Rate Of Land Cover Change Research Articles

Assessing the impact of three biosphere reserves on the conservation of coastal ecosystems

Biosphere Reserves (BR) manage large territories with diverse natural covers and land uses to preserve biodiversity, promote local development and preserve ecosystems. This study evaluated how their zoning (buffer and core) and policy timeframes (decree period, management plan period, and land planning period) influence four landscape management outcomes: deforestation, natural cover recovery, and anthropic and natural permanence. For three Mexican BR case studies, land use and cover transitions were calculated and compared to contrafactual sites. Observed rates of land cover change were marginal within all three BR zoning and across their policy timeframe (<0.02 % change rate), suggesting that BR effectively promote the permanence of both natural and anthropic covers. Nevertheless, the predicted probability of uncommon deforestation and recovery outcomes at local levels showed that the effect of a BR over its regulated landscape is not spatiotemporally static, contrasting the effect of individual allocation vs a group or network. Poverty, land tenure, agriculture aptitude and distance to markets adds to this dynamic and is modelled and discussed. This study shows that BR zoning schemes and its regulatory sequence influence the rates of land cover change and the predicted probability of landscape management outcomes across space and time.

Rapid land cover change in agricultural valleys of the Atacama Desert, Chile

The profound landscape modification produced by agriculture is considered one of the main threats to biodiversity worldwide. Although the main ecological impacts are likely due to agricultural expansion into natural areas, changes in crop types and production intensification also induce significant ecological changes. We studied the land cover change process in four agricultural valleys (Lluta, Azapa, Vítor and Camarones) in the Atacama Desert of Chile between 2003 and 2019, divided into four equal periods. Using spatial data and logistic regression, we assessed the driving factors of land cover change. The Azapa valley showed the highest rate of land cover change, reaching 36.4% of netting and greenhouses landscape cover by the end of the study, and 50.4% annual rate of increase during the 2007–2011 period. Intensification was observed as the anti-aphid netting cover expanded mainly in Azapa and in the last four years in Vítor and Lluta valleys. Most land cover change was related to low slopes and elevation or sites closer to roads or surrounded by already cultivated land, like agriculture expansion on barren soil or native shrubland loss. High population density was significantly correlated with land cover change (p < 0.01). If land cover change into netting continues as observed, it is possible that landscape homogenization occurs in the short term in almost all valleys. Anyway, disentangling how landscape change works and the involved drivers, could be useful to understand landscape simplification in arid lands that are under an agricultural intensification process.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China.

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Analisis Kerentanan Lanskap Pesisir di Kecamatan Kuta Selatan, Bali

Vulnerability Analysis of Land Changes in Coastal Landscapes in Kuta Selatan District, Bali. Kuta Selatan District is a district located in the southern coastal part of Bali Province. Its existence in a coastal area makes this area rich in natural resources but also has a high level of vulnerability to damage, change, or disappearance of an ecosystem. This study aim to analyze the vulnerability of the landscape based on land cover changes in Kuta Selatan District and analyze the driving factors of landscape change. Land cover is classified into 4 (water bodies, vegetation areas, open areas, and built areas. Land cover change analysis used Landsat 7 ETM+ image data with test years 2000, 2010, and 2020. The spatial analysis method was used to analyze the land cover changes that occurred. The results showed that the rate of land cover change that occurs in Kuta Selatan District from 2000 to 2020 is 3-4% per year with dominated by increasing of open areas and built-up areas respectively. Meanwhile, the area of vegetation and water bodies has always experienced a decrease in area. This study also analyzes the driving factors of land cover change using the binary logistic regression analysis method. 7 variables are thought to affect changes in land cover that occur, namely distance to the main road (X1), distance to the river (X2), altitude/elevation (X3), slope (X4), population density (X5), distance from state universities (X6) and distance to tourist attractions (X7). The analysis results show that only the distance variable to the main road (X1) influences changes that occur in the 2000-2020 range. Supervision and policies that favor the sustainability of the existence of vegetation areas are the main focus of the recommendations.&#x0D;

Analysis of Land Cover Change Using Sentinel-2 Images at Natural Laboratory of Peat Swamp Forest Area CIMTROP University of Palangka Raya

The Natural Laboratory of Peat Swamp Forest Area known as the Center For International Co-operation in Management of Trofical Peatland (CIMTROP) University of Palangka Raya has been set as specific research area of tropical peat swamps since 1993 and has a 50.000 hectares total area. Natural Laboratory of Peat Swamp Forest Area – CIMTROP University of Palangka Raya is peat and sandy land and as a catchment water area, that is crucial to preserve and protect its ecosystem. This research used remote sensing and GIS technology for land cover monitoring utilize Sentinel-2 images. The purpose of this research was to determine (1) land cover changes in the Natural Laboratory/CIMTROP UPR in 2016 and 2021, (2) The rate of land cover change in the Natural Laboratory/CIMTROP UPR and (3) the causes of land cover change in the Natural Laboratory/CIMTROP UPR. Land cover analysis by interpreting Sentinel-2 images in 2016 and 2021 used Supervised Classification Technique. Based on results of the analysis, there were five land cover classes: forest, shurbs, brush, swamp grass and open land. The accuracy testing process performed uses a confusion matrix method to produce mapping accuracy values for each land cover class. Between 2016 to 2021, the change in forest class decreased from 31.809,40 hectares (67,37%) to 29.888,10 hectares (63,30%). Contrary to the shrub class, there was and increase of 1.762,30 hectares (3,73%) to 2.585,20 hectares (5,47%). The increase in shrub area was due to forest reforestation, degradation and deforestation from the forest and land fires which occurred in previors years.

An analysis of fire dynamics in and around indigenous territories and protected areas in a Brazilian agricultural frontier

Sustainable land management is integral to conserving tropical ecosystems and reducing carbon emissions. Human-set fires are part of the land cover change process and are the most common driver of deforestation and carbon emissions in Amazonia. Analyzing the behavior of major fire events provides insight into the effectiveness of current protections. This study aims to quantify the role of Indigenous Territories and Protected Areas (ITPAs) in characterizing anthropogenic fire regimes over the 2020 fire season in Mato Grosso, Brazil. Because of the rapid rate of land cover change and lack of widespread ground validation data in the region, we used a combination of ESA’s Sentinel-5 Precursor Aerosol Index, NASA’s Visible Infrared Imaging Radiometer Suite, and high resolution Planet satellite imagery for the Monitoring of the Andean Amazon Project to analyze spatial and temporal patterns by fire type. ITPAs cover 25% of the study area’s land and contained approximately 20% of significant recorded fires in 2020. Recently deforested areas, forest, grassland, and cropland fires showed varying seasonality and lower frequencies inside ITPAs, but mean fire start dates for all fire types occurred in mid-September. Results suggest that the overall density of major fires is reduced in ITs. PAs only inhibit the density of crop or pastureland fires, but no major fires occurred past 10 km inside their borders. Burn severity of major fires had a weak relationship to distance from ITPAs for some fire types. This study highlights the advantages of near real-time data for individual fire events, provides further evidence of the effect of ITPAs on fire behavior, and demonstrates the importance of adequate protection strategies for mitigating fire activity.

Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Land Cover Change Analysis to Assess Sustainability of Development in the Mongolian Plateau over 30 Years

The changes in land cover patterns in the Mongolian Plateau can reveal the regional status of sustainable development. Based on land cover data from 1990–2020, the study reveals the process of land cover change on the Mongolian plateau and integrates those changes with UN Sustainable Development Goals (SDGs) to further evaluate regional sustainable development status. Result revealed there is a stable rate of land cover change (0.16%) for the Mongolian Plateau, but with diverse shifting trends for various land cover types and SDGs indicators in past 30 years. Croplands (SDG2) showed a growth trend in the last five years, which was different from its initial obviously decreasing trend. The status of water (SDG6) showed a clear decreasing trend, which presents a major threat to this arid-to-semi-arid region. The built area (SDG11) increased continuously, but the long upward trend has slowed in recent years. The forest area (SDG15) declined, but it has recently recovered. Grasslands showed diverse changes in various steppe types (including real, meadow, and desert steppe types) while still experiencing land degradation. The expansion of sand areas presents a hidden risk of increasing sandstorms. Comparative analysis revealed that there have clear differences between Mongolia and Inner Mongolia due to the various government policies. In general, the land use degree in Mongolian Plateau increased annually. This indicated that the climate change and human activities have more and more influences, and it is still facing severe challenges for specific SDGs indicators in the region.

Land Use Hotspots of the Two Largest Landlocked Countries: Kazakhstan and Mongolia

As the two largest landlocked countries, Kazakhstan and Mongolia have similar biophysical conditions and socioeconomic roots in the former Soviet Union. Our objective is to investigate the direction, extent, and spatial variation of land cover change at three administrative levels over three decades (1990–2020). We selected three provinces from each country (Aktobe, Akmola, and Almaty province in Kazakhstan, and Arkhangai, Tov, and Dornod in Mongolia) to classify the land cover into forest, grassland, cropland, barren, and water. Altogether, 6964 Landsat images were used in pixel-based classification method with random forest model for image processing. Six thousand training data points (300 training points × 5 classes × 4 periods) for each province were collected for classification and change detection. Land cover changes at decadal and over the entire study period for five land cover classes were quantified at the country, provincial, and county level. High classification accuracy indicates localized land cover classification have an edge over the latest global land cover product and reveal fine differences in landscape composition. The vast steppe landscapes in these two countries are dominated by grasslands of 91.5% for Dornod in Mongolia and 74.7% for Aktobe in Kazakhstan during the 30-year study period. The most common land cover conversion was grassland to cropland. The cyclic land cover conversions between grassland and cropland reflect the impacts of the Soviet Union’s largest reclamation campaign of the 20th century in Kazakhstan and the Atar-3 agriculture re-development in Mongolia. Kazakhstan experienced a higher rate of land cover change over a larger extent of land area than Mongolia. The spatial distribution of land use intensity indicates that land use hotspots are largely influenced by policy and its shifts. Future research based on these large-scale land use and land cover changes should be focused the corresponding ecosystem and society functions.

Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications

Land abandonment in European mountains threatens habitats shaped for centuries by low-intensity agriculture and grazing. Hence, it is important to identify spatiotemporal patterns in rural abandonment, and relate them to biophysical and socioeconomic drivers. We pursued these goals in the theoretical context of transitions from traditional to productivist and then to post-productivist agriculture. We conducted a case study in a representative of southern Europe sub-mountainous marginal area that was once traditionally exploited (Pindus range, Epirus, Greece). Land cover was mapped from the outset of abandonment (years 1945, 1970, 1996 and 2015), and we subsequently calculated landscape metrics. An Intensity Analysis facilitated the comparison of rates of land cover change between time periods. By employing random forest modelling, we related socioeconomic, physiographic, geological and climatic predictors to land type occurrence and succession intensity. We found that farmland decreased from 30% to 3% during the 70 years of the study period, and that forest increased from 22% to 63%. The landscape’s heterogeneity, ecotone diversity, and spatial aggregation decreased. Abandonment and succession accelerated and relocated to lower elevation, especially during the latest time period, which was related to a second depopulation wave and livestock decrease. The remaining lowland farmlands were of productivist agriculture, and no widespread post-productivist regime was found. Thus, our study supports the view that policies, which have been mainly based on the linear transition of agricultural regimes in northern Europe, must take into account southern European mountains, where widespread abandonment can coexist with limited intensification and extensification.

Understanding traditional agro-ecosystem dynamics in response to systematic transition processes and rainfall variability patterns at watershed-scale in Southern Ethiopia

Multi-temporal analysis of land cover dynamics using remote sensing can enable the determination of the spatial extent and average rate of land cover change. With the application of an appropriate change analysis method, it is also possible to distinguish whether a land cover change has occurred by the effect of a random or a systematic process. In connection with this, characterizing rainfall variability and historical meteorological drought events can allow understanding of their effects on agro-ecosystems and vegetation cover dynamics. Therefore, this study has evaluated multi-temporal land cover change in response to the possible impacts of population pressure and rainfall variability on agro-ecosystem dynamics. This was conducted on a traditional agroforestry-dominated landscape in Southern Ethiopia. Using Landsat images acquired in 1985, 2000, and 2018, a post-classification land cover change analysis approach was employed to distinguish between a systematic and random process of inter-category transitions. Assessment of drought events and rainfall variability dynamics were performed using standardized precipitation index (SPI) and rainfall coefficient of variation (CV), respectively. Mann–Kendall test was also applied for the detection of a monotonic rainfall trend. A bias-corrected Climate Hazards group Infrared Precipitation with Stations (CHIRPS) over 1981–2017 was used to calculate the SPI, CV and Mann–Kendall trend test. The analysis showed that above 41% of the landscape has experienced land cover transitions between 1985 and 2018. This has primarily resulted by a systematic and rapid expansion of agriculture, urban areas, and eucalyptus plantations, at the expense of natural vegetation ecosystems. Consequently, over the last 33 years (1985–2018), natural forest, grassland, and wetland have declined by 74.8%, 83.3%, and 78.4%, respectively. Another major land cover change identified in this study was the replacement of open-field crops by agroforestry, mainly in the western part of the catchment. Such expansion of agroforestry has appeared to be spatially correlated with a lower amount of long-term average and more variable rainfall. Perhaps, this could indicate farmers’ response to rainfall variability by diversifying agricultural production options (i.e. agroforestry system), by replacing the more risk-prone mono-cropping culture. The observed persistence and further expansion of traditional agroforestry (a combination of perennial crops and scattered trees) have implications in terms of enhancing biodiversity conservation and environmental protection. Overall, the land cover transitions that occurred over the last three decades suggest future conservation priorities for improved landscape management, with more emphasis on the most exposed natural vegetation ecosystems.

Land Use Practices along Saadani-Wami-Mbiki Wildlife Corridor and their Implications to Wildlife Conservation

Saadani-Wami-Mbiki wildlife corridor has been facing conservation threats as a result of various land-use practices (LUP) carried out in and around the corridor. The understanding of changes happening in the corridor over time is important for establishing the management baseline data. This study aimed at identifying land use practices along the Saadani-Wami-Mbiki wildlife corridor and their implications to wildlife conservation. Specifically, the study sought to determine the rate of land cover changes in the corridor between 1975 and 2011 and the effects associated with land use practices on wildlife conservation. The land sat imageries of 1975, 1995, and 2011 were used to assess the rate of vegetation cover changes as a result of various land use practices carried out along. The household survey and Key informants' interview methods were used to obtain socio-economic data which were analyzed using SPSS while GIS data were analyzed using the ERDAS IMAGINE 9.1 and ArcGIS 9.3 programs. In the past 36 years (1975-2011), the cultivated land increased by 25%, settlement by 13%, open forest by 10% while closed forest and grassland decreased by 18% and 3% respectively. Shifting cultivation, overgrazing, charcoal burning, settlements, and poaching were identified as major land use practices threatening wildlife conservation within the corridor. Based on the results, it was recommended that, the Government should formulate a land use management plan and introduce a community-based natural resources management strategy to improve natural resources utilization and reduction of human stress to the corridor.

Livelihood Impacts of Forest Carbon Protection in the Context of Redd+ in Cross River State, Southeast Nigeria

The rate of landcover change linked to deforestation and forest degradation in tropical environments has continued to surge despite a series of forest governance policy instruments over the years. These informed the launch of one of the most important international policies called Reducing Emission from Deforestation and Forest Degradation Plus (REDD+) to combat forest destruction. REDD+ assumes that communities will have increased assets to natural capital which will enhance their livelihood portfolio and mitigate the effects of climate variability and change across biomes. The aim of this study is to ascertain the livelihoods impacts of forest carbon protection within the context of REDD+ in Cross River State, Nigeria. Six forest communities were chosen across three agroecological zones of the State. Anchored on the Sustainable Livelihood Framework, a set of questionnaires were administered to randomly picked households. The results indicate that more than half of the respondents aligned with financial payment and more natural resources as the perceived benefits of carbon protection. More so, a multinomial logistic regression showed that income was the main factor that influenced respondent’s support for forest carbon protection. Analysis of income trends from the ‘big seven’ non-timber forest resources in the region showed increase in Gnetum africanum, Bushmeat, Irvingia gabonensis, Garcinia kola, while carpolobia spp., Randia and rattan cane revealed declining income since inception of REDD+. The recorded increase in household income was attributed to a ban in logging. It is recommended that the forest communities should be more heavily involved in the subsequent phases of the project implementation to avoid carbon leakages.

ANALISIS PERUBAHAN TUTUPAN LAHAN DAS ASAM–ASAM KECAMATAN JORONG KABUPATEN TANAH LAUT PERIODE 2000 SAMPAI 2017

This study aims to analyze the shape and rate of land cover change for the period 2000 to 2017, the method used in this study is image interpretation and data analysis. There are 15 classifications namely water body, shrub, swamp shrub, secondary mangrove forest, primary dryland forest, secondary dryland forest, plantations, mixed dryland agriculture, plantations, settlements, dry land agriculture, swamps, open land, open land, mining and pond. There is an increase in the type of land cover in 2017. The most dominant land cover in 2000 in the Asam-Asam watershed was 39399 ha of forest with a percentage of 78.66% while based on land cover data of Landsat 7 in 2017 the forest area was reduced to 16755, 6 Ha with a percentage of 33.45%. The function of forest area in the APL area is the biggest change, namely secondary dryland forest to dryland agriculture with an area of 2118.8 Ha, for the function of forest area in the HL region the largest is secondary dryland forest into shrubs with an area of 507.4 Ha, at HP area has the biggest change, namely plantation forest to plantation with an area of 5036.2 Ha, the function of HPK area is the biggest change in secondary dryland forest into shrubs with an area of 238.2 Ha, the next area Riam Kanan is primary dryland forest into secondary dryland forest with an area of 3545.7 ha and in the area of the Pelaihari TWA forest function the largest is secondary swamp forest into swampland with an area of 217.9 ha.

Land Cover Trends in South Texas (1987–2050): Potential Implications for Wild Felids

The Rio Grande Delta and surrounding rangelands in Texas has become one of the fastest urbanizing regions in the United States over the last 35 years. We assessed how land cover trends contributed to the large-scale processes that have driven land cover change since 1987. We classified LANDSAT imagery from 1987 to 2016 to quantify different rates of land cover change and used housing density scenarios to project changes in the amount and spatial distribution of woody cover until 2050 and its potential impact on wild felid habitat. Since 1987, woody cover increased from 3.9% along with patch and edge density, whereas mean patch area and Euclidean nearest neighbor decreased. Closer inspection revealed that woody encroachment of small patches (&lt;1 ha) was the leading cause of woody cover increase by a magnitude of 4, with an observed significant skewness and kurtosis in the frequency distribution of patch size across years. By 2050, urbanization will be the dominant landscape type and at least 200 km2 of woody cover may be lost, thereby affecting felid populations in South Texas. These results provide important information for predicting future woody cover fragmentation and its potential impact on the connectivity of wild felid populations.

How feasible are global forest restoration commitments?

AbstractNumerous countries have made voluntary commitments to conduct forest landscape restoration over millions of hectares of degraded land in the coming decade. We consider the relative likelihood these countries will achieve their restoration commitments. Across countries, the area committed to restoration increased with existing forest and plantation area, but was inversely related to development status, with less developed countries pledging more area. Restoration commitments are generally large (median: 2 million hectares) and will be challenging to meet without the wholesale transformation of food production systems. Indeed, one third of countries committed &gt;10% of their land area to restoration (maximum: 81%). Furthermore, high rates of land cover change may reverse gains: a quarter of countries experienced recent deforestation and agricultural expansion that exceeded their restoration commitment area. The limited progress reported by countries, and the sheer scale of commitments, raises serious questions about long‐term success, especially absent necessary monitoring and management plans.

The Rate of Land Cover Change using Landsat Data in Coal Mining Area of Sawah Lunto City, Indonesia

Sawahlunto city is well-known for coal mining region. As in 2010, there have been at least 12 mining companies exploring coal resources in the region. As time passes, land cover conditions have gradually decreased due to mining activities. This region on which was originally covered by various vegetation and ecosystem have systematically transformed into open areas for coal mining. The use of remote sensing technology for land cover monitoring has been commonly well-developed in accordance with the need for improvement of detailed information about the changes of land use coverage. This study examines the land cover changes using supervised classification method based on Landsat data. The method focuses on four dominant classes of land cover in the region, namely forest, mining, settlement, and water resources. The classification processes were performed based on true-color composite satellite images. The results show that the overall accuracies of classification are 91.68 %, 92.49 %, 93.69 %, and 93.74 % in 2000, 2006, 2011, and 2016 respectively. It was also found that forest is the largest area in the coal mining area which, in some sense, tends to continuously decrease in terms of land cover in the last 15 years. The rate of forest area degradation achieved its maximum between 2006 and 2011.

Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks

Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as long short-term memory (LSTM) have yielded promising results in remote sensing and GIScience studies. However, the characteristics of geospatial datasets selected for use with these methods have demonstrated important implications on method performance. The number of data layers available, the rate of LCC, and inherent errors resulting from classification procedures are expected to influence model performance. Yet, it is unknown how these can affect compatibility with the LSTM method. As such, the main objective of this study is to explore the capacity of LSTM to forecast patterns that have emerged from LCC dynamics given varying temporal resolutions, persistent land cover classes, and auxiliary data layers pertaining to classification confidence. Stacked LSTM modeling approaches are applied to 17-year MODIS land cover datasets focused on the province of British Columbia, Canada. This geospatial data is reclassified to four major land cover (LC) classes during pre-processing procedures. The evaluation considers the dataset at variable temporal resolutions to demonstrate the significance of geospatial data characteristics on LSTM method performance in several scenarios. Results indicate that LSTM can be utilized for forecasting LCC patterns when there are few limitations on temporal intervals of the datasets provided. Likewise, this study demonstrates improved performance measures when there are classes that do not change. Furthermore, providing classification confidence data as ancillary input also demonstrated improved results when the number of timesteps or temporal resolution is limited. This study contributes to future applications of DL and LSTM methods for forecasting LCC.

Change versus stability: are protected areas particularly pressured by global land cover change?

ContextLand cover change is a global multi-scale process affecting ecosystems, with potential implications for ecological processes and for the biological communities that support them. Land cover changes are especially relevant for protected areas where long-term ecosystem stability is a critical aspect of protecting and maintaining high levels of biodiversity and ecosystem functions.ObjectivesTo understand the extent of land cover change impact on global ecosystem stability of protected areas across time and space.MethodsHere we analysed 23 years of global spatial and temporal distribution of land cover change its occurrence within protected areas. We investigated whether higher land cover change rates occurred inside or outside protected areas, identified the main type of changes, and their distribution by UN region.ResultsOur results show that from 1992 to 2015, 4.89% of the world’s land surface changed from one land cover to another, with 97.9% of this change persisting until the end of the time period. We found that regions with higher land cover change, tend to have a higher incidence of change close to protected areas, suggesting a spillover effect on these areas. Also, the proportion of change inside and outside protected areas varied considerably across UN Regions.ConclusionsOur results suggest that to reach current global conservation targets, it is not enough to increase the targets of protected area coverage. Instead, governments and conservation management agencies should account for regional specificities, and pay attention to the territories surrounding protected areas to develop strategies to reduce spillover effects.

A Modified Change Vector Approach for Quantifying Land Cover Change

This paper develops and applies a novel method for inferring land cover/land use (LCLU) change that combines direct multi-date classification with measures from a change vector analysis. The model predicts change directly rather than the land cover at either time, although these could be inferred. Unsupervised classifications of bi-temporal imagery were manually labeled and used to generate reference data for class-to-class changes. These were used to train a Random Forest model with inputs from the bi-temporal image bands and change vector measures (change vector direction, angle and the spectral angle) and used to generate a predicted surface of land cover change for a case study in the Pearl River Delta, China. The overall accuracy of LCLU change prediction was 96% and specific class-to-class changes had errors rates of 0–12.8%. Some errors were related the semi-automated labeling of the training data. The spectral angle variables and Near Infra-Red image bands for both years were found to be strong predictors of change. Odd ratios were used to quantify regional differences in land cover change rates in urban and peri-urban areas. The regional differences and origins of the observed errors are discussed, along with some areas of further work. The key contributions of this paper are the focus on change rather than LCLU through the construction of a model to predict changes directly and the development of an approach that provides quick, effective and informative analysis of LCLU change in support of policy and planning in rapidly urbanizing areas.