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The Normalized Difference Vegetation Index (NDVI), one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery, is now the most popular index used for vegetation assessment. This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band. Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite, aerial, and increasingly—Unmanned Aerial Systems (UAS). While studies have found that the NDVI is effective for expressing vegetation status and quantified vegetation attributes, its widespread use and popularity, especially in UAS applications, carry inherent risks of misuse with end users who received little to no remote sensing education. This article summarizes the progress of NDVI acquisition, highlights the areas of NDVI application, and addresses the critical problems and considerations in using NDVI. Detailed discussion mainly covers three aspects: atmospheric effect, saturation phenomenon, and sensor factors. The use of NDVI can be highly effective as long as its limitations and capabilities are understood. This consideration is particularly important to the UAS user community.

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing initiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, measuring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it is important to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

ContextImage classification is routine in a variety of disciplines, and analysts rely on accuracy metrics to evaluate the resulting maps. The most frequently used accuracy metric in Earth resource remote sensing is overall accuracy. However, the inherent properties of this accuracy metric make it inappropriate as the single metric for map assessment, particularly when a map contains imbalanced categories.ObjectivesWe discuss four noteworthy problems with overall accuracy. Under circumstances frequently encountered, overall accuracy is misleading or misinterpreted.MethodsLiterature review, hypothetical examples, and mathematic equations are used to prove overall accuracy is a poor general indicator of map quality.ConclusionsAny research that involves classification techniques or a map product that is evaluated only with overall accuracy may be unreliable. It is necessary for map providers to publish the error matrix and its development procedure so that map users can computer whatever metrics as they wish.

Forest wildfires pose significant and growing threats to human safety, wildlife habitat, regional economies and global climate change. It is crucial that forest fires be subject to timely and accurate monitoring by forest fire managers and other stake-holders. Measurement by spaceborne equipment has become a practical and appealing method to monitor the occurrence and development of forest wildfires. Here we present an overview of the principles and case studies of forest fire monitoring（FFM） with satelliteand drone-mounted infrared remote sensing（IRRS）. This review includes four types of FFM-relevant IRRS algorithms： bi-spectral methods, fixed threshold methods, spatial contextual methods, and multi-temporal methods. The spatial contextual methods are presented in detail since they can be applied easily with commonly available satellite IRRS data, including MODIS, VIIRS, and Landsat 8 OLI. This review also evaluates typical cases of FFM using NOAAAVHRR, EOS-MODIS, S-NPP VIIRS, Landsat 8 OLI,MSG-SEVIRI, and drone infrared data. To better implement IRRS applications in FFM, it is important to develop accurate forest masks, carry out systematic comparative studies of various forest fire detection systems（known as forest fire products）, and improve methods for assessing the accuracy of forest fire detection. Medium-resolution IRRS data are effective for landscape-scale FFM, and the VIIRS 375 m contextual algorithm and RST-FIRES algorithm are helpful for closely tracking forest fires（including small and shortlived fires） and forest-fire early warning.

There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.

Advances in remote sensing technologies have provided practical means for land use and land cover mapping which is critically important for landscape ecological studies. However, all classifications of remote sensing data are subject to different kinds of errors, and these errors can be carried over or propagated in subsequent landscape pattern analysis. When these uncertainties go unreported, as they do commonly in the literature, they become hidden errors. While this is apparently an important issue in the study of landscapes from either a biophysical or socioeconomic perspective, limited progress has been made in resolving this problem. Here we discuss how errors of mapped data can affect landscape metrics and possible strategies which can help improve the reliability of landscape pattern analysis.

In recent years, with the development of society, the awareness of environmental protection for people has been increasing. While ports promote the economic development and employment levels of port cities, they also have a negative impact on the environment of port cities. The sustainability of port cities is increasingly valued. Port cities face huge challenges, and their sustainability needs to be better understood. The purpose of this article is to review research on the sustainability of port cities. We used content analysis to classify and analyze the existing relevant literature, to learn about the hotspots and deficiencies of past research, and to propose future research directions. We found that port sustainability has become an increasingly important research topic during the past ten years. From the perspective of geographic research areas, European port cities are the hot spots for sustainability research. Regarding research fields, technologies, methods and measures to promote the sustainability of port cities are popular research topics. In terms of research methods, qualitative research plays an important role in the study of port city sustainability. Finally, guidance for future research on port city sustainability is proposed according to the review results.

Wildfire regimes are closely linked to changes in landscape structure, yet the influence of accelerated land use transitions on fire activity remains poorly understood, particularly in rapidly transforming regions like central Chile. Although land use change has been extensively documented in the country, the specific role of the speed, extent, and spatial configuration of these transitions in shaping fire dynamics requires further investigation. To address this gap, we examined how landscape transitions influence fire frequency in central Chile, a region experiencing rapid land use change and heightened fire activity. Using multi-temporal remote sensing data, we quantified land use transitions, calculated landscape metrics to describe their spatial characteristics, and applied intensity analysis to assess their relationship with fire frequency changes. Our results show that accelerated landscape transitions significantly increased fire frequency, particularly in areas affected by forest plantation rotations, new forest establishment, and urban expansion, with changes exceeding uniform intensity expectations. Regional variations were evident: In the more densely populated northern areas, increased fire frequency was primarily linked to urban development and deforestation, while in the more rural southern regions, forest plantation cycles played a dominant role. Areas with a high number of large forest patches were especially prone to fire frequency increases. These findings demonstrate that both the speed and spatial configuration of landscape transitions are critical drivers of wildfire activity. By identifying the specific land use changes and landscape characteristics that amplify fire risks, this study provides valuable knowledge to inform fire risk reduction, landscape management, and urban planning in Chile and other fire-prone regions undergoing rapid transformation.

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

The land use and land cover changes in rapidly urbanized regions is one of the main causes of water quality deterioration. However, due to the heterogeneity of urban land use patterns and spatial scale effects, a clear understanding of the relationships between land use and water quality remains elusive. The primary purpose of this study is to investigate the effects of land use on water quality across multi scales in a rapidly urbanized region in Hangzhou City, China. The results showed that the response characteristics of stream water quality to land use were spatial scale-dependent. The total nitrogen (TN) was more closely related with land use at the circular buffer scale, whilst stronger correlations could be found between land use and algae biomass at the riparian buffer scales. Under the circular buffer scale, the forest and urban greenspace were more influential to the TN at small buffer scales, whilst significant positive or negative correlations could be found between the TN and the areas of industrial land or the wetland and river as the buffer scales increased. The redundancy analysis (RDA) showed that more than 40% variations in water quality could be explained by the landscape metrics at all circular and riparian buffer scales, and this suggests that land use pattern was an important factor influencing water quality. The variation in water quality explained by landscape metrics increased with the increase of buffer size, and this implies that land use pattern could have a closer correlation with water quality at larger spatial scales.