From Land Cover to Land Use: A Methodology to Assess Land Use from Remote Sensing Data

Land cover and drought risk assessment in Türkiye's mountain regions using neutrosophic decision support system

Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first time by combining remote sensing with a hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), analytic hierarchy process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000-2022) of land use and land cover (LULC) changes in the mountain regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye's mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.

Open Foris Collect Earth: a remote sensing sampling survey of Azerbaijan to support climate change reporting in the land use, land use change, and forestry

Land use, land use change, and forestry (LULUCF) are critical in climate change mitigation. Producing or collecting activity data for LULUCF is essential in developing national greenhouse gas inventories, national communications, biennial update reports, and nationally determined contributions to meet international commitments under climate change. Collect Earth is a free, publicly accessible software for monitoring dynamics between all land use classes: forestlands, croplands, grasslands, wetlands, settlements, and other lands. Collect Earth supports countries in monitoring the trends in land use and land cover over time by applying a sample-based approach and generating reliable, high-quality, consistent, accurate, transparent, robust, comparable, and complete activity data through augmented visual interpretation for climate change reporting. This article reports forest extent estimates in Azerbaijan, analyzing 7782 0.5-ha sampling units through an augmented visual interpretation of very high spatial and temporal resolution images on the Google Earth platform. The results revealed that in 2016, tree cover existed in 31.9% of total land, equal to 2,751,167 ha and 1,301,188 ha or 15.1% of the total land, with a 5.4% sampling error covered by forests. The estimate is 15 to 25% higher than the previous estimates, equal to 169,418 to 260,888 ha of forest that was never reported in previous studies.

Using Open Foris Collect Earth in Kyrgyzstan to support greenhouse gas inventory in the land use, land use change, and forestry sector

The Kyrgyz Republic (Kyrgyzstan) is one of the countries most vulnerable to the adverse effects of climate change in Central Asia. The land use, land use change, and forestry (LULUCF) sector is critical in climate change mitigation in Kyrgyzstan and is integral to national greenhouse gas (GHG) inventories. However, consistent, complete, and updated activity data is required for the LULUCF sector to develop a transparent GHG inventory. Collect Earth (CE), developed by the Food and Agriculture Organization of the United Nations (FAO), is a free, user-friendly, and open-source tool for collecting activity data for the LULUCF sector. CE assists countries in developing GHG inventories by providing consistent and complete land representation. This article reports an estimate of land use and land-use change dynamics in Kyrgyzstan, based on analyzing 13,414 1-hectare (ha) sampling units through an augmented visual interpretation approach using satellite imagery at the very high spatial and temporal resolution available through the Google Earth platform. The results show that in 2019, forests covered 1.36 million ha or 6.83% of the total land with a 6.23% uncertainty. This estimate was 5 to 16% higher than previous estimates, detecting an additional 63,024 to 188,164 ha of forestland that had not been reported previously. The new estimates suggest an average increase of 10.4% in the current forestlands of Kyrgyzstan.

Analysis of land cover dynamics in Mozambique (2001–2016)

Land cover change (LCC) is a complex and dynamic process influenced by social, economic, and biophysical factors that can cause significant impacts on ecological processes and biodiversity conservation. The assessment of LCC is particularly relevant in a country like Mozambique where livelihood strongly depends on natural resources. In this study, LCC was assessed using a point-based sampling approach through Open Foris Collect Earth (CE), a free and open-source software for land assessment developed by the Food and Agriculture Organization of the United Nations. This study aimed to conduct an LCC assessment using CE for the entire Mozambique, and according to three different land classifications: administrative boundaries (provinces), ecoregions, and protected vs unprotected areas. A set of 23,938 randomly selected plots, with an area of 0.5 hectares, placed on a 4 × 4 km regular grid over the entire country, was assessed using CE. The analysis showed that Mozambique has gone through significant loss of forest (− 1.3 Mha) mainly to the conversion to cropland. Deforestation is not occurring evenly throughout the country with some provinces, such as Nampula and Zambezia, characterized by higher rates than others, such as Gaza and Niassa. This result can be explained considering a combination of ecological and socio-economic factors, as well as the conservative role played by the protected areas. Our study confirmed that LCC is a complex phenomenon, and the augmented visual interpretation methodology can effectively complement and integrate the LCC analyses conducted using the traditional wall-to-wall mapping to support national land assessment and forest inventories and provide training data for environmental modeling.

Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

The simulation and analysis of future land use and land cover—LULC scenarios using artificial neural networks (ANN)—has been applied in the last 25 years, producing information for environmental and territorial policy making and implementation. LULC changes have impacts on many levels, e.g., climate change, biodiversity and ecosystem services, soil quality, which, in turn, have implications for the landscape. Therefore, it is fundamental that planning is informed by scientific evidence. The objective of this work was to develop a geographic model to identify the main patterns of LULC transitions between the years 2000 and 2018, to simulate a baseline scenario for the year 2036, and to assess the effectiveness of the Chapecó River ecological corridor (an area created by State Decree No. 2.957/2010), regarding the recovery and conservation of forest remnants and natural fields. The results indicate that the forest remnants have tended to recover their area, systematically replacing silviculture areas. However, natural fields (grassland) are expected to disappear in the near future if proper measures are not taken to protect this ecosystem. If the current agricultural advance pattern is maintained, only 0.5% of natural fields will remain in the ecological corridor by 2036. This LULC trend exposes the low effectiveness of the ecological corridor (EC) in protecting and restoring this vital ecosystem.

Land Use, Land Cover Change and Sustainable Intensification of Agriculture and Livestock in the Amazon and the Atlantic Forest in Brazil

The Amazon and the Atlantic Forest are Brazilian biomes that suffered an intense land use and land cover change, marked by the loss of native forest and expansion of agriculture and livestock. This article aims to analyze land use and land cover change history and to propose a sustainable alternative for agriculture and livestock as an opportunity for rural development in these biomes. The statistics of the platform from the Annual Mapping Project for Land Use and Land Cover in Brazil (MapBiomas) were used in an annual historical series from 1985 to 2020. The analysis of land use and land cover changes indicates that the Amazon native forest was reduced by 44.53 million hectares (Mha), while pasture, agriculture and planted forest increased by 38.10, 6.06 and 0.26 Mha, respectively, over the 35 years (1985 to 2020). In the Atlantic Forest, for the same period, forest and pasture reduced by 0.99 and 11.53 Mha, respectively, while agriculture expanded by 8.06 Mha and planted forest by 2.99 Mha. Sustainable land use strategies, such as the Integration Crop-Livestock-Forest (ICLF), can support the increase in agricultural production while recovering and preserving the environment. Policies and programs should consider regional particularities and barriers for more significant adoption of this strategy.

Application of Remote Sensing Tools to Assess the Land Use and Land Cover Change in Coatzacoalcos, Veracruz, Mexico

Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative technology and tools that support their various decision making systems. This study provides the most recent LULC change analysis for the last six years (2015–2021) of Coatzacoalcos, Veracruz, Mexico, one of the most important petrochemical cities in the world and host of the ongoing Interoceanic Corridor project. The analysis was carried out using Landsat 8 Operational Land Imager (OLI) satellite images, ancillary data and ground-based surveys and the Normalized Difference Vegetation Index (NDVI) to identify and to ameliorate the discrimination between four main macro-classes and fourteen classes. The LULC classification was performed using the maximum likelihood classifier (MLC) to produce maps for each year, as it was found to be the best approach when compared to minimum distance (MDM) and spectral angle mapping (SAM) methods. The macro-classes were water, built-up, vegetation and bare soil, whereas the classes were an improved classification within those. Our study achieved both user accuracy (UA) and producer accuracy (PA) above 90% for the proposed macro-classes and classes. The average Kappa coefficient for macro-classes was 0.93, while for classes it was 0.96, both comparable to previous studies. The results from the LULC analysis show that residential, industry and commercial areas slowed down their growth throughout the study period. These changes were associated with socio-economical drivers such as insecurity and lack of economic investments. Groves and trees presented steady behaviors, with small increments during the five-year period. Swamps, on the other hand, significantly degraded, being about 2% of the study area in 2015 and 0.93% in 2021. Dunes and medium and high vegetation densities (∼80%) transitioned mostly to low vegetation densities. This behavior is associated with rainfall below the annual reference and increments of surface runoff due to the loss of vegetation cover. Lastly, the present study seeks to highlight the importance of remote sensing for a better understanding of the dynamics between human–nature interactions and to provide information to assist planners and decision-makers for more sustainable land development.

Medium- (MR) and Very-High-Resolution (VHR) Image Integration through Collect Earth for Monitoring Forests and Land-Use Changes: Global Forest Survey (GFS) in the Temperate FAO Ecozone in Europe (2000–2015)

Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS) integrating medium- (MR) and very-high-resolution (VHR) images through the FAO’s Collect Earth platform. More than 11,150 plots were inventoried in the Temperate FAO ecozone in Europe to monitor LULUCF from 2000 to 2015. As a result, 2.19% (VHR) to 2.77% (MR/VHR) of the study area underwent LULUCF, including a 0.37% (VHR) to 0.43% (MR/VHR) net increase in forest lands. Collect Earth and VHR images have also (i) allowed for shaping a preliminary structure of the land-use network, showing that cropland was the land type that changed most and that cropland and grassland were the more frequent land uses that generated new forest land, (ii) shown that, in 2015, mixed and monospecific forests represented 44.3% and 46.5% of the forest land, respectively, unlike other forest sources, and (iii) shown that 14.9% of the area had been affected by disturbances, particularly wood harvesting (67.47% of the disturbed forests). According to other authors, the area showed a strong correlation between canopy mortality and reported wood removals due to the transition from past clear-cut systems to “close-to-nature” silviculture.

Incorporating Deep Features into GEOBIA Paradigm for Remote Sensing Imagery Classification: A Patch-Based Approach

The fast and accurate creation of land use/land cover maps from very-high-resolution (VHR) remote sensing imagery is crucial for urban planning and environmental monitoring. Geographic object-based image analysis methods (GEOBIA) provide an effective solution using image objects instead of individual pixels in VHR remote sensing imagery analysis. Simultaneously, convolutional neural networks (CNN) have been widely used in the image processing field because of their powerful feature extraction capabilities. This study presents a patch-based strategy for integrating deep features into GEOBIA for VHR remote sensing imagery classification. To extract deep features from irregular image objects through CNN, a patch-based approach is proposed for representing image objects and learning patch-based deep features, and a deep features aggregation method is proposed for aggregating patch-based deep features into object-based deep features. Finally, both object and deep features are integrated into a GEOBIA paradigm for classifying image objects. We explored the influences of segmentation scales and patch sizes in our method and explored the effectiveness of deep and object features in classification. Moreover, we performed 5-fold stratified cross validations 50 times to explore the uncertainty of our method. Additionally, we explored the importance of deep feature aggregation, and we evaluated our method by comparing it with three state-of-the-art methods in a Beijing dataset and Zurich dataset. The results indicate that smaller segmentation scales were more conducive to VHR remote sensing imagery classification, and it was not appropriate to select too large or too small patches as the patch size should be determined by imagery and its resolution. Moreover, we found that deep features are more effective than object features, while object features still matter for image classification, and deep feature aggregation is a critical step in our method. Finally, our method can achieve the highest overall accuracies compared with the state-of-the-art methods, and the overall accuracies are 91.21% for the Beijing dataset and 99.05% for the Zurich dataset.

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner.

Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.

Land Use and Land Cover Changes, and Environment and Risk Evaluation of Dujiangyan City (SW China) Using Remote Sensing and GIS Techniques

Understanding of the Land Use and Land Cover (LULC) change, its transitions and Landscape risk (LR) evaluation in earthquake-affected areas is important for planning and urban sustainability. In the present study, we have considered Dujiangyan City and its Environs (DCEN), a seismic-prone area close to the 2008 Wenchuan earthquake (8.0 Mw) during 2007–2018. Five different multi-temporal data sets for the years 2007, 2008, 2010, 2015, and 2018 were considered for LULC mapping, followed by the maximum likelihood supervised classification technique. The individual LULC maps were further used in four time periods, i.e., 2007–2018, 2008–2018, 2010–2018, and 2015–2018, to evaluate the Land Use and Land Cover Transitions (LULCT) using combined remote sensing and GIS (Geographical Information System). Furthermore, multi-criteria evaluation (MCE) techniques were applied for LR mapping. The results of the LULC change data indicate that built-up, agricultural area, and forest cover are the prime categories that had been changed by the natural and anthropogenic activities. LULCT, along with multi-parameters, are suggested to avoid development in fault-existing areas that are seismically vulnerable for future landscape planning in a sustainable manner.

Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia

Forests, particularly in the tropics, are suffering from deforestation and forest degradations. The estimation of forest area and canopy cover is an essential part of the establishment of a measurement, reporting, and verification (MRV) system that is needed for monitoring carbon stocks and the associated greenhouse gas emissions and removals. Information about forest area and canopy cover might be obtained by visual image interpretation as an alternative to expensive fieldwork. The objectives of this study were to evaluate different types of satellite images for forest area and canopy cover estimation though visual image interpretation, and assess the influence of sample sizes on the estimates. Seven sites in Ethiopia with different vegetation systems were subjectively identified, and visual interpretations were carried out in a systematical design. Bootstrapping was applied to evaluate the effects of sample sizes. The results showed that high-resolution satellite images (≤5 m) (PlanetScope and RapidEye) images produced very similar estimates, while coarser resolution imagery (10 m, Sentinel-2) estimates were dependent on forest conditions. Estimates based on Sentinel-2 images varied significantly from the two other types of images in sites with denser forest cover. The estimates from PlanetScope and RapidEye were less sensitive to changes in sample size.

The extent of forest in dryland biomes

Dryland biomes cover two-fifths of Earth's land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than 210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high temporal resolution, which are available through the Google Earth platform. We show that in 2015, 1327 million hectares of drylands had more than 10% tree-cover, and 1079 million hectares comprised forest. Our estimate is 40 to 47% higher than previous estimates, corresponding to 467 million hectares of forest that have never been reported before. This increases current estimates of global forest cover by at least 9%.

Examining the Driving Factors Causing Rapid Urban Expansion in China: An Analysis Based on GlobeLand30 Data

A large number of studies have dealt with the driven forces of land expansion, in which the remote sensing data and statistical data are most commonly used. The recent progress based on the statistical data have not been fully tested and discussed by the remote sensing data, and the remote sensing data used in the previous studies are usually interpreted within certain areas which is not convenient for global comparison. In this paper, the 30-m GlobalLand Cover Dataset (GlobeLand30) and socioeconomic data from 2000 to 2010 are adopted to investigate the factors driving impervious surface expansion in China based on a multilevel regression model. The GlobeLand30 provides a world-wide data framework which has a sound basis for regional comparison research. The variables are selected according to the existing research. Most, but not all, results are consistent with the previous studies when using impervious surface data of GlobeLand30. The main findings are: (1) the market demand caused by economic development, such as the increase in GDP from 2000 to 2010, plays a positive role in the expansion of developed land; (2) the land supply, as reflected by the ratio of the total of land transfer fees to fiscal revenue, also has a positive effect on the increase in impervious surfaces; (3) the percentage of the increase by private workers to the increase in total workers and certain other frequently-used variables are not relevant after controlling for land demand- and supply-related variables; and (4) the growth in impervious surfaces is related to the amount of the cultivated land, which implies the necessity for a more stringent farmland protection policy. Considering the need to compare across regions, we suggest that GlobeLand30 should be used for more studies to better understand the driving forces of land expansion.