Multi-seasonal Landsat Research Articles

Monitoring native, non-native, and restored tropical dry forest with Landsat: A case study from the Hawaiian Islands

Tropical dry forests are highly threatened at a global scale. Long-term monitoring of remaining stands is needed to assess forest health, efficacy of management practices, and potential impacts of climate change. Using a multi-seasonal Landsat time series, we examined Normalized Difference Vegetation Index (NDVI) patterns in native dry forest, non-native vegetation types, and dry forest restoration sites from 1999 to 2022 in the Hawaiian Islands. We calculated trends in median NDVI and robust coefficient of variation of NDVI for dry and wet seasons, and used Breaks for Additive Seasonal and Trend analysis to detect trend departures. To assess the impact of regional drying trends, NDVI trends were compared to the seasonal long-term precipitation anomaly and cumulative precipitation anomaly. We found that native dry forest was less green than non-native forest, particularly during the dry season, and that median NDVI increased in both native and non-native dry forests over the study period despite negative precipitation anomaly trends. This result differs from coarser-scale studies in Hawaii, but is supported by trends in other dry forest regions. Greening was also observed in restoration study sites, especially larger sites where native species establishment and recruitment has been reported. Non-native grassland NDVI exhibited a strong positive link to precipitation anomalies, suggesting that drier climate scenarios may exacerbate the invasive grass-wildfire cycle that threatens native dry forest. These results demonstrate that Landsat time series may be used to detect seasonal variation in dry forest plots and to support restoration site monitoring in a highly fragmented ecosystem.

Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data

The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R2 of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables.

Assessing natural resource change in Vhembe biosphere and surroundings.

South Africa is a custodian of an immense wealth of natural and biodiversity resources in Africa. Natural resources are continually changing in different South African biospheres based on anthropogenic and non-anthropogenic causes. Land use activities like agriculture, cultivation, livestock rearing, commercial plantations, urbanisation and mining are among the major drivers of natural resource change and transformation. In this study, land cover change assessment was used to assess natural resource change in Vhembe biosphere and surroundings. To assess natural resource change in Vhembe biosphere, land use land cover change assessment was conducted using South African's national land-cover dataset, generated from multi-seasonal Landsat 5 and Sentinel-2 images. The 72× class land cover map was re-classified into 12× classes to fit the study objectives. Eight out of twelve classes quantified in hectares: indigenous forests, thicket/dense bush, natural woodland, shrubland, grassland, water bodies and wetlands were categorised as natural resources for which the natural resource change assessment for this study was based. Assessment findings established that land use and its related activities have contributed substantially to natural resource change where cultivated commercial, natural woodland and built-up residential contributed the most significant upward change in hectarage and percentage, from 132,246.9 to 365,644.92 (ha)-percentage change of 176%; from 94,665.42 to 257,889.68 (ha)-percentage changes of 172% and from 74,070.27 to 147,701.88(ha)-percentage change of 99% respectively. Shrubland, thicket/dense bush and indigenous forests registered the highest downward changes from 263,070.6 to 977.72 (ha); from 338,723.7 to 23,166.92 and from 13,211.91 to 7402.92 (ha) with percentage changes of -100%, -93% and -44% respectively in Vhembe biosphere and the surroundings from 1990 to 2018. The study showed how natural resources are changing and the use of remote sensing for environmental monitoring and assessment in the Vhembe district.

The fate of rangelands: Revealing past and predicting future land‐cover transitions from 1985 to 2036 in the drylands of Central Iran

AbstractLand serves as a vital production resource, and therefore land planning plays an important role in sustainable land‐use design. Iran includes large areas of the world's drylands, many of which are used as rangelands, and where the population has increased by 60% from 1985 to 2016. Further population increase in Iran would probably require more land resources to be allocated for human needs. However, the pace and patterns of these changes remain unclear. The aim of this study was to map land‐cover change from 1985 to 2016 and predict future land‐cover change in the Zayandehrood ecologic sub‐basins of Central Iran. By using multiseasonal LANDSAT imagery, we mapped land‐cover change with a Random Forest classifier for 1985, 1998, and 2016 with an overall accuracy of 80% for each period. Classification results revealed that from 1985 to 2016 residential areas doubled and industrial areas increased at the expense of rangelands. Our study also revealed cropland expansion at the expense of rangelands, cropland abandonment, and contraction of croplands due to residential and industrial development. Prediction of changes by 2036 with a multilayer perceptron neural network and Markov chain analysis showed further expansion of industries and residencies, particularly near the Ghamashlu Wildlife Refuge. Despite the partial restoration of rangelands in some parts of the world, our study provides evidence of ongoing rangeland degradation in Iran due to conversion to other land uses. Therefore, the study underscores the importance of sustainable land management of already cultivated areas and the development of strategies for the protection of global rangelands.

A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria

Providing constantly updated information on vegetation serves as a basis for studies of natural resources and ecological issues. This paper discusses the question related to an appropriate season(s) for classification vegetation cover in the Mediterranean region and detecting its changes using Landsat imagery. Autumn, spring, and multi-seasonal satellite images, captured in 2017, were used to classify vegetation cover in a part of the Lattakia province, Syria. The satellite images were classified using the random forest algorithm, and high spatial resolution satellite images Google Earth Pro were used as reference data. The results indicate better effectiveness of the autumn images over spring ones for vegetation cover classification with 73.6% and 62.4% overall accuracy, respectively. In addition, a comparison of autumn and multi-seasonal Landsat images indicates no significant statistical difference in the accuracy of vegetation cover classification at the significance level of 0.05, which illustrates the effectiveness of using autumn images to classify the vegetation cover of the Mediterranean region. Furthermore, the obtained results show the necessity of using additional features as the spectral channels may not be sufficient for mapping vegetation cover in the Mediterranean region with high accuracy.

Abandonment and Recultivation of Agricultural Lands in Slovakia—Patterns and Determinants from the Past to the Future

Central and Eastern Europe has experienced fundamental land use changes since the collapse of socialism around 1990. We analyzed the patterns and determinants of agricultural land abandonment and recultivation in Slovakia during the transition from a state-controlled economy to an open-market economy (1986 to 2000) and the subsequent accession to the European Union (2000 to 2010). We quantified agricultural land-use change based on available maps derived from 30-m multi-seasonal Landsat imagery and analyzed the socioeconomic and biophysical determinants of the observed agricultural land-use changes using boosted regression trees. We used a scenario-based approach to assess future agricultural land abandonment and recultivation until 2060. The maps of agricultural land use analysis reveal that cropland abandonment was the dominant land use process on 11% of agricultural land from 1986 to 2000, and on 6% of the agricultural land from 2000 to 2010. Recultivation occurred on approximately 2% of agricultural land in both periods. Although most abandoned land was located in the plains, the rate of abandonment was twice as high in the mountainous landscapes. The likelihood of abandonment increased with increased distance from the national capital (Bratislava), decreased with an increase of annual mean temperatures and was higher in proximity to forest edges and on steeper slopes. Recultivation was largely determined by the opposite effects. The scenario for 2060 suggests that future agricultural land abandonment and recultivation may largely be determined by climate and terrain conditions and, to a lesser extent, by proximity to economic centers. Our study underscores the value of synergetic use of satellite data and land-use modeling to provide the input for land planning, and to anticipate the potential effects of changing environmental and policy conditions.

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California

The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m).

Time series harmonic regression analysis reveals seasonal vegetation productivity trends in semi-arid savannas

Vegetation cover in savannas is characterized by high spatial heterogeneity driven by natural and anthropogenic drivers acting at multiple spatial and temporal scales. This research article identifies trends in vegetation cover and productivity in response to both land management and variable precipitation through a methodological approach that incorporates vegetation transect fieldwork with satellite imagery time series analysis. The phenological cycles of semi-arid savanna vegetation were analyzed over 27 years for both a protected area and a privately-owned property in order to capture multiple vegetation cover categories within the eastern Edwards Plateau ecoregion of central Texas. Line-intercept vegetation transects were established across these two sites in 2015, with ground cover and structural vegetation measurements collected along each transect. These measurements were classified based upon distinct vegetation cover categories: woodland, intermediate, open, and open disturbed. Time series analysis of vegetation productivity utilized 111 multi-season Landsat TM5 and Landsat 8 images that captured spring, fall, and winter seasonalities across the 27-year time period spanning from 1988 to 2015, with the Soil-Adjusted Vegetation Index (SAVI) calculated as an indicator of vegetative productivity. Harmonic regression was applied to this time series in order to determine the relationship between time of year with respect to growing season and productivity level across vegetation cover types. Fixed mean harmonic amplitudes for calendar year productivity were calculated across the entire time period as well as across three natural breaks-based sub-divisions of the 27-year period. Mean amplitudes per transect were then plotted against field-truthed tree-grass ratios in order to determine the relationship between tree and grass cover and mean harmonic SAVI amplitude. Harmonic regression identified inter- and intra-annual seasonal variability across cover types, with woodland sites exhibiting much lower variability in SAVI levels than open sites. These results provide insight into the phenological response of vegetation cover types typical of semi-arid savannas to spatially and temporally variable precipitation levels and land management schemes, allowing for better informed management and monitoring strategies for these environments.

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30 m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p < 0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies.

Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China

Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and Spartina using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km2, which was mostly converted from mudflats and Spartina. Environmental threats including climate change and sea-level rise, aquaculture development and Spartina invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and Spartina control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests.

Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making.

Canadian Prairie Rangeland and Seeded Forage Classification Using Multiseason Landsat 8 and Summer RADARSAT-2

Rangeland and seeded forage in Canada’s Prairie provinces represent productive landscapes that provide multiple ecosystem services. Past efforts to map these resources at regional scales have not achieved consistently high accuracies as they are spatially variable in both ecology and management. In particular, Agriculture and Agri-food Canada needs to distinguish these land use classes from each other and from cropland in its annual national agricultural land cover inventory. Given the potential to distinguish these classes based on seasonal phenological differences, this study used multi-season Landsat 8 top-of-atmosphere reflectance data and derived vegetation and phenological indices, as well as mid-summer RADARSAT-2 data in random forest classification of two ecoregions in Alberta and Manitoba. Classification accuracy was compared for single and multi-date Landsat 8 variables, the vegetation index and phenological variable groups, RADARSAT-2 VV and VH backscatter intensity, and combined datasets. Variable importance analysis showed that spring Landsat 8 reflectance generally contributed most to class discrimination, but accuracy improved with the addition of Landsat 8 data from the other seasons. Vegetation indices and phenological variables produced similar accuracies and were deemed to not warrant the additional processing effort to derive them. RADARSAT-2 VH backscatter was the most important variable for the Manitoba study area, which is wetter with more vegetation structure variability than the Alberta study area. Backscatter intensity significantly increased overall accuracy when it was combined with one or two-season Landsat 8 data. The best overall accuracy was achieved using the three seasons of Landsat 8 and mid-summer RADARSAT-2 data, but it was not significantly better than that for two season Landsat 8 + RADARSAT-2. The methods presented in this paper provide a process for accurate and efficient classification of seeded forage, rangeland and cropland that can be applied over large areas in operational agricultural land cover inventory.

Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua

Shade-grown coffee (shade coffee) is an important component of the forested tropics, and is essential to the conservation of forest-dependent biodiversity. Despite its importance, shade coffee is challenging to map using remotely sensed data given its spectral similarity to forested land. This paper addresses this challenge in three districts of northern Nicaragua, here leveraging cloud-based computing techniques within Google Earth Engine (GEE) to integrate multi-seasonal Landsat 8 satellite imagery (30 m), and physiographic variables (temperature, topography, and precipitation). Applying a random forest machine learning algorithm using reference data from two field surveys produced a 90.5% accuracy across ten classes of land cover, with an 82.1% and 80.0% user’s and producer’s accuracy respectively for shade-grown coffee. Comparing classification accuracies obtained from five datasets exploring different combinations of non-seasonal and seasonal spectral data as well as physiographic data also revealed a trend of increasing accuracy when seasonal data were included in the model and a significant improvement (7.8–20.1%) when topographical data were integrated with spectral data. These results are significant in piloting an open-access and user-friendly approach to mapping heterogeneous shade coffee landscapes with high overall accuracy, even in locations with persistent cloud cover.

Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine

Fanjinshan National Nature Reserve (FNNR) is a biodiversity hotspot in China that is part of a larger, multi-use landscape where farming, grazing, tourism, and other human activities occur. The steep terrain and persistent cloud cover pose challenges to robust vegetation and land use mapping. Our objective is to develop satellite image classification techniques that can reliably map forest cover and land use while minimizing the cloud and terrain issues, and provide the basis for long-term monitoring. Multi-seasonal Landsat image composites and elevation ancillary layers effectively minimize the persistent cloud cover and terrain issues. Spectral vegetation index (SVI) products and shade/illumination normalization approaches yield significantly higher mapping accuracies, compared to non-normalized spectral bands. Advanced machine learning image classification routines are implemented through the cloud-based Google Earth Engine platform. Optimal classifier parameters (e.g., number of trees and number of features for random forest classifiers) were achieved by using tuning techniques. Accuracy assessment results indicate consistent and effective overall classification (i.e., above 70% mapping accuracies) can be achieved using multi-temporal SVI composites with simple illumination normalization and elevation ancillary data, despite the fact limited training and reference data are available. This efficient and open-access image analysis workflow provides a reliable methodology to remotely monitor forest cover and land use in FNNR and other mountainous forested, cloud prevalent areas.

Long term monitoring and assessment of desertification processes using medium & high resolution satellite data

Desertification is one of the major environmental problems, affecting the living conditions of millions of people all across the world. Inventory and monitoring of the desertification status are the primary requirement in preparation of mitigation plans to combat desertification. The objective of the present paper is, therefore, to prepare spatial inventory of desertification status that provides information on the spatial extent of the area under desertification, its type and severity. Multi-season Landsat data of 30 m spatial resolution, pertaining to the years 1991, 2000 and 2016, have been analysed to prepare desertification status map (DSM) at 1: 25,000 scale for Jaitran Taluka, Pali district, Rajasthan state in western India. Monitoring and assessment of desertification has been carried out for a period of 25 years. Area under desertification in the Jaitran taluka has increased from 13080.22 ha to 15376.37 ha during the past 25 years. Preparation of desertification combating plans, which can be implemented in the field, requires DSM at cadastral scale. Therefore, DSM were also prepared on 1: 10,000 scale using very high spatial resolution data of Resourecesat-2 LISS-IV. Spatial information on land use, desertification status (extent, type and severity) extracted from the above DSM and the information on topography and morphology, inferred from Cartosat-1 images (2.5-m spatial resolution, panchromatic stereo) were used to suggest local specific actions for combating desertification.

Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach

Woody cover encroachment/expansion/conversion is a complex phenomenon that has environmental and economic impacts around the world. This research demonstrates the development of highly accurate models for estimating percent woody cover using high spatial resolution image data in combination with multi-seasonal Landsat reflectance products. We use a classification and regression tree (CART) approach to classify woody cover using fine resolution multispectral National Agricultural Imaging Program (NAIP) data. A continuous classification and regression tree (Cubist) ingests the aggregated woody cover classification along with the seasonal Landsat data to create a continuous woody cover model. We applied the models, derived by Cubist, across several Landsat scenes to estimate the percentage of woody plant cover, within each Landsat pixel, over a larger regional extent. We measured an average absolute error of 12.1 percent and a correlation coefficient of 0.78 for the models performed. The method of modelling percent woody cover established in this manuscript outperforms currently available woody cover estimates including Landsat Vegetation Continuous Fields (VCF), on average by 26 percent, and Web-Enabled Landsat Data (WELD) products, on average by 16 percent, for the region of interest. Current woody cover products are also limited to certain years and not available pre-2000. This manuscript describes a novel Cubist-based technique to model woody cover for any area of the world, as long as fine (~1–2 m) spatial resolution and Landsat data are available.

Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data

A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry forests, yet remotely sensed mapping studies of fire-impacted tropical dry forests lag behind fire research of other forest types. Methods used to map burns in temperature forests may not perform as well in tropical dry forests where it can be difficult to distinguish between multiple-age burn scars and between bare soil and burns. In this study, the extent of forest lost to stand-replacing fire in Kirindy Mitea National Park was quantified using both spectral and textural information derived from multi-date satellite imagery. The total area of the burn was 18,034 ha. It is estimated that 6% (4761 ha) of the Park’s primary tropical dry forest burned over the period 23 June to 27 September. Half of the forest burned (2333 ha) was lost in the large conflagration adjacent to the Research Station. The best model for burn scar mapping in this highly-seasonal tropical forest and pastoral landscape included the differenced Normalized Burn Ratio (dNBR) and both uni- and multi-temporal measures of greenness. Lessons for burn mapping of tropical dry forest are much the same as for tropical dry forest mapping—highly seasonal vegetation combined with a mixture of background spectral information make for a complicated analysis and may require multi-temporal imagery and site specific techniques.

Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl

Studies have shown that spatial information on abandoned land could play an important role in urban land management, as land abandonment was proven capable of revealing future trajectories of change. However, mapping land abandonment with traditional methods (e.g., field work, digitization of aerial images) can be time consuming, expensive, and require considerable man-power. In this context, Landsat imagery proves to be a reliable source of data. To our knowledge, the potential of Landsat imagery for mapping abandoned land has not been tested in heterogeneous and highly fragmented urban settings. The aim of our paper is to propose a resource-efficient (i.e., in terms of time and manpower) method for the assessment of land abandonment in areas affected by urban sprawl, by using seasonal time series of Landsat data. Bucharest, Romania was chosen as case study area. Landsat scenes from the year 2013 are grouped based on vegetation phenology into four PGSs (periods of the growing season). NDVI values corresponding to land abandonment are analyzed with Classification and Regression Trees. A total of 23 models—representing combinations of PGSs—are tested in order to determine what period of the vegetation growing season fits best for mapping abandoned land, and for the purpose of deriving such a map for Bucharest. Finally, results are validated against independent data and a resource estimation for the entire mapping process is performed. Results show that abandoned land can be mapped with Landsat imagery with accuracies above 80%. Higher accuracies are obtained when scenes encompassing the beginning of the vegetation growing season are included in the models. We also observed that accuracy tends to decrease from models which include PGSs representing the beginning of the vegetation growing season towards those representing the end. Estimation showed that mapping land abandonment with Landsat data could reduce time and workforce resources by almost half compared with aerial imagery and field work. As our method is rapid, easy to implement, and based on freely available data, it can be used by local authorities that cannot allocate significant resources for land change monitoring. Furthermore, the approach could provide objective information to municipalities where official statistics on land abandonment are unreliable or of low quality.

Updating the Grassland Vegetation Inventory Using Change Vector Analysis and Functionally-Based Vegetation Indices

The Grassland Vegetation Inventory (GVI), which represents a comprehensive biophysical, anthropogenic, and land-use inventory of grasslands in Alberta, is widely used as a baseline for grassland conditions. An up-to-date GVI is essential for understanding grassland changes and for planning management or conservation actions on grasslands. In this study, a hybrid change detection method is proposed that incorporates change vector analysis and a set of vegetation indices (VIs) measuring different vegetation attributes for mapping the conversion of native grassland to cultivated agriculture, and ultimately to update the GVI based on multiseasonal and multiyear Landsat images. Vegetation indices that contribute significantly to differentiation between existing native grassland and land recently converted from native grassland to cultivated cropland were identified by using stepwise regression analyses and were used as inputs for mapping the conversion between 2006 and 2011 or 2015. The results showed that land conversion can be detected using a single image acquired during the growing season, but that the accuracy of identification is affected by the date of image collection and the nature of the VIs used. The greatest accuracy in detecting land conversion between 2006 and 2011 was achieved using the difference in VI between years (dVI) for the Shortwave Infrared Reflectance 3/2 Ratio (SWIR32) and the Enhanced Vegetation Difference Index (EVI) derived from July imagery (accuracy = 95.2 %; Kappa = 0.86). The same combination of SWIR32 and EVI was also effective, although with lower accuracy (accuracy = 86.0 %; Kappa = 0.64) when tested on a larger geographical area and for detecting land use change between 2006 and 2015. The method proposed here could be applied to detect the land cover conversion in other grassland regions, although the optimal VIs and image acquisition date may need to be modified depending on the type of land use activities implemented in each region.

Woodland expansion in South African grassy biomes based on satellite observations (1990-2013): general patterns and potential drivers.

Increases in woody plant cover in savanna grassland environments have been reported on globally for over 50years and are generally perceived as a threat to rangeland productivity and biodiversity. Despite this, few attempts have been made to estimate the extent of woodland increase at a national scale, principally due to technical constraints such as availability of appropriate remote sensing products. In this study, we aimed to measure the extent to which woodlands have replaced grasslands in South Africa's grassy biomes. We use multiseason Landsat data in conjunction with satellite L-band radar backscatter data to estimate the extent of woodlands and grasslands in 1990 and 2013. The method employed allows for a unique, nationwide measurement of transitions between grassland and woodland classes in recent decades. We estimate that during the 23-year study period, woodlands have replaced grasslands over ~57000km2 and conversely that grasslands have replaced woodlands over ~30000km2 , a net increase in the extent of woodland of ~27000km2 and an annual increase of 0.22%. The changes varied markedly across the country; areas receiving over 500mm mean annual precipitation showed higher rates of woodland expansion than regions receiving <500mm (0.31% yr-1 and 0.11% yr-1 , respectively). Protected areas with elephants showed clear loss of woodlands (-0.43% yr-1 ), while commercial rangelands and traditional rangelands showed increases in woodland extent (>0.19% yr-1 ). The woodland change map presented here provides a unique opportunity to test the numerous models of woody plant encroachment at a national/regional scale.