Distribution Of Land Cover Types Research Articles

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

An experimental comparison of pixel-based and object-based classifications with different machine learning algorithms in landscape pattern analysis – Case study from Quang Ngai city, Vietnam

In landscape pattern analysis, the choice of an efficient method for image classification is widely studied, but the features are mostly extracted from digital values by the traditional approach (Pixel-based) and modern approach (Object-based). In this study, we compared the performance of two supervised classification algorithms (Maximum likelihood classifier - MLC and Support vector machines - SVM). We used SPOT-5 image data from 2011 to analyze the landscape pattern of a complex territory in Quang Ngai City, Vietnam. We collected 215 ground-truth samples and classified them into seven landscape classes. The results showed that the overall accuracy of the classification of Pixel-based (MLC), Object-based (MLC), Pixel-based (SVM) and Object-based (SVM) was 67.9%, 74.0%, 72.1%, and 82.3%, respectively. The combination of the object-based approach and SVM algorithm had the best classification result, which reflected the current spatial distribution of land cover types accurately. In the next step, we computed landscape metrics from detailed images to compare quantitative parameters with the actual verification data sources. From these metrics results, we discussed how classification methods could affect landscape structure, and possible ways to improve the accuracy of landscape-pattern identification.

Monitoring the evolution of a wetland pattern near Addis Ababa with the use of Landsat data (1986-2019)

A wetland is an area of land that is covered by water, whether salt, fresh, or somewhere in between, either seasonally or permanently. It operates as a separate ecosystem. Wetlands are distinguished from other types of land or bodies of water primarily by vegetation that has adapted to moist soil. However, this study focused on identifying wetlands, and change in Addis Ababa, the study employs remote sensing and GIS software. USSG provides free pictures from Landsat 5 TM and Landsat 8 OLI. The area is separated into six classes, with a Confusion Matrix reaching an accuracy of 95.58% for images taken between 1986 and 2019, and 86% in 2019. The Kappa Coefficients (k) are 0.8645 and 0.82, respectively. The Confusion Matrix also reports the Kappa Coefficient (k), which is another indicator of categorization accuracy. The k values for the picture classifications in 1986 and 2019 were 0.8645 and 0.82, respectively. Wetlands totaled 52.66 km2 of land in the original condition (1986) image; in 2019, that figure had reduced to 17.03 km2. The distribution of land cover types in 1986 and 2019 for built-up and other concerns influenced the demise of the marsh in and around the city. To develop a smart city and a green, climate-resilient economy, immediate and mid-term restoration management plans are required.

Pemetaan Potensi Biomassa Permukaan Rawan Terbakar Berbasis Citra Landsat 8 Oli Di Kecamatan Kahayan Hilir Kabupaten Pulang Pisau

Peatland fires in 2015 in Central Kalimantan were biomass fires on peatlands that burned with an area of ​​196,987 hectares and one of them was in Kahayan Hilir District. this research aims to determine the potential distribution of surface biomass and identify the types of vegetation. this research uses Landsat 8 OLI imagery in 2020 as well as land cover classification and calculation of the Vegetation Index (NDVI) which is then combined with an overlay to create a map of the distribution of land cover types based on their density which is then used for field checks and surface biomass calculations using the allometric formula on each type of land cover. Based on the research conducted, there are 8 types of land cover that can be identified and the percentage of total biomass, namely high density forest (1,419,013,40 tons or 62.58%), high density shrubs and shrubs (770,449.68 tons or 33.98%). ), High density settlements (44.169.32 tons or 1.95 %), high density oil palm plantations (21,518.77 tons or 0.95 %), High density open land (8,025.37 tons or 0.35%), plantations medium density palm oil (2,690.09 tons or 0.12%), medium density open land (1,545.81 tons or 0.07%) and low density open land (79.65 tons or 0.004%). The types of tree-level vegetation found in high-density forests are laban (Vitex pinnata), mahang damar (Macaranga triloba), and bungur (Lagerstroemia speciosa), while on understorey grass species rija-rija (Scleria sumatrensis) are found

Mapping Potential Locations of Reservoir Development Planning Based on Biogeophysical Conditions in Bulok Watershed of Lampung Province

Floods and droughts are problems that threaten the success of agricultural crops. The reservoir can be used as a water management system that can anticipate these problems. This study aims to examine the potential location of the reservoir development planning based on biogeophysical conditions in the Bulok Watershed of Lampung Province, which uses the overlay method and weighting and scoring techniques developed by Geographic Information System (GIS). The population covers the entire biogeophysical area of the Bulok watershed, with samples of 4 biogeophysical parameters, namely land cover, slope, soil conditions and geological conditions. The results showed that the distribution of land cover types that dominate in the Bulok watershed is mixed dry land agriculture. The most common slopes are flat to wavy slopes. The soil types that dominates are soils with andesite lithology, basalt, diorite, fine-grained tefra, and coarse-grained tefra. The geological formation that dominates is the Hulu Simpang Formation. Based on the 4 biogeophysical parameters selected in the study, there are 69 locations with great potential for planning the construction of reservoirs in the Bulok watershed. 20 points are spread over the administrative area of Tanggamus Regency, 23 points in Pringsewu Regency, and 26 points in Pesawaran Regency. Of the 87.670 ha of Bulok watershed area, 14.192 ha is very potential location area. Keywords: Biogeophysical Conditions, Potential Locations, Reservoir

CoastTrain: A Global Reference Library for Coastal Ecosystems

Estimating the distribution, extent and change of coastal ecosystems is essential for monitoring global change. However, spatial models developed to estimate the distribution of land cover types require accurate and up-to-date reference data to support model development, model training and data validations. Owing to the labor-intensive tasks required to develop reference datasets, often requiring intensive campaigns of image interpretation and/or field work, the availability of sufficiently large quality and well distributed reference datasets has emerged as a major bottleneck hindering advances in the field of continental to global-scale ecosystem mapping. To enhance our ability to model coastal ecosystem distributions globally, we developed a global reference dataset of 193,105 occurrence records of seven coastal ecosystem types—muddy shorelines, mangroves, coral reefs, coastal saltmarshes, seagrass meadows, rocky shoreline, and kelp forests—suitable for supporting current and next-generation remote sensing classification models. coastTrain version 1.0 contains curated occurrence records collected by several global mapping initiatives, including the Allen Coral Atlas, Global Tidal Flats, Global Mangrove Watch and Global Tidal Wetlands Change. To facilitate use and support consistency across studies, coastTrain has been harmonized to the International Union for the Conservation of Nature’s (IUCN) Global Ecosystem Typology. coastTrain is an ongoing collaborative initiative designed to support sharing of reference data for coastal ecosystems, and is expected to support novel global mapping initiatives, promote validations of independently developed data products and to enable improved monitoring of rapidly changing coastal environments worldwide.

Evaluation of effectiveness of supervised classification algorithms in land cover classification using ASTER images-A case study from the Mankweng (Turfloop) Area and its environs, Limpopo Province, South Africa

The production of land cover maps using supervised classification algorithms is one of the most common applications of remote sensing. In this study, the effectiveness of supervised classification algorithms in land cover classification using ASTER data was evaluated in the Mankweng Area and its environs. The false colour composite image generated from combination of band 1, 2 and 3 in red, green and blue, respectively, was used to generate training classes for six land cover types (waterbody, forest, vegetation, Duiwelskloof leucogranite, Turfloop granite and built-up land). These were used to construct land cover maps using eight supervised classification algorithms: Maximum Likelihood, Minimum Distance, Support Vector Machine, Mahalanobis Distance, Parallelepiped, Neural Network, Spectral Angle Mapper and Spectral Information Divergence. To evaluate the effectiveness of the algorithms, the land cover maps were subjected to accuracy assessment to determine precision of the algorithms in accurately classifying the land cover types and level of confidence that can be attributed to the land cover maps. Most algorithms poorly performed in classifying spatially overlapping land cover types without abrupt boundaries. This indicates that the environmental conditions and distribution of land cover types can affect the performance of certain classification algorithms, and thus need to be considered prior to selection of algorithms. However, Support Vector Machine and Minimum Distance proved to be the two most effective algorithms as they provided better producer’s and user’s accuracy in the range of 80-100% for all land cover types, which represent good classification.

Knowledge-guided land pattern depiction for urban land use mapping: A case study of Chinese cities

Accurate urban land-use maps, which reflect the complicated land-use pattern implied in the function and distribution of land-cover types, play an important role in urban analysis. In recent years, data-driven deep learning-based land-use mapping methods have made great breakthroughs due to their strong feature extraction ability. Meanwhile, multisource geographic data, such as open street map (OSM), has been applied in land-use mapping with high spatial resolution remote sensing (HSR) imagery. Nevertheless, given the intraclass visual inconsistency and interclass label ambiguity, there are enormous challenges in OSM-based land-use pattern depiction: 1) the significant size variability of land-use parcel generated by OSM; 2) the weak interpretability of the data-driven based features; and 3) the neglect of intrinsically hierarchical and nested relationships between land-cover and land-use. In this paper, to bridge the “knowledge gap” for urban land-use mapping, a knowledge-guided land pattern depicting (KGLPD) framework is proposed. The proposed KGLPD framework mainly contains four parts. Land-use parcels with various scales are generated based on OSM. An adaptive gradient perceptive (AGP) mechanism is proposed to provide patch distribution prior knowledge for guiding the data-driven based visual feature extraction. To effectively cognize the layout of different land-cover types as the knowledge-driven information, a land pattern cognitive (LPC) model is designed to capture the inner and outer relationships (i.e., direction, distance and co-frequency) of different land-cover types. The fully sparse topic model (FSTM) is then used to extract the critical land pattern information from the data-driven and knowledge-driven information. Four typical Chinese urban cities are selected to evaluate the proposed framework. Experimental results on three cities with four regions of distinctive characteristics in different years, have achieved high classification accuracies of about 80%, with 10% improvement compared with other methods. This demonstrates the effectiveness and robustness of the proposed knowledge-guided urban land use mapping framework. Experimental results on the whole city of Shenzhen in China imply that the proposed framework perform well with small training samples. The results on different cities validate the generalizability and transferability of KGLPD. The typical land-use maps and the corresponding land-cover maps help understanding the relationship between them.

Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)

Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.

Landscape patterns of ocelot–vehicle collision sites

Road networks can negatively impact wildlife populations through habitat fragmentation, decreased landscape connectivity, and wildlife-vehicle collisions, thereby influencing the spatial ecology and population dynamics of imperiled species. The ocelot (Leopardus pardalis) is a federally endangered wild felid in South Texas, with a high mortality rate linked to vehicle collisions. Using a multi-scale approach, we quantified and examined landscape spatial structure at ocelot roadkill locations, and between roadkill locations of male and female ocelots. We quantified the spatial distribution of land cover types at 26 ocelot–vehicle collision sites in South Texas that occurred from 1984–2017. We compared landscape metrics of woody, herbaceous, and bare ground cover types across multiple spatial scales at roadkill locations to those from random road locations, and between male and female ocelots. Roadkill sites consisted of 13–20% more woody cover than random locations. Woody patches at roadkill sites were 7.1–11% larger (2.4 ha) closer to roads and spaced 10–16 m closer together farther away from roads compared to random locations. Percent woody cover was the best indicator of ocelot–vehicle collision sites; there were no differences in woody cover between male and female road mortality locations. These findings suggest that ocelots are likely struck by vehicles while crossing between habitat patches. Roads that bisect areas of woody cover have negative impacts on ocelots by increasing habitat fragmentation and vulnerability to vehicle collisions. Crossing structures should be placed in areas with ≥ 30% woody cover and 3.5 ha woody patches.

Vertical differentiation of land cover in the central Himalayas

Characterized by obvious altitudinal variation, habitat complexity, and diversity in land cover, the Mt. Qomolangma region within the central Himalayas is one of the most sensitive areas to climate change in the world. At the same time, because the Mt. Qomolangma region possesses the most complete natural vertical spectrum in the world, it is also an ideal place to study the vertical structure of alpine land cover. In this study, land cover data for 2010 along with digital elevation model data were used to define three methods for dividing the northern and southern slopes in the Mt. Qomolangma region, i.e., the ridgeline method, the sample transect method, and the sector method. The altitudinal distributions of different land cover types were then investigated for both the northern and southern slopes of the Mt. Qomolangma region by using the above three division methods along with ArcGIS and MATLAB tools. The results indicate that the land cover in the study region was characterized by obviously vertical zonation with the south-six and north-four pattern of vertical spectrum that reflected both the natural vertical structure of vegetation and the effects of human activities. From low to high elevation, the main land cover types were forests, grasslands, sparse vegetation, bare land, and glacier/snow cover. The compositions and distributions of land cover types differed significantly between the northern and southern slopes; the southern slope exhibited more complex land cover distributions with wider elevation ranges than the northern slope. The area proportion of each land cover type also varied with elevation. Accordingly, the vertical distribution patterns of different land cover types on the southern and northern slopes could be divided into four categories, with glaciers/snow cover, sparse vegetation, and grasslands conforming to unimodal distributions. The distribution of bare land followed a unimodal pattern on the southern slope but a bimodal pattern on the northern slope. Finally, the use of different slope division methods produced similar vertical belt structures on the southern slope but different ones on the northern slope. Among the three division methods, the sector method was better to reflect the natural distribution pattern of land cover.

Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China

The increase in impervious surfaces due to the urbanization has caused many adverse effects on urban ecological systems, including the urban heat environmental risk. Revealing the relationship between landscape composition and pattern and land surface temperature (LST) gives insight into how to effectively mitigate the urban heat island (UHI) effect. It is also essential to simulate and optimize the distribution of impervious surfaces in urban planning. In this study, the multi-scale relationship between impervious surface and LST in Beijing was analyzed. Different distributions of land cover types and the corresponding LSTs were simulated under two development scenarios. Various geospatial approaches, including geographic information system (GIS), remote sensing, and the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), were used to facilitate the analysis. The results showed that (1) impervious surfaces increased from 36.76% to 44.95% of the total area between 2005 and 2015 and the mean LST of impervious surfaces was approximately 2 °C higher than that of the areas with vegetation cover; (2) impervious surfaces had a positive logarithmic correlation with LST, while the vegetation coverage had a negative linear correlation with LST; (3) as the grid size increased, the correlation coefficients between the impervious surface density and mean LST increased at different magnitudes, and the correlation coefficients stabilized after the scale of 900 × 900 m; (4) large and contiguous patches of impervious surfaces aggravated the UHI effect when the total percentage of impervious surface remained the same; and (5) to achieve an improved and healthier urban living environment, populations controls should be considered to decrease future impervious surface demands by 7.69%—which corresponds to an average LST decrease of 1.1 °C. Landscape distribution and configuration should also be better integrated into landscape and urban planning.

Soil organic carbon and total nitrogen pools in permafrost zones of the Qinghai-Tibetan Plateau

There are several publications related to the soil organic carbon (SOC) on the Qinghai-Tibetan Plateau (QTP). However, most of these reports were from different parts of the plateau with various sampling depth. Here, we present the results from a systematic sampling and analysis of 200 soil pits. Most of the pits were deeper than 2 m from an east-west transect across the plateau. The SOC and total nitrogen (TN) pools of the 148 × 104 km2, the area of the permafrost zone, for the upper 2 m soils calculated from the vegetation map were estimated to be 17.07 Pg (interquartile range: 11.34–25.33 Pg) and 1.72 Pg (interquartile range: 1.08–2.06 Pg), respectively. We also predicted the distribution of land cover types in 2050 and 2070 using decision tree rules and climate scenarios, and then predicted SOC and TN pools of this region. The results suggested that the SOC and TN pools will decrease in the future. The results not only contribute to the carbon and nitrogen storage and stocks in the permafrost regions as a whole but most importantly, to our knowledge of the possible changes of C and N storage on the QTP in the future.

Classification of Vegetation Type in Iraq Using Satellite-Based Phenological Parameters

Primary information of great importance to various grand challenges such as sustainable agricultural intensification, food insecurity, and climate change impacts, can be obtained indirectly from land cover monitoring. However, in arid-to-semiarid regions, such as Iraq, accurate discrimination of different vegetation types is challenging due to their similar spectral responses. Moreover, Iraq has been subjected to major disturbances, both natural and anthropogenic which have affected the distribution of land cover types through space and time. Reliable information about croplands and natural vegetation in such regions is generally scarce. This research aimed to develop a phenology-based classification approach using support vector machines for the assessment of space-time distribution of the dominant vegetation land cover (VLC) types in Iraq, particularly croplands, from 2002 to 2012. Thirteen successive years of 8-day composites of MODIS-NDVI data at a spatial resolution of 250 m were employed to estimate 11 phenological parameters. The classification methodology was assessed using reference samples taken from fine spatial resolution imagery and independent testing data obtained through fieldwork. Overall accuracies were generally ${>} {85}\,\% $ , with relatively high Kappa coefficients $\left({ {>} {0}.{86}} \right)$ across the classified land cover types. The predicted cropland class area and the Global MODIS land cover product were compared with ground statistical data at the governorate level, revealing a significantly larger coefficient of determination for the present phenology-based approach $\left({{{\textit{R}}^{\text {2}}} = 0.70\;\text{against} \;{{\text {R}}^{\text {2}}} = 0.33\;\text{for\;MODIS},\;{\text{p}{ . The resulting maps delimit for the first time, at a fine spatial resolution, the spatial and interannual variability in the dominant VLC classes across Iraq.

Landscape heterogeneity and scale considerations for super-resolution mapping

Super-resolution mapping (SRM) is a rapidly emerging field of remote sensing that seeks to map the spatial distribution of land cover proportions resulting from soft classification techniques. Many methods have been proposed, but there has been little impetus to converge these efforts or compare the results. One reason for this lack of comparison is the issue of landscape heterogeneity and the range of scaling issues it embodies. Landscape heterogeneity refers to the complex distribution of land cover types across the landscape and the spatial patterns or spatial frequency of those land cover types. Landscape heterogeneity and the spatial patterns it produces are inherently scale-dependent while SRM is fundamentally an inverse scaling challenge being applied to real landscapes. The result is that SRM techniques developed for a particular landscape or scaling factor may not translate well when applied to other landscapes with different heterogeneity or computed for a different scaling factor. This lack of transferability due to landscape heterogeneity has largely been ignored in the literature. Heterogeneity is rarely reported in SRM studies and its behaviour across resolutions rarely integrated into SRM techniques. This article discusses the importance of heterogeneity in SRM, demonstrates how heterogeneity impacts SRM results, proposes several solutions for reporting landscape heterogeneity, and discusses the difficulties associated with incorporating heterogeneity into SRM. By highlighting the interrelatedness between landscape heterogeneity and SRM, the aim is for studies to begin reporting heterogeneity to facilitate inter-comparisons and ultimately incorporate the scale-dependency of landscape heterogeneity into SRM techniques to improve accuracy and applicability.

The fragmented nature of tundra landscape

The vegetation and land cover structure of tundra areas is fragmented when compared to other biomes. Thus, satellite images of high resolution are required for producing land cover classifications, in order to reveal the actual distribution of land cover types across these large and remote areas. We produced and compared different land cover classifications using three satellite images (QuickBird, Aster and Landsat TM5) with different pixel sizes (2.4m, 15m and 30m pixel size, respectively). The study area, in north-eastern European Russia, was visited in July 2007 to obtain ground reference data. The QuickBird image was classified using supervised segmentation techniques, while the Aster and Landsat TM5 images were classified using a pixel-based supervised classification method. The QuickBird classification showed the highest accuracy when tested against field data, while the Aster image was generally more problematic to classify than the Landsat TM5 image. Use of smaller pixel sized images distinguished much greater levels of landscape fragmentation. The overall mean patch sizes in the QuickBird, Aster, and Landsat TM5-classifications were 871m2, 2141m2 and 7433m2, respectively. In the QuickBird classification, the mean patch size of all the tundra and peatland vegetation classes was smaller than one pixel of the Landsat TM5 image. Water bodies and fens in particular occur in the landscape in small or elongated patches, and thus cannot be realistically classified from larger pixel sized images. Land cover patterns vary considerably at such a fine-scale, so that a lot of information is lost if only medium resolution satellite images are used. It is crucial to know the amount and spatial distribution of different vegetation types in arctic landscapes, as carbon dynamics and other climate related physical, geological and biological processes are known to vary greatly between vegetation types.

Integrating Landscape Ecology and Geoinformatics to Decipher Landscape Dynamics for Regional Planning

We used remote sensing and GIS in conjunction with multivariate statistical methods to: (i) quantify landscape composition (land cover types) and configuration (patch density, diversity, fractal dimension, contagion) for five coastal watersheds of Kalloni gulf, Lesvos Island, Greece, in 1945, 1960, 1971, 1990 and 2002/2003, (ii) evaluate the relative importance of physical (slope, geologic substrate, stream order) and human (road network, population density) variables on landscape composition and configuration, and (iii) characterize processes that led to land cover changes through land cover transitions between these five successive periods in time. Distributions of land cover types did not differ among the five time periods at the five watersheds studied because the largest cumulative changes between 1945 and 2002/2003 did not take place at dominant land cover types. Landscape composition related primarily to the physical attributes of the landscape. Nevertheless, increase in population density and the road network were found to increase heterogeneity of the landscape mosaic (patchiness), complexity of patch shape (fractal dimension), and patch disaggregation (contagion). Increase in road network was also found to increase landscape diversity due to the creation of new patches. The main processes involved in land cover changes were plough-land abandonment and ecological succession. Landscape dynamics during the last 50 years corroborate the ecotouristic-agrotouristic model for regional development to reverse trends in agricultural land abandonment and human population decline and when combined with hypothetical regulatory approaches could predict how this landscape could develop in the future, thus, providing a valuable tool to regional planning.

Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica

In this paper we present the results of an experiment to measure forest structure and biomass dynamics over the tropical forests of La Selva Biological Station in Costa Rica using a medium resolution lidar. Our main objective was to observe changes in forest canopy height, related height metrics, and biomass, and from these map sources and sinks of carbon across the landscape. The Laser Vegetation Imaging Sensor (LVIS) measured canopy structure over La Selva in 1998 and again in 2005. Changes in waveform metrics were related to field‐derived changes in estimated aboveground biomass from a series of old growth and secondary forest plots. Pairwise comparisons of nearly coincident lidar footprints between years showed canopy top height changes that coincided with expected changes based on land cover types. Old growth forests had a net loss in height of −0.33 m, while secondary forests had net gain of 2.08 m. Multiple linear regression was used to relate lidar metrics with biomass changes for combined old growth and secondary forest plots, giving an r2 of 0.65 and an RSE of 10.5 Mg/ha, but both parametric and bootstrapped confidence intervals were wide, suggesting weaker model performance. The plot level relationships were then used to map biomass changes across La Selva using LVIS at a 1 ha scale. The spatial patterns of biomass changes matched expected patterns given the distribution of land cover types at La Selva, with secondary forests showing a gain of 25 Mg/ha and old growth forests showing little change (2 Mg/ha). Prediction intervals were calculated to assess uncertainty for each 1 ha cell to ascertain whether the data and methods used could confidently estimate the sign (source or sink) of the biomass changes. The resulting map showed most of the old growth areas as neutral (no net biomass change), with widely scattered and isolated sources and sinks. Secondary forests in contrast were mostly sinks or neutral, but were never sources. By quantifying both the magnitude of biomass changes and the sensitivity of lidar to detect them, this work will help inform the formulation of future space missions focused on biomass dynamics, such as NASA's Deformation Ecosystem Structure and Dynamics of Ice mission.

Land cover dynamics of different topographic conditions in Beijing, China

Topographic conditions play an important role in controlling land cover dynamic processes. In this study, remotely sensed data and the geographic information system were applied to analyze the changes in land cover along topographic gradients from 1978 to 2001 in Beijing, a rapidly urbanized mega city in China. The study was based on five periods of land cover maps derived from remotely sensed data: Landsat MSS for 1978, Landsat TM for 1984, 1992, 1996 and 2001, and the digital elevation model (DEM) derived from 1:250,000 topographic map. The whole area was divided into ten land cover types: conifer forest, broadleaf forest, mixed forest, shrub, brushwood, meadow, farmland, built-up, water body and bare land. The results are summarized as follows. (1) Shrub, forest, farmland and builtup consist of the main land cover types of the Beijing area. The most significant land cover change from 1978 to 2001 was the decrease of the farmland and expansion of the builtup area. Farmland decreased from 6354 to 3813 km2 in the 23 years, while the built-up area increased from 421 to 2642 km2. Meanwhile, the coverage of forest increased from 17.2% to 24.7% of the total area. The conversion matrix analysis indicated that the transformation of farmland to the built-up area was the most significant process and afforestation was the primary cause of the replacement of shrub to forest. (2) Topographic conditions are of great importance to the distribution of land cover types and the process of land cover changes. Elevation has an intensive impact on the distribution of land cover types. The area below 100 m mostly consists of farmland and built-up areas, while the area above 100 m is mainly covered by shrub and forest. Shrub has the maximum frequency in areas between 100 and 1000 m, while forest has dominance in areas above 800 m. According to the analysis of land cover changes in different ranges of elevation, the greatest change below 100 m was the process of urbanization. The process of the main land cover change occurred above 100 m was the transformation from shrub to forest. This result was consistent with the vertical change of natural vegetation distribution in Beijing. (3) Slope has a great influence on the distribution of land cover. Farmland and built-up areas are mostly distributed in flat areas, while shrub and forest occupy steeper areas compared with other land cover types. Forest frequency increased with the increasing slope. Land cover changes differed from the slope gradients. In the plain area, the land cover change occurred as the result of urbanization. With the increasing of the slope gradient, afforestation, which converts shrub to forest, was the process of the primary land cover change.

Scenarios of land cover in China

A method for surface modeling of land cover change (SMLC) is developed on the basis of establishing transition probability matrixes between land cover types and HLZ types. SMLC is used to simulate land cover scenarios of China for the years 2039, 2069 and 2099, for which HLZ scenarios are first simulated in terms of HadCM3 climatic scenarios that are downscaled in zonal model of spatial climate change in China. This paper also analyzes spatial distribution of land cover types, area change and mean center shift of each land cover type, ecotope diversity, and patch connectivity under the land cover scenarios. The results show that cultivated land would decrease and woodland would expand greatly with climatic change, which coincides with consequences expected by implementation of Grain-for-Green policy. Nival area would shrink, and desertification area would expand at a comparatively slow rate in future 100 years. Climate change would generally cause less ecotope diversity and more patch connectivity. Ecosystems in China would have a pattern of beneficial cycle after efficient ecological conservation and restoration. However, if human activities would exceed regulation capacity of ecosystems themselves, the ecosystems in China might deteriorate more seriously.