Many countries have adopted large-scale tree-planting programs as a climate mitigation strategy and to support local livelihoods. We evaluate a series of large-scale tree planting 23 programs using data collected from historical Landsat imagery in the state of Himachal Pradesh 24 in Northern India. Using this panel dataset, we use an event study design to estimate the 25 socioeconomic and biophysical impacts over decades of these programs. We find that tree plantings have not, on average, increased the proportion of forest canopy cover, and have modestly shifted forest composition away from the broadleaf varieties valued by local people.Further cross-sectional analysis, from a household livelihood survey, shows that tree planting supports little direct use by local people. We conclude that decades of expensive tree planting programs in this region have not proved effective. This result shows that large-scale tree planting may sometimes fail to achieve its climate mitigation and livelihood goals.3 MainMany countries have begun adopting large-scale tree-planting programs based on the potential of forests to absorb carbon and support local livelihoods 1-3 . As of 2015, the extent of 35 global tree cover from planted forests is estimated at 280 million hectares, and 12 million 36 hectares lie within India 4 . Despite the broad appeal of planting trees, some researchers and practitioners have raised concerns about potential negative impacts of large-scale tree-planting programs on vulnerable people and diverse ecosystems [5][6][7] . Restoration ecologists have cautioned 39 that tree planting should not be equated with forest restoration, but instead countries should 40 consider diverse restoration strategies in diverse ecosystems 7 . However, forest restoration commitments made under international agreements like the Bonn Challenge and UNFCCC Paris Accords demand nationally-coordinated efforts to achieve ambitious restoration targets at immense scale 8 . As a result, much of the current nationally-pledged restoration area is set aside 44 for large-scale tree planting 2,9 . For example, the Indian National Determined Contributions 45 (NDC) from the Paris Accords commits "To create an additional carbon sink of 2.5 to 3 billion 46 tonnes of CO2 equivalent through additional forest and tree cover by 2030" 10 . Understanding the 47

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Classification is a common objective when analyzing hyperspectral images, where each pixel is assigned to a predefined label. Deep learning-based algorithms have been introduced in the remote-sensing community successfully in the past decade and have achieved significant performance improvements compared with conventional models. However, research on the extraction of sequential features utilizing a single image, instead of multi-temporal images still needs to be further investigated. In this paper, a novel strategy for constructing sequential features from a single image in long short-term memory (LSTM) is proposed. Two pixel-wise-based similarity measurements, including pixel-matching (PM) and block-matching (BM), are employed for the selection of sequence candidates from the whole image. Then, the sequential structure of a given pixel can be constructed as the input of LSTM by utilizing the first several matching pixels with high similarities. The resulting PM-based LSTM and BM-based LSTM are appealing, as all pixels in the whole image are taken into consideration when calculating the similarity. In addition, BM-based LSTM also utilizes local spectral-spatial information that has already shown its effectiveness in hyperspectral image classification. Two common distance measures, Euclidean distance and spectral angle mapping, are also investigated in this paper. Experiments with two benchmark hyperspectral images demonstrate that the proposed methods achieve marked improvements in classification performance relative to the other state-of-the-art methods considered. For instance, the highest overall accuracy achieved on the Pavia University image is 96.20% (using both BM-based LSTM and spectral angle mapping), which is an improvement compared with 84.45% overall accuracy generated by 1D convolutional neural networks.

Surface processes on debris-covered glaciers are governed by a variety of controlling factors including climate, debris load, water bodies, and topography. Currently, we have not achieved a general consensus on the role of supraglacial processes in regulating climate–glacier sensitivity in High Mountain Asia, which is mainly due to a lack of an integrated understanding of glacier surface dynamics as a function of debris properties, mass movement, and ponding. Therefore, further investigations on supraglacial processes is needed in order to provide more accurate assessments of the hydrological cycle, water resources, and natural hazards in the region. Given the scarcity of long-term in situ data and the difficulty of conducting fieldwork on these glaciers, many numerical models have been developed by recent studies. This review summarizes our current knowledge of surface processes on debris-covered glaciers with an emphasis on the related modeling efforts. We present an integrated view on how numerical modeling provide insights into glacier surface ablation, supraglacial debris transport, morphological variation, pond dynamics, and ice-cliff evolution. We also highlight the remote sensing approaches that facilitate modeling, and discuss the limitations of existing models regarding their capabilities to address coupled processes on debris-covered glaciers and suggest research directions.

Myriad scholars, policymakers, and practitioners advocate tree planting as a climate mitigation strategy and to support local livelihoods. But, is the broad appeal of tree planting supported by evidence? We report estimated impacts from decades of tree planting in Northern India. We find that tree plantings have not, on average, increased the proportion of dense forest cover, and have modestly shifted species composition away from the broadleaf varieties valued by local people. Supplementary analysis from household livelihood surveys show that, in contrast to narratives of forest dependent people being supported by tree planting, there are few direct users of these plantations and their dependence is low. We conclude that decades of expensive tree planting programs have not proved effective.