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Information on a crop's three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a vital parameter for quantifying the material and energy exchange between terrestrial ecosystems and the atmosphere. The Global Ecosystem Dynamics Investigation (GEDI), with its mission to produce a near-global map of forest structure, provides a product of the effective leaf area index (referred to as GEDI LAIe). However, it is unclear about the performance of GEDI LAIe across different temperate forest types and the degree of factors influencing GEDI LAIe performance. This study assessed the accuracy of GEDI LAIe in temperate forests and quantifies the effects of various factors, such as the difference of gap fraction (DGF) between GEDI and discrete point cloud Lidar of the National Ecological Observatory Network (NEON), sensor system parameters, and characteristics of the canopy, topography, and soil. The reference data for the LAIe assessment were derived from the NEON discrete point cloud Lidar, referred to as NEON Lidar LAIe, covering 12 forest types across 22 sites in the Continental United States (the CONUS). Results showed that GEDI underestimated LAIe (Bias: −0.56 m2/m2), with values of the mean absolute error (MAE), root mean square error (RMSE), percent bias (%Bias), and percent RMSE (%RMSE) of 0.70 m2/m2, 0.89 m2/m2, −0.20, and 0.31, respectively. Among forest types, the underestimation of GEDI LAIe in broadleaf forests and mixed forests was generally greater than that in coniferous forests, which showed a moderate error (%RMSE: 0.33~0.52). Factor analysis indicated that multiple factors explained 52% variance of the GEDI LAIe error, among which the DGF contributed the most with a relative importance of 49.82%, followed by characteristics of canopy and soil with a relative importance of 23.20% and 16.18%, respectively. The DGF was a key pivot for GEDI LAIe error; that is, other factors indirectly influence the GEDI LAIe error by affecting the DGF first. Our findings demonstrated that the GEDI LAIe product has good performance, and the factor analysis is expected to shed some light on further improvements in GEDI LAIe estimation. [ABSTRACT FROM AUTHOR]

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth's surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks. [ABSTRACT FROM AUTHOR]

The Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r2 of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15–60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Niño drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience. [ABSTRACT FROM AUTHOR]

In Brazil’s state of Acre, in southwestern Amazonia, wildfires mediated by extreme droughts in 2005 and 2010 affected more than 500,000 ha of forest, causing changes in their structure, species diversity, and aboveground biomass (AGB), and the expansion of bamboo. Our objective was to analyze these changes in an open bamboo forest in Acre after forest fires occurred either in one of the extreme drought years (2005 or 2010) or in both years (2005 + 2010). We sampled 9.75 ha (in 2016 and 2017), distributed in 18 0.5 ha (100 m × 50 m) plots and three 0.25-ha (50 m × 50 m) plots. We identified a strong fire effect on the number of tree individuals per hectare, which declined by 50% if the forest was burned in only one year (2005 or 2010) and by 74% if burned in both years. This was inversely related to the expansion of bamboo stems, which increased in number by 7 to 9 times. Changes in forest structure and species composition after the fire were characterized by a high importance value for pioneer tree species; reductions in the number of trees with logging potential, in the basal area of trees, and in the number of lianas; and an increase in the density of bamboo stems. AGB in the burned forests was 51–73% that of the unburned forest. With the expansion of bamboo, its contribution to AGB increased from 1% in the unburned forest to 27% in the twice-burned forest. These forms of degradation represent serious threats to Amazon forests. [ABSTRACT FROM AUTHOR]

Amazonian tropical rainforest is being converted to other land cover types including crops and pasture. In deforested areas, secondary forest grows after pastures are abandoned, and 'dirty pasture' that has trees and shrubs but is actively used for grazing are also regionally important land cover types following forest conversion. This study describes a multistage process land cover classification method to map primary forest, secondary forest, pasture, pasture with trees, built and water in the Brazilian state of Rondônia. A recently developed Spectral Variability Vegetation Index (SVVI) is tested to discriminate land cover types with differing tree cover amounts. Random Forest classifier (RF) is applied to inputs from a) spectral mixture analysis (SMA), and b) tasselled cap (TC) transformation, both with and without SSVI as an additional input feature. SVVI improved the classification accuracy from 73% (TC) to 85% (TC-SVVI), and TC-SVVI yielded a land cover map with higher accuracy than that from SMA-SVVI (82%). Pasture-with-trees, secondary forest and primary forest were all distinguishable with the SVVI. Pasture-with-trees accounted for 67% of all pastures, demonstrating its importance for regional land cover. This land cover classification workflow with the SVVI index improves the accuracy of mapping heterogeneous tropical land cover types. [ABSTRACT FROM AUTHOR]

Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land cover types over highly fragmented landscapes in the southern Amazon and characterize the ET dynamics during the dry season using the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC, a Landsat-based ET model, that generates spatially continuous ET estimates at a 30 m spatial resolution widely used for agricultural applications, was adapted to the southern Amazon by using the NDVI indexed reference ET fraction (ETrF) approach. Compared to flux tower-based ET rates, this approach showed an improved performance on the forest ET estimation over the standard METRIC approach, with R2 = 0.73 from R2 = 0.70 and RMSE reduced from 0.77 mm/day to 0.35 mm/day. We used this approach integrated into the METRIC procedure to estimate ET rates from primary, regenerated, and degraded forests and pasture in Acre, Rondônia, and Mato Grosso, all located in the southern Amazon, during the dry season in 2009. The lowest ET rates occurred in Mato Grosso, the driest region. Acre and Rondônia, both located in the southwestern Amazon, had similar ET rates for all land cover types. Dry season ET rates between primary forest and regenerated forest were similar (p > 0.05) in all sites, ranging between 2.5 and 3.4 mm/day for both forest cover types in the three sites. ET rates from degraded forest in Mato Grosso were significantly lower (p < 0.05) compared to the other forest cover types, with a value of 2.03 mm/day on average. Pasture showed the lowest ET rates during the dry season at all study sites, with the dry season average ET varying from 1.7 mm/day in Mato Grosso to 2.8 mm/day in Acre. [ABSTRACT FROM AUTHOR]

While forest evapotranspiration (ET) dynamics in the Amazon have been studied both as point estimates using flux towers, as well as spatially coarse surfaces using satellite data, higher resolution (e.g., 30 m resolution) ET estimates are necessary to address finer spatial variability associated with forest biophysical characteristics and their changes by natural and human impacts. The objective of this study is to evaluate the potential of the Landsat-based METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model to estimate high-resolution (30 m) forest ET by comparing to flux tower ET (FT ET) data collected over seasonally dry tropical forests in Rondônia, the southwestern region of the Amazon. Analyses were conducted at daily, monthly and seasonal scales for the dry seasons (June-September for Rondônia) of 2000-2002. Overall daily ET comparison between FT ET and METRIC ET across the study site showed r² = 0.67 with RMSE = 0.81 mm. For seasonal ET comparison, METRIC-derived ET estimates showed an agreement with FT ET measurements during the dry season of r² >0.70 and %MAE <15%. We also discuss some challenges and potential applications of METRIC for Amazonian forests. [ABSTRACT FROM AUTHOR]

Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000-2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46% between the first and second halves of the study period, annual forest degradation increased by 20%. Existing operational monitoring systems (PRODES: Monitoramento da Florestal Amazônica Brasileira por Satélite) report deforestation area to within 2% of our results, but do not account for the extensive forest degradation occurring throughout the region due to selective logging and forest fire. Annual monitoring of forest degradation across tropical forests is critical for developing land management policies as well as the monitoring of carbon stocks/emissions and protected areas. [ABSTRACT FROM AUTHOR]

Fire is frequently used as tool for land management in the Amazon, but often escapes into surrounding forests, with potentially severe impacts for forest biodiversity. We investigated the effects of single wildfires on ant communities in four geographically distinct regions of the Brazilian Amazon (Roraima, Pará, Acre and Mato Grosso) where forests had burned between 8 months and 10 years before our sampling. We established 7-12 transects, 500 m each, in burned and unburned forests in each region to investigate the effects of fire on forest structure and leaf litter ant communities, which were sampled using Winkler sacks. Fire effects on forest structure were more drastic in the most recently burned forests in Acre and Mato Grosso, while the impacts of older burns in Roraima and Pará were more subtle. Ant species richness was not different between burnt and unburned areas, but community composition differed between burned and control forests in all regions except Mato Grosso. At the species level, indicator species analysis showed that a limited number of species were significant indicators of unburned control forests in all regions, except Acre. Forests structure variables and leaf litter volume were all important in shaping ant communities, but their relative importance varied between regions. Our results indicate that burned forest have different ant species communities from unburned forests, and those differences are still apparent 10 years after the disturbance, highlighting the importance of effective policies for fire management in Amazon. [ABSTRACT FROM AUTHOR]

Fire has become an increasingly important disturbance event in south-western Amazonia. We conducted the first assessment of the ecological impacts of these wildfires in 2008, sampling forest structure and biodiversity along twelve 500 m transects in the Chico Mendes Extractive Reserve, Acre, Brazil. Six transects were placed in unburned forests and six were in forests that burned during a series of forest fires that occurred from August to October 2005. Normalized Burn Ratio (NBR) calculations, based on Landsat reflectance data, indicate that all transects were similar prior to the fires. We sampled understorey and canopy vegetation, birds using both mist nets and point counts, coprophagous dung beetles and the leaflitter ant fauna. Fire had limited influence upon either faunal or floral species richness or community structure responses, and stems <10 cm DBH were the only group to show highly significant (p = 0.001) community turnover in burned forests. Mean aboveground live biomass was statistically indistinguishable in the unburned and burned plots, although there was a significant increase in the total abundance of dead stems in burned plots. Comparisons with previous studies suggest that wildfires had much less effect upon forest structure and biodiversity in these south-western Amazonian forests than in central and eastern Amazonia, where most fire research has been undertaken to date. We discuss potential reasons for the apparent greater resilience of our study plots to wildfire, examining the role of fire intensity, bamboo dominance, background rates of disturbance, landscape and soil conditions. [ABSTRACT FROM AUTHOR]

Estimation of fire impacts and forest recovery using remote sensing is difficult because of the heterogeneity of fire history (frequency, severity, and time since last fire) across burned forest landscapes. The authors analyzed impacts of fire frequency and severity within recovering forests in the Amazon region using remote sensing. A multispectral Landsat time series dataset was used to reconstruct the fire history from 1990 to 2002 in a portion of Mato Grosso, Brazil. Five narrowband vegetation indices were then calculated from a hyperspectral Earth Observing One (EO-1) Hyperion image for spectral analysis of physiological characteristics of fire-disturbed forests and their recovery. A total of 30%% of the forests burned during the study period, with 72%% burned once, 24%% burned twice, and less than 4%% burned three times. In terms of severity, 70%% of burned forest was lightly burned, 21.1%% was moderately burned, and 9.1%% was severely burned. Analyses of spectral indices [normalized difference vegetation index (NDVI), carotenoid reflectance index (CRI), and photochemical reflectance index (PRI)] showed that those related to canopy greenness and pigment contents can discriminate between burned forests and undisturbed forest for the first 3 years after forest fire, whereas the effectiveness of canopy water content indices [normalized difference water index (NDWI) and normalized difference infrared index (NDII)] varied from 1 to 3 years, depending on the fire severity. Despite the relatively low signal-to-noise ratios of Hyperion imagery, we show that narrowband-derived indices provide useful information for monitoring degraded forests beyond what is currently possible with Landsat. This illustrates the great potential for environmental monitoring using satellite-borne hyperspectral sensors, such as the Hyperspectral Infrared Imager (HyspIRI), which have better signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. Regional analysis of pasture dynamic patterns was conducted using high-frequency temporal satellite data and ancillary data to better understand pasture degradation under varied soil, environmental, and pasture management conditions in the state of Rondônia, Brazil. The 10-day normalized difference vegetation index (NDVI) composite derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution was used to characterize different grass phenological patterns for 32 counties in Rondônia between 2001 and 2003. Six pasture greenness classes showed that high greenness pasture classes dominated in young pastures, while low greenness pasture classes were least common. As pastures aged, the proportion of high greenness pasture classes decreased and the proportion of low greenness pastures increased, indicating a decrease in forage productivity over time in Rondônia. The magnitude of productivity decline depended on environmental constraints and land use systems. To refine this analysis, trajectories of pasture change were determined using spectral mixture analysis applied to Landsat time series data from 1988 to 2001 with the focus on two counties that show contrasting patterns of potential of grass production: Pimenteira, representing the “degraded” pasture category, and Governador Jorge Teixeira, as the “productive” pasture category. The results revealed a clear pasture degradation pattern in Pimenteira, related to low soil fertility and dry climate conditions, while Governador Jorge Teixeira, with better soil fertility and intermediate precipitation, did not show signs of pasture degradation through time. [ABSTRACT FROM AUTHOR]

Regional and global environmental modeling depend on soil data for input layers or parameterization. However, randomly located observations, such as provided by agricultural databases, are not always representative of trends identified in field studies conducted under carefully controlled conditions. Many researchers lament the paucity of soil profile data in Amazônia, and suggest that given more data, regional studies would more closely approximate field research results. We assess the ability of a well-populated regional database collected in the southwestern Brazilian Amazon to reproduce expected biogeochemical trends associated with forest clearing and pasture establishment, and explore the ramifications of relying on independently collected soil data for regional modeling. The Soteron database includes analyses of approximately 3000 soil cores collected for zoning purposes in the state of Rondônia. Pasture ages were determined from a time series of Landsat TM images classified using spectral mixture analysis. Although regional averages showed some of the temporal trends expected based on field study results (e.g. increase in pH following forest clearing), the trends were not statistically significant. Stratification by precipitation and other variables showed pasture age to be important but difficult to separate from other potential controls on soil conditions, mainly because of the reduced number of observations in each stratum. Using multiple regression, which permitted the inclusion of all potential explanatory factors and interactions, pasture age was shown to be a statistically significant predictor of soil conditions. However, the expected temporal sequence of changes documented by field chronosequence studies could not be reproduced. Properties dominated by large-scale environmental gradients – pH, sum of base cations, aluminum saturation, and exchangeable calcium – were moderately well modeled, while those more strongly linked to dynamic spatially heterogeneous processes such as biological cycling and land management, particularly organic carbon and nitrogen, could not be modeled. Management-induced soil changes occur at too fine a scale to be captured by most maps, and the relative changes are small compared with spatial heterogeneity caused by controls on soil development over large regions. Therefore, regardless of whether chronosequence-derived models of biogeochemical response to land-cover change are correct, the results of these models will not lead to spatially explicit maps that can be validated by regional reconnaissance, nor will they facilitate realistic predictions of the regional biogeochemical consequences of land-cover change. The change from local to regional scale entails a change in the relative importance of processes controlling soil property behavior. [ABSTRACT FROM AUTHOR]

The dynamic change and spatial–temporal distribution of vegetation coverage are of great significance for regional ecological evolution, especially in the subtropics and tropics. Identifying the heterogeneity in vegetation activities and its response to climate factors is crucial for projecting ecosystem dynamics. We used long-term (2001–2018) satellite-derived enhanced vegetation index (EVI) datasets and climatic factors to analyze the spatiotemporal patterns of vegetation activities in an experimental area in Guangdong Province (China), as well as their links to changes in temperature (TEM), relative humidity (HUM), precipitation (PRE), sunshine duration (SUN), and surface runoff. The pruned exact linear time change point detection method (PELT) and the disturbance lag model (DLM) were used to understand the detailed ecological coverage status and time lag relationships between the EVI and climatic factors. The results indicate the following. (1) At the whole regional scale, a significant overall upward trend in the EVI variation was observed in 2001–2018. More specifically, there were two distinct periods with different trends, which were split by a turning point in 2005. PRE was the main climate-related driver of the rising EVI pre-2005, and the increase in TEM was the main climate factor influencing the forest EVI variation post-2006. (2) A three-month time lag effect was observed in the EVI response to relative humidity. The same phenomenon was found in the sunshine duration factor. (3) The EVI of farmlands (one type of land use) exhibited the largest lags between relative humidity and the sunshine duration factor, followed by grasslands and forests. (4) The comprehensive index of surface runoff could explain the time lags of vegetation activities, and the surface runoff value showed an apparently negative relationship with the vegetation coverage change. [ABSTRACT FROM AUTHOR]

Land consumption is the increase in artificial land cover, which is a major issue for environmental sustainability. In Italy, the Italian Institute for Environmental Protection and Research (ISPRA) and National System for Environmental Protection (SNPA) have the institutional duty to monitor land consumption yearly, through the photointerpretation of high-resolution images. This study intends to develop a methodology in order to produce maps of land consumption, by the use of the semi-automatic classification of multitemporal images, to reduce the effort of photointerpretation in detecting real changes. The developed methodology uses vegetation indices calculated over time series of images and decision rules. Three variants of the methodology were applied to detect the changes that occurred in Italy between the years 2018 and 2019, and the results were validated using ISPRA official data. The results show that the produced maps include large commission errors, but thanks to the developed methodology, the area to be photointerpreted was reduced to 7300 km2 (2.4% of Italian surface). The third variant of the methodology provided the highest detection of changes: 70.4% of the changes larger than 100 m2 (the pixel size) and over 84.0% of changes above 500 m2. Omissions are mainly related to single pixel changes, while larger changes are detected by at least one pixel in most of the cases. In conclusion, the developed methodology can improve the detection of land consumption, focusing photointerpretation work over selected areas detected automatically. [ABSTRACT FROM AUTHOR]

Boreal forest areas in the Russian Far East contained very large intact forests. This particular area is considered one of the most productive and diverse forests in the boreal biome of the world, and it is also home to many endangered species. Zeya State Nature Reserve is located at the southern margin of the boreal forest area in the Russian Far East and has rich fauna and flora. However, the forest in the region faced large-scale forest fires and clearcutting for timber recently. The information of disturbances is rarely understood. This study aimed to explore the effects of disturbance and forest dynamics around the reserve. Our study used two-year overlaid Landsat images from Landsat 5 Thematic Mapper (TM) and 8 Operational Land Imager (OLI), to generate forest-cover-change maps of 1988–1999, 1999–2010, and 2010–2016. In this paper, we analyze the direction of forest successional stages, to demonstrate the effectiveness of this protected area in terms of preventing human-based deforestation on the vegetation indices. The vegetation indices included the normalized burn ratio (NBR), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). The study provided information on the pattern of forest-cover change and disturbance area around the reserve. The NDWI was used to differentiate between water and non-water areas. The mean values of NBR and NDVI were calculated and determine the forest successional stages between burn, vegetation recovery, grass, mixed forest, oak forest, and birch and larch forest. The accuracy was assessed by using field measurements, field photos, and high-resolution images as references. Overall, our classification results have high accuracy for all three periods. The most disturbed area occurred during 2010–2016. The reserve was highly protected, with no human-disturbance activity. However, large areas from fire disturbance were found (137 km2) during 1999–2010. The findings also show a large area of disturbance, mostly located outside of the reserve. Mixed disturbance increased to almost 50 km2 during 2010–2016, in the buffer zone and outside of the reserve. We recommend future works to apply our methods to other ecosystems, to compare the forest dynamics and disturbance inside and outside the protected area. [ABSTRACT FROM AUTHOR]

Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management. [ABSTRACT FROM AUTHOR]

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions. [ABSTRACT FROM AUTHOR]