Fine Resolution Land Cover Research Articles

Assessment of fine-resolution land cover mapping products in the Changbai Mountain Range, China

Assessment of fine-resolution land cover mapping products in the Changbai Mountain Range, China

Assessing Normalized Difference Vegetation Index as a proxy of urban greenspace exposure

The Normalized Difference Vegetation Index (NDVI) is a popular proxy of urban greenspace (UGS). However, it’s unclear how NDVI approximates physical characteristics of UGS in the context of urban health studies, causing ambiguities in translating research findings to UGS management. Therefore, we collected data from Landsat and MODIS satellites and Lidar 3D scans in New York City as of circa 2013, and we evaluated linear and non-linear relationships between NDVI and UGS characteristics. We found that: (1) % UGS was the best predicted UGS characteristic by NDVI (R2: 0.35–0.90, varies by data source and unit of analysis), whereas average tree height was the worst (R2: 0.09–0.46). The predictive power on % canopy cover, tree density, and crown volume density was in a similar range (R2: 0.10–0.67). Prediction improved with finer-resolution NDVI sources and larger units of analysis at the cost of losing useful variations; (2) There was a saturation effect where a linear relationship underestimated UGS characteristics in areas of high NDVI. These areas typically had NAIP-NDVI greater than the range of 0.08–0.25, Landsat-NDVI greater than the range of 0.42–0.65, and MODIS-NDVI greater than the range of 0.49–0.75; (3) Smaller absolute errors from a linear NDVI-UGS relationship were often found in more developed locations. We therefore recommend NDVI as a reliable predictor of UGS coverage and its use in longitudinal studies. Future studies should also consider fine resolution land cover maps and Lidar, which are increasingly available to derive detailed UGS characteristics.

Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge

ABSTRACT The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.

Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area

With the development of remote sensing technology, a number of fine-resolution (30-m) global/national land cover (LC) products have been developed. However, accuracy assessments for the developed LC products are commonly conducted at global and national scales. Due to the limited availability of representative validation observations and reference data, knowledge relating to the accuracy and applicability of existing LC products on a regional scale is limited. Since Xinjiang, China, exhibits diverse surface cover and fragmented urban landscapes, existing LC products generally have high classification uncertainty in this region. This makes Xinjiang suitable for assessing the accuracy and consistency of exiting fine-resolution land cover products. In order to improve knowledge of the accuracy of existing fine-resolution LC products at the regional scale, Xinjiang province was selected as the case area. First, we employed an equal-area stratified random sampling approach with climate, population density, and landscape heterogeneity information as constraints, along with the hexagonal discrete global grid system (HDGGS) as basic sampling grids to develop a high-density land cover validation dataset for Xinjiang (HDLV-XJ) in 2020. This is the first publicly available regionally high-density validation dataset that can support analysis at a regional scale, comprising a total of 20,932 validation samples. Then, based on the generated HDLV-XJ dataset, the accuracies and consistency among three widely used 30-m LC products, GLC_FCS30, GlobeLand30, and CLCD, were quantitatively evaluated. The results indicated that the CLC_FCS30 exhibited the highest overall accuracy (88.10%) in Xinjiang, followed by GlobeLand30 (with an overall accuracy of 83.58%) and CLCD (81.57%). Moreover, through a comprehensive analysis of the relationship between different environmental conditions and land cover product performance, we found that GlobeLand30 performed best in regions with high landscape fragmentation, while GLC_FCS30 stood out as the most outstanding product in areas with uneven proportions of land cover types. Our study provides a novel insight into the suitability of these three widely-used LC products under various environmental conditions. The findings and dataset can provide valuable insights for the application of existing LC products in different environment conditions, offering insights into their accuracies and limitations.

Effectiveness of landscape indicators for explaining the variability of benthic macro-invertebrates in urban streams

Landscape indicators – measures of land use and land cover – are widely used as proxies for monitoring urban stream conditions, particularly for benthic invertebrates which are often negatively impacted by watershed urbanization. However, multi-scale and nonlinear relationships between benthic macroinvertebrates and landscape configuration derived from fine spatial resolution land cover are not well explored. Here, we developed a series of landscape indicators and assessed their effectiveness in explaining the variability of benthic macroinvertebrate communities in 63 streams across the Greater Vancouver Region in British Columbia, Canada. We asked: 1) How effective are multi-scale landscape indicators in explaining the variability of instream benthic macroinvertebrates? 2) Does the explanatory power of landscape indicators vary at different spatial scales? 3) Do different urban forest classification schemes and their spatial configurations impact the explanatory power of landscape indicators? We developed high spatial resolution (5-m) landscape indicators and evaluated their utility in statistical models explaining taxa richness, instream benthic indices of biological integrity (B-IBI), % Ephemeroptera, Plecoptera, and Trichoptera (EPT), and % Oligochaetes. For all benthic responses, landscape indicators measured at the watershed scale explained 5–25% more variation than riparian-based indicators. Combining indicators mapped at multiple scales further improved the explanatory power of landscape indicators for % EPT and % Oligochaetes, ultimately explaining over 70% of the variability of benthic macro-invertebrates in streams. Distinguishing deciduous and coniferous forest types improved the explanatory power of landscape indicators in a riparian model for B-IBI by 10%. When considering the spatial arrangement of land cover, patch density of forests in the surrounding watersheds of stream explained as much as 47% of the variability in % Oligochaetes. Our results highlight the importance of investigating nonlinear relationships between benthic macroinvertebrates and landscape configuration. This monitoring approach is transferable across cities interested in maintaining the ecological health of urban streams while supporting urban expansion and growth.

Reliability and consistency assessment of land cover products at macro and local scales in typical cities

ABSTRACT Urban areas have higher heterogeneity compared to natural areas, it is crucial to assess fine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various application scenarios. In this study, five typical cities in China were chosen as study areas to evaluate four commonly used 30 m land cover products: GLC_FCS30-2020, FROM-GLC30-2017, Globeland30-2020, and CLCD-2019. We analyzed the reliability of these four products using validation samples as well as by examining their area and spatial pattern consistency. Given the limitations of traditional accuracy assessments at the macro level, we added a local area evaluation to further examine the classification details in these products. The macro results indicated that four land cover products within urban areas have a similar overall accuracy, surpassing 76%, but there was a low consistency among them, ranging from 42.21% to 61.13%. The local accuracy assessment illustrated that GLC_FCS30-2020 and FROM-GLC30-2017 performed well in reflecting the intricate details of the city, however, the four products exhibited varying degrees of misclassifications and omissions. These phenomena suggest that more sophisticated algorithms are needed to consider urban particularities since fine-resolution land cover products may fail to capture complex urban details.

Contrastive Analysis and Accuracy Assessment of Three Global 30 m Land Cover Maps Circa 2020 in Arid Land

Fine-resolution land cover (LC) products are critical for studies of urban planning, global climate change, the Earth’s energy balance, and the geochemical cycle as fundamental geospatial data products. It is important and urgent to evaluate the performance of the updated global land cover maps. In this study, three widely used LC maps with 30 m spatial resolution (FROM-GLC30-2020, GLC_FCS30, and GlobeLand30) published around 2020 were evaluated in terms of their degree of consistency and accuracy metrics. First, we compared their similarities and difference in the area ratio and spatial patterns over different land cover types. Second, the sample and response protocol was proposed and validation samples were collected. Based on this, the overall accuracy, producer’s accuracy, and user’s accuracy were analyzed. The results revealed that: (1) the consistent areas of the three maps accounted for 65.96% of the total area and that two maps exceeded 75% of it. (2) The dominant land cover types, bare land and grassland, were the most consistent land cover types across the three products. In contrast, the spatial inconsistency of the wetland, shrubland, and built-up areas were relatively high, with the disagreement mainly occurring in the heterogeneous regions. (3) The overall accuracy of the GLC_FCS30 map was the highest with a value of 87.07%, which was followed by GlobeLand30 (85.69%) and FROM-GLC30 (83.49%). Overall, all three of the LC maps were found to be consistent and have a good performance in classification in the arid regions, but their ability to accurately classify specific types varied.

Improved forest cover mapping by harmonizing multiple land cover products over China

ABSTRACT Fine resolution land cover products are becoming increasingly more available for many regions. These products, however, may not meet the quality requirements of many applications. This study provides an approach for improving land cover mapping by leveraging existing products and clear view Landsat composites. Assessments using independent reference datasets revealed that the CAF-LC30 2020 product derived using this approach over China was more accurate than four existing land cover products. Its overall accuracy with field observations was 2.94% to 10.28% higher than those of the four existing land cover products in northeast China and was 2.10% to 8.18% better across China. It provided a more accurate representation of the land cover types in many regions where the existing land cover products had large classification errors. Forest areas calculated using the CAF-LC30 2020 for the 31 provinces and autonomous regions and municipalities (PARM) in mainland China were better correlated with those reported by the most recent National Forest Inventory (NFI) survey than areas calculated using the other four existing land cover products. Therefore, the CAF-LC30 2020 product should be a better alternative for understanding China’s forests in 2020 than the other four existing land cover products.

Spatio-temporal subpixel mapping with cloudy images

Spatio-temporal subpixel mapping (STSPM) has shown great potential for monitoring land surfaces, by generating land cover maps with both fine spatial and temporal resolutions. Selecting cloud-free fine spatial resolution images as ancillary data for STSPM can ensure that the temporal dependence term is measured for all subpixels, as in all current STSPM methods. However, such images are generally limited by cloud contamination, thereby resulting in great land cover changes between the available clear image and the desired fine spatial resolution land cover map. This research proposes a cloud-independent STSPM (C-STSPM) method to reconstruct the fine spatial resolution land cover maps by using cloudy images directly, which are assumed to have fewer land cover changes than temporally distant clear images. Cloud-independent spatio-temporal dependence was proposed in the presence of cloudy pixels. Experiments were performed under various cloud conditions involving 21 × 21 pairs of simulated cloudy images. The results demonstrate that by utilizing land cover information of clear pixels in cloudy images, more accurate prediction can be produced by C-STSPM compared to directly discarding those cloudy images, even if the number of cloud pixels increases to 95%. The advantage of C-STSPM is more evident when the clouds are distributed sparsely, which benefits from the increased number of clear pixels at the edge of the cloudy areas. Furthermore, a negative linear correlation was detected between the prediction accuracy and the ratio of overlapping cloudy pixels in the cloudy images. Moreover, the C-STSPM method helps to deal with abrupt changes occurred in the temporally distant cloud-free images by utilizing the temporally adjacent cloudy images with gradual land cover changes. Overall, the C-STSPM method provides a completely new solution to make fuller use of the widely existing cloudy images in multi-scale time-series images.

Evaluation of MODIS, Climate Change Initiative, and CORINE Land Cover Products Based on a Ground Truth Dataset in a Mediterranean Landscape

Land cover can reflect global environmental changes if their associated transitions are quantitatively and correctly analysed, thus helping to assess the drivers and impacts of climate change and other applied research studies. It is highly important to acquire accurate spatial land cover information to perform multidisciplinary analyses. This work aims at estimating the accuracy of three widely used land cover products, the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1), the European Space Agency Climate Change Initiative land cover (ESA-CCI-LC), and the EU CORINE land cover (CLC), all for the reference year of 2018, by comparing them against a fine resolution land cover dataset created for this study with combined ground surveys and high-resolution Large Scale Orthophotography (LSO 25/2015). Initially, the four datasets had their land cover classes harmonized and all were resampled to the same spatial resolution. The accuracy metrics used to conduct the comparisons were Overall Accuracy, Producer’s Accuracy, User’s Accuracy, and the Kappa Coefficient. Comparisons with the reference dataset revealed an underestimation of the forested areas class in all three compared products. Further analysis showed that the accuracy metrics were reasonably high for the broad classes (forest vs. non-forest), with an overall accuracy exceeding 70% in all examined products. On the contrary, in the detailed classification (total land cover mapping), the comparison of the reference dataset with the three land cover products highlighted specific weaknesses in the classification results of the three products, showing that CLC depicted more precisely the landscape characteristics than the two other products, since it demonstrated the highest overall accuracy (37.47%), while MODIS and ESA-CCI-LC revealed a percentage that did not exceed 22%.

Graph Convolutional Networks-Based Super-Resolution Land Cover Mapping

Super-resolution mapping (SRM) is an effective technology to solve the problem of mixed pixels because it can be used to generate fine-resolution land cover maps from coarse-resolution remote sensing images. Current methods based on deep neural networks have been successfully applied to SRM, as they can learn complex spatial patterns from training data. However, they lack the ability to learn structural information between adjacent land cover classes, which is vital in the reconstruction of spatial distribution. In this article, an SRM method based on graph convolutional networks (GCNs), named SRM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GCN</sub> , is proposed to improve SRM results by capturing structure information on the graph. In SRM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GCN</sub> , a supervised inductive learning strategy with mini-graphs as input is considered, which is an extension of the GCN framework. Furthermore, two operations are designed in terms of adjacency matrix construction and an information propagation rule to help reconstruct detailed information of geographical objects. Experiments on three datasets with different spatial resolutions demonstrate the qualitative and quantitative superiority of SRM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GCN</sub> over three other popular SRM methods.

Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia

Land degradation is an important environmental problem facing the world. “Land Degradation Neutrality” is one of the core indicators in the 15th goal of the “United Nations Sustainable Development Goals” for 2030. Mongolia is an important country for global land degradation. The increasingly serious land degradation has caused a direct impact on the ecosystem of the entire Mongolian plateau. We analyzed the patterns of land degradation and restoration during 1990–2010 and 2010–2015 and determined the driving forces behind the variations, by using fine resolution land cover data for the first time in Mongolia. The results showed that the spatial distribution of newly increased land degradation and restoration have a strong transitional nature. For the past 25 years, the trend of land change in Mongolia was dominated by land degradation. However, land degradation was accompanied by ongoing restoration of some land areas, and the capacity for land restoration has been gradually improved. This study discovers a series of typical land degradation and restoration regions and provides an interpretation of the driving forces in these areas. The joint effects of natural and socioeconomic factors have been found to result in land degradation and restoration in different regions.

Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

Super-resolution land cover mapping by deep learning

ABSTRACTSuper-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional proportion images. SRM is often based explicitly on the use of a spatial pattern model that represents the land cover mosaic at the fine spatial resolution. Recently developed deep learning methods have considerable potential as an alternative approach for SRM, based on learning the spatial pattern of land cover from existing fine resolution data such as land cover maps. This letter proposes a deep learning-based SRM algorithm (DeepSRM). A deep convolutional neural network was first trained to estimate a fine resolution indicator image for each class from the coarse resolution fractional image, and all indicator maps were then combined to create the final fine resolution land cover map based on the maximal value strategy. The results of an experiment undertaken with simulated images show that DeepSRM was superior to conventional hard classification and a suite of popular SRM algorithms, yielding the most accurate land cover representation. Consequently, methods such as DeepSRM may help exploit the potential of remote sensing as a source of accurate land cover information.

Comparing the Effects of Temporal Features Derived From Synthetic Time-Series NDVI on Fine Land Cover Classification

Landsat data are an ideal data source for deriving fine-resolution land cover maps, and integrating temporal features extracted from time-series normalized difference vegetation index (NDVI) data achieves better performance. This paper compares the different roles of NDVI statistic features and phenology features in land cover classification at a finer scale. Time-series NDVI with fine resolution is first obtained by fusing Landsat-8 Operational Land Imager and moderate resolution imaging spectrometer (MODIS) NDVI via spatiotemporal fusion algorithm. Statistic and phenology features are then extracted from the fused data and added into random forest (RF) classifier. Performance under different classifiers and importance of phenology features are further discussed. Results show that both NDVI statistic features and phenology features have great effects on improving the classification accuracy after adding them to Landsat spectral bands. The overall accuracy is improved approximately 3% and 5%. Phenology features contain majority information of statistic features, and better reflect the seasonal variations of time-series NDVI, especially for vegetation types. Additionally, neural network classifier achieved similar trends of results with RF but lower accuracy, while support vector machine classifier seems to be poor in dealing with high-dimension temporal features, especially in regions with abundant vegetation. Among phenology features, maximum value, large integrated value, and base value have the highest importance scores, while start, end, and middle times of season provide extra information for identifying grass and nongrass.

Characterizing multi-decadal, annual land cover change dynamics in Houston, TX based on automated classification of Landsat imagery

ABSTRACTIn 2017, Hurricane Harvey caused substantial loss of life and property in the swiftly urbanizing region of Houston, TX. Now in its wake, researchers are tasked with investigating how to plan for and mitigate the impact of similar events in the future, despite expectations of increased storm intensity and frequency as well as accelerating urbanization trends. Critical to this task is the development of automated workflows for producing accurate and consistent land cover maps of sufficiently fine spatio-temporal resolution over large areas and long timespans. In this study, we developed an innovative automated classification algorithm that overcomes some of the traditional trade-offs between fine spatio-temporal resolution and extent – to produce a multi-scene, 30m annual land cover time series characterizing 21 years of land cover dynamics in the 35,000 km2 Greater Houston area. The ensemble algorithm takes advantage of the synergistic value of employing all acceptable Landsat imagery in a given year, using aggregate votes from the posterior predictive distributions of multiple image composites to mitigate against misclassifications in any one image, and fill gaps due to missing and contaminated data, such as those from clouds and cloud shadows. The procedure is fully automated, combining adaptive signature generalization and spatio-temporal stabilization for consistency across sensors and scenes. The land cover time series is validated using independent, multi-temporal fine-resolution imagery, achieving crisp overall accuracies between 78–86% and fuzzy overall accuracies between 91–94%. Validated maps and corresponding areal cover estimates corroborate what census and economic data from the Greater Houston area likewise indicate: rapid growth from 1997–2017, demonstrated by the conversion of 2,040 km2 (± 400 km2) to developed land cover, 14% of which resulted from the conversion of wetlands. Beyond its implications for urbanization trends in Greater Houston, this study demonstrates the potential for automated approaches to quantifying large extent, fine resolution land cover change, as well as the added value of temporally-dense time series for characterizing higher-order spatio-temporal dynamics of land cover, including periodicity, abrupt transitions, and time lags from underlying demographic and socio-economic trends.

Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

Accuracy Assessment of GlobeLand30 2010 Land Cover over China Based on Geographically and Categorically Stratified Validation Sample Data

Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products.

Spatiotemporal nexus between the pattern of land degradation and land cover dynamics in Iran

AbstractIn recent decades, extensive land transformations and environmental and climate change events such as floods and droughts, increasing heat waves, and forest fires have been observed in Iran. Monitoring and intensity analysis of the land cover dynamics and land degradation in Iran are lacking due to the lack of fine resolution land cover data. In this paper, we explore the type, extent, and intensity of land transformations and the intensity of transitions among land cover classes from 2000 to 2010 using GlobeLand30 land cover data. Our results indicate that approximately 35% of Iran changed during 2000–2010, due mainly to the active gain of barren land. Furthermore, the increase in barren targeted grass and shrub. Barren expansion in Iran is alarming because most of the country is located in arid and semi‐arid regions. Iran has actively participated in desertification combat plans, and thus, it is critical to explore extra intervals of land dynamics. This will help to evaluate the temporal rate of land degradation at multiple intervals and assess the effectiveness of desertification management strategies. Additionally, investigating the role of climate and human‐made interventions into the type and extent of land transformation is recommended.

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.