the Sierra Leone coast lost 25% of its mangroves between 1990 and 2016, with the lowest coverage in 2000, during the period of civil war (1991–2002). However, natural mangrove dynamics, as supported by field observations, indicate the potential for regeneration and sustainability under carefully constructed management strategies.